amici_refs.bib

@article{BallnusHug2017,
	Author = {Ballnus, B. and Hug, S. and Hatz, K. and G{\"o}rlitz, L. and Hasenauer, J. and Theis, F. J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1186/s12918-017-0433-1},
	Journal = {{BMC} Syst. Biol.},
	Keywords = {parameter estimation, MCMC sampling, Bayesian parameter estimation, AMICI},
	Month = {June},
	Number = {63},
	Title = {Comprehensive benchmarking of {Markov} chain {Monte} {Carlo} methods for dynamical systems},
	Volume = {11},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1186/s12918-017-0433-1}}

@article{BallnusSch2018,
	Abstract = {Motivation: Mathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research.
Results: We suggest a region-based adaptive parallel tempering algorithm which adapts to the problem-specific posterior distributions, i.e. modes and valleys. The algorithm combines several established algorithms to overcome their individual shortcomings and to improve sampling efficiency. We assessed its properties for established benchmark problems and two ordinary differential equation models of biochemical reaction networks. The proposed algorithm outperformed state-of-the-art methods in terms of calculation efficiency and mixing. Since the algorithm does not rely on a specific problem structure, but adapts to the posterior distribution, it is suitable for a variety of model classes.
Availability and implementation: The code is available both as Supplementary Material and in a Git repository written in MATLAB.
Supplementary information: Supplementary data are available at Bioinformatics online.},
	Author = {Ballnus, Benjamin and Schaper, Steffen and Theis, Fabian J and Hasenauer, Jan},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1093/bioinformatics/bty229},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Month = {July},
	Number = {13},
	Pages = {i494--i501},
	Pmc = {PMC6022572},
	Pmid = {29949983},
	Pst = {ppublish},
	Title = {Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering},
	Volume = {34},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/bty229}}

@Article{BastCal2018,
  author        = {Bast, Lisa and Calzolari, Filippo and Strasser, Michael and Hasenauer, Jan and Theis, Fabian J. and Ninkovic, Jovica and Marr, Carsten},
  journal       = {Cell Reports},
  title         = {Subtle Changes in Clonal Dynamics Underlie the Age-Related Decline in Neurogenesis},
  year          = {2018},
  date-added    = {2019-03-19 23:04:09 +0100},
  date-modified = {2019-03-19 23:04:09 +0100},
  doi           = {10.1016/j.celrep.2018.11.088},
  keywords      = {stem cells, differentiation, stochastic modeling, parameter estimation, tree structure},
}

@article{BoigerHas2016,
	Author = {Boiger, R. and Hasenauer, J. and Hross, S. and Kaltenbacher, B.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1088/0266-5611/32/12/125009},
	Journal = {Inverse Prob.},
	Keywords = {parameter estimation, profile likelihood, PDE, AMICI},
	Month = {Dec.},
	Number = {12},
	Pages = {125009},
	Title = {Integration based profile likelihood calculation for {PDE} constrained parameter estimation problems},
	Volume = {32},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1088/0266-5611/32/12/125009}}

@article{FiedlerRae2016,
	Author = {Fiedler, A. and Raeth, S. and Theis, F. J. and Hausser, A. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1186/s12918-016-0319-7},
	Journal = {{BMC} Syst. Biol.},
	Keywords = {parameter estimation, steady state},
	Month = {Aug.},
	Number = {80},
	Title = {Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints},
	Volume = {10},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1186/s12918-016-0319-7}}

@Article{FischerFie2017,
  author        = {Fischer, David S. and Fiedler, Anna K. and Kernfeld, Eric and Genga, Ryan M. J. and Bastidas-Ponce, Aim\'ee and Bakhti, Mostafa and Lickert, Heiko and Hasenauer, Jan and Maehr, Rene and Theis, Fabian J.},
  title         = {Inferring population dynamics from single-cell RNA-sequencing time series data},
  journal       = {Nature Biotechnology},
  year          = {2019},
  volume        = {37},
  pages         = {461--468},
  abstract      = {Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in T-cell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cell{\textquoteright}s state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates time-series and genetic knock-out information with such transcriptome-based descriptions in order to describe and analyze the real-time evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cell-state-dependent growth and differentiation of thymic T-cell development. The model approximates a developmental potential function (Waddington{\textquoteright}s landscape) and suggests that thymic T-cell development is biphasic and not strictly deterministic before beta-selection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.},
  bdsk-url-1    = {https://www.biorxiv.org/content/early/2017/11/14/219188},
  bdsk-url-2    = {https://doi.org/10.1101/219188},
  date-added    = {2019-03-19 23:04:09 +0100},
  date-modified = {2019-07-26 12:14:33 +0200},
  doi           = {10.1038/s41587-019-0088-0},
  keywords      = {pseudo time; single-cell RNA-seq; PDE},
  url           = {https://www.nature.com/articles/s41587-019-0088-0},
}

@article{FroehlichKal2017,
	Author = {Fr\"ohlich, F. and Kaltenbacher, B. and Theis, F. J. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1371/journal.pcbi.1005331},
	Journal = {{PLoS} Comput. Biol.},
	Keywords = {parameter estimation; adjoint sensitivity},
	Month = {Jan.},
	Number = {1},
	Pages = {e1005331},
	Title = {Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks},
	Volume = {13},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1005331}}

@article{FroehlichRei2018,
	Abstract = {Single-cell time-lapse studies have advanced the quantitative understanding of cellular pathways and their inherent cell-to-cell variability. However, parameters retrieved from individual experiments are model dependent and their estimation is limited, if based on solely one kind of experiment. Hence, methods to integrate data collected under different conditions are expected to improve model validation and information content. Here we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.},
	Author = {Fr{\"o}hlich, Fabian and Reiser, Anita and Fink, Laura and Wosch{\'e}e, Daniel and Ligon, Thomas and Theis, Fabian Joachim and R{\"a}dler, Joachim Oskar and Hasenauer, Jan},
	Da = {2018/12/10},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1038/s41540-018-0079-7},
	Isbn = {2056-7189},
	Journal = {npj Systems Biology and Applications},
	Keywords = {mixed-effect models},
	Number = {1},
	Pages = {1},
	Title = {Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection},
	Ty = {JOUR},
	Url = {https://doi.org/10.1038/s41540-018-0079-7},
	Volume = {5},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1038/s41540-018-0079-7}}

@article{FroehlichThe2016,
	Author = {Fr\"ohlich, F. and Theis, F. J. and R\"{a}dler, J. O. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1093/bioinformatics/btw764},
	Journal = {Bioinformatics},
	Keywords = {events, logical model},
	Month = {Apr.},
	Number = {7},
	Pages = {1049--1056},
	Title = {Parameter estimation for dynamical systems with discrete events and logical operations},
	Volume = {33},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/btw764}}

@article{FroehlichTho2016,
	Author = {Fr\"ohlich, F. and Thomas, P. and Kazeroonian, A. and Theis, F. J. and Grima, R. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1371/journal.pcbi.1005030},
	Journal = {{PLoS} Comput. Biol.},
	Keywords = {SSE, moment equation, parameter estimation, stochastic modeling},
	Month = {July},
	Number = {7},
	Pages = {e1005030},
	Title = {Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion},
	Volume = {12},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1005030}}

@inproceedings{HrossFie2016,
	Author = {Hross, S. and Fiedler, A. and Theis, F. J. and Hasenauer, J.},
	Booktitle = {Proc. 6th {IFAC} Conf. Found. Syst. Biol. Eng.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1016/j.ifacol.2016.12.136},
	Editor = {Findeisen, R. and Bullinger, E. and Balsa-Canto, E. and Bernaerts, K.},
	Keywords = {PDE, parameter estimation, Pom1, cell division},
	Number = {26},
	Pages = {264--269},
	Publisher = {IFAC-PapersOnLine},
	Title = {Quantitative comparison of competing {PDE} models for {Pom1p} dynamics in fission yeast},
	Volume = {49},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.ifacol.2016.12.136}}

@article{KazeroonianFro2016,
	Author = {Kazeroonian, A. and Fr{\"o}hlich, F. and Raue, A. and Theis, F. J. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1371/journal.pone.0146732},
	Journal = {{PLoS} {ONE}},
	Keywords = {CME, moment equation, moment closure, SSE, SSA, toolbox},
	Month = {January},
	Number = {1},
	Pages = {e0146732},
	Title = {{CERENA:} {Chemical} {REaction} Network {Analyzer -- A} Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics},
	Volume = {11},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1371/journal.pone.0146732}}

@article{KazeroonianThe2017,
	Author = {Kazeroonian, A. and Theis, F. J. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1093/bioinformatics/btx249},
	Journal = {Bioinformatics},
	Keywords = {moment equation, scale-free networks, scaling, approximation},
	Month = {July},
	Number = {14},
	Pages = {i293--i300},
	Title = {A scalable moment-closure approximation for large-scale biochemical reaction networks},
	Volume = {33},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/btx249}}

@inproceedings{LoosFie2016,
	Author = {Loos, C. and Fiedler, A. and Hasenauer, J.},
	Booktitle = {Proc. 13th Int. Conf. Comp. Meth. Syst. Biol.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1007/978-3-319-45177-0},
	Editor = {Bartocci, E. and Lio, P. and Paoletti, N.},
	Keywords = {parameter estimation, ODE, cell-to-cell variability},
	Month = {Sept.},
	Pages = {186--200},
	Publisher = {Springer International Publishing},
	Series = {Lecture Notes in Bioinformatics},
	Title = {Parameter estimation for reaction rate equation constrained mixture models},
	Year = {2016},
	Bdsk-Url-1 = {https://doi.org/10.1007/978-3-319-45177-0}}

@article{LoosKra2018,
	Abstract = {Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems. In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness, and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches.},
	Author = {Loos, Carolin and Krause, Sabrina and Hasenauer, Jan},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1093/bioinformatics/bty514},
	Journal = {Bioinformatics},
	Keywords = {Parameter estimation; ODE},
	Month = {July},
	Number = {24},
	Pages = {4266--4273},
	Title = {Hierarchical optimization for the efficient parametrization of {ODE} models},
	Volume = {34},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/bty514}}

@InBook{LoosMar2015,
  author        = {Loos, C. and Marr, C. and Theis, F. J. and Hasenauer, J.},
  editor        = {Roux, O. and Bourdon, J.},
  chapter       = {Approximate {B}ayesian {C}omputation for stochastic single-cell time-lapse data using multivariate test statistics},
  pages         = {52--63},
  publisher     = {Springer International Publishing},
  title         = {Computational Methods in Systems Biology},
  year          = {2015},
  month         = {Sept.},
  series        = {Lecture Notes in Computer Science},
  volume        = {9308},
  date-added    = {2019-03-19 23:04:09 +0100},
  date-modified = {2019-03-19 23:04:09 +0100},
  doi           = {10.1007/978-3-319-23401-4_6},
  keywords      = {ABC, single-cell time lapse, parameter estimation},
}

@article{LoosMoe2018,
	Author = {Loos, Carolin and Moeller, Katharina and Fr{\"o}hlich, Fabian and Hucho, Tim and Hasenauer, Jan},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1016/j.cels.2018.04.008},
	Journal = {Cell Systems},
	Keywords = {heterogeneity; single-cell; extrinsic noise; subpopulation},
	Number = {5},
	Pages = {593--603},
	Publisher = {Elsevier},
	Title = {A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-To-Cell Variability},
	Volume = {6},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.cels.2018.04.008}}

@article{MaierLoo2017,
	Author = {Maier, C. and Loos, C. and Hasenauer, J.},
	Date-Added = {2019-03-19 23:04:09 +0100},
	Date-Modified = {2019-03-19 23:04:09 +0100},
	Doi = {10.1093/bioinformatics/btw703},
	Journal = {Bioinformatics},
	Keywords = {Robust estimation, parameter estimation, outlier},
	Month = {Mar.},
	Number = {5},
	Pages = {718--725},
	Title = {Robust parameter estimation for dynamical systems from outlier-corrupted data},
	Volume = {33},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1093/bioinformatics/btw703}}

@Article{SchaelteSta2018,
  author        = {Sch{\"a}lte, Y. and Stapor, P. and Hasenauer, J.},
  journal       = {FAC-PapersOnLine},
  title         = {Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology},
  year          = {2018},
  number        = {19},
  pages         = {98--101},
  volume        = {51},
  date-added    = {2019-03-19 23:04:09 +0100},
  date-modified = {2019-07-26 09:04:30 +0200},
  doi           = {10.1016/j.ifacol.2018.09.025},
  keywords      = {Optimization, Parameter estimation, derivative-free, derivatives, gradients},
}

@Article{StaporFro2018,
  author        = {Stapor, Paul and Fr{\"o}hlich, Fabian and Hasenauer, Jan},
  journal       = {Bioinformatics},
  title         = {Optimization and profile calculation of {ODE} models using second order adjoint sensitivity analysis},
  year          = {2018},
  number        = {13},
  pages         = {i151--i159},
  volume        = {34},
  date-added    = {2019-03-19 23:04:09 +0100},
  date-modified = {2019-07-26 09:06:44 +0200},
  doi           = {10.1093/bioinformatics/bty230},
  keywords      = {adjoint sensitivity, second order methods, hessian, optimization, profile likelihood, hybrid methods, profile integration},
  publisher     = {Oxford University Press},
}

@article{DharmarajanKal2019,
	Author = {Dharmarajan, Lekshmi and Kaltenbach, Hans-Michael and Rudolf, Fabian and Stelling, Joerg},
	Comment = {doi: 10.1016/j.cels.2018.12.007},
	Doi = {10.1016/j.cels.2018.12.007},
	Issn = {2405-4712},
	Journal = {Cell Systems},
	Month = mar,
	Number = {1},
	Pages = {15--26.e11},
	Publisher = {Elsevier},
	Title = {A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics},
	Url = {https://doi.org/10.1016/j.cels.2018.12.007},
	Volume = {8},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1016/j.cels.2018.12.007}}

@Article{GreggSar2019,
  author       = {Gregg, Robert W and Sarkar, Saumendra N and Shoemaker, Jason E},
  journal      = {Journal of theoretical biology},
  title        = {Mathematical modeling of the cGAS pathway reveals robustness of DNA sensing to TREX1 feedback.},
  year         = {2019},
  issn         = {1095-8541},
  month        = feb,
  pages        = {148--157},
  volume       = {462},
  abstract     = {Cyclic GMP-AMP synthase (cGAS) has recently been identified as the primary protein that detects cytosolic double stranded DNA to invoke a type I interferon response. The cGAS pathway is vital in the recognition of DNA encoded viruses as well as self-DNA leaked from the nucleus of damaged cells. Currently, the dynamics regulating the cGAS pathway are poorly understood; limiting our knowledge of how DNA-induced immune responses are regulated. Using systems biology approaches, we formulated a mathematical model to describe the dynamics of this pathway and examine the resulting system-level emergent properties. Unknown model parameters were fit to data compiled from literature using a Parallel Tempering Markov Chain Monte Carlo (PT-MCMC) approach, resulting in an ensemble of parameterized models. A local sensitivity analysis demonstrated that parameter sensitivity trends across model ensembles were independent of the select parameterization. An in-silico knock-down of TREX1 found that the interferon response is highly robust, showing that complete inhibition is necessary to induce chemical conditions consistent with chronic inflammation. Lastly, we demonstrate that the model recapitulates interferon expression data resulting from small molecule inhibition of cGAS. Overall, the importance of this model is exhibited in its capacity to identify sensitive components of the cGAS pathway, generate testable hypotheses, and confirm experimental observations.},
  bdsk-url-1   = {https://doi.org/10.1016/j.jtbi.2018.11.001},
  country      = {England},
  doi          = {10.1016/j.jtbi.2018.11.001},
  groups       = {[dweindl:]},
  issn-linking = {0022-5193},
  keywords     = {Interferon signaling; ODE modeling; Systems biology},
  nlm-id       = {0376342},
  owner        = {NLM},
  pii          = {S0022-5193(18)30548-4},
  pmid         = {30395807},
  pubmodel     = {Print-Electronic},
  pubstatus    = {ppublish},
  revised      = {2018-12-23},
}

@Article{PittBan2019,
  author          = {Pitt, Jake Alan and Banga, Julio R},
  journal         = {BMC bioinformatics},
  title           = {Parameter estimation in models of biological oscillators: an automated regularised estimation approach.},
  year            = {2019},
  issn            = {1471-2105},
  month           = feb,
  pages           = {82},
  volume          = {20},
  abstract        = {Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability. We successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh-Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls. Our approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power).},
  bdsk-url-1      = {https://doi.org/10.1186/s12859-019-2630-y},
  citation-subset = {IM},
  completed       = {2019-03-13},
  country         = {England},
  doi             = {10.1186/s12859-019-2630-y},
  groups          = {dweindl:6},
  issn-linking    = {1471-2105},
  issue           = {1},
  keywords        = {Algorithms; Biological Clocks; Calibration; Humans; Metabolic Networks and Pathways; Models, Biological; Signal Transduction; Systems Biology, methods; Dynamic modelling; Global optimisation; Parameter bounding; Parameter estimation; Regularisation},
  nlm-id          = {100965194},
  owner           = {NLM},
  pii             = {10.1186/s12859-019-2630-y},
  pmc             = {PMC6377730},
  pmid            = {30770736},
  pubmodel        = {Electronic},
  pubstatus       = {epublish},
  revised         = {2019-03-13},
}

@Article{KaltenbacherPed2018,
  author     = {Barbara Kaltenbacher and Barbara Pedretscher},
  journal    = {Journal of Mathematical Analysis and Applications},
  title      = {Parameter estimation in SDEs via the Fokker--Planck equation: Likelihood function and adjoint based gradient computation},
  year       = {2018},
  issn       = {0022-247X},
  number     = {2},
  pages      = {872 - 884},
  volume     = {465},
  abstract   = {In this paper we consider the problem of identifying parameters in stochastic differential equations. For this purpose, we transform the originally stochastic and nonlinear state equation to a deterministic linear partial differential equation for the transition probability density. We provide an appropriate likelihood cost function for parameter fitting, and derive an adjoint based approach for the computation of its gradient.},
  bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0022247X18304414},
  bdsk-url-2 = {https://doi.org/10.1016/j.jmaa.2018.05.048},
  doi        = {10.1016/j.jmaa.2018.05.048},
  keywords   = {Parameter identification, Stochastic differential equation, State space model, Likelihood function, Adjoint method},
  url        = {http://www.sciencedirect.com/science/article/pii/S0022247X18304414},
}

@InProceedings{NousiainenInt2019,
  author    = {Nousiainen, Kari and Intosalmi, Jukka and L{\"a}hdesm{\"a}ki, Harri},
  booktitle = {Algorithms for Computational Biology},
  title     = {A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation},
  year      = {2019},
  address   = {Cham},
  editor    = {Holmes, Ian and Mart{\'\i}n-Vide, Carlos and Vega-Rodr{\'\i}guez, Miguel A.},
  pages     = {191--202},
  publisher = {Springer International Publishing},
  abstract  = {Cell differentiation and development are for a great part steered by cell type specific enhancers. Transcription factor (TF) binding to an enhancer together with DNA looping result in transcription initiation. In addition to binding motifs for TFs, enhancer regions typically contain specific histone modifications. This information has been used to detect enhancer regions and classify them into different subgroups. However, it is poorly understood how TF binding and histone modifications are causally connected and what kind of molecular dynamics steer the activation process.},
  doi       = {10.1007/978-3-030-18174-1_14},
  isbn      = {978-3-030-18174-1},
}

@Article{SchmiesterSch2019,
  author   = {Schmiester, Leonard and Schälte, Yannik and Fröhlich, Fabian and Hasenauer, Jan and Weindl, Daniel},
  title    = {{Efficient parameterization of large-scale dynamic models based on relative measurements}},
  journal  = {Bioinformatics},
  year     = {2019},
  month    = {07},
  issn     = {1367-4803},
  abstract = {{Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset, and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (\\>1000 state variables, \\>4000 parameters) using relative protein, phospho-protein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.Supplementary information are available at Bioinformatics online. Supplementary code and data are available online at https://doi.org/10.5281/zenodo.3254429 and https://doi.org/10.5281/zenodo.3254441.}},
  doi      = {10.1093/bioinformatics/btz581},
  eprint   = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz581/29004243/btz581.pdf},
  url      = {https://doi.org/10.1093/bioinformatics/btz581},
}

@Article{PedretscherKal2019,
  author   = {B. Pedretscher and B. Kaltenbacher and O. Pfeiler},
  journal  = {Applied Numerical Mathematics},
  title    = {Parameter identification and uncertainty quantification in stochastic state space models and its application to texture analysis},
  year     = {2019},
  issn     = {0168-9274},
  pages    = {38 - 54},
  volume   = {146},
  abstract = {In this paper, a computational framework, which enables efficient and robust parameter identification, as well as uncertainty quantification in state space models based on Itô stochastic processes, is presented. For optimization, a Maximum Likelihood approach based on the system's corresponding Fokker-Planck equation is followed. Gradient information is included by means of an adjoint approach, which is based on the Lagrangian of the optimization problem. To quantify the uncertainty of the Maximum-A-Posteriori estimates of the model parameters, a Bayesian inference approach based on Markov Chain Monte Carlo simulations, as well as profile likelihoods are implemented and compared in terms of runtime and accuracy. The framework is applied to experimental electron backscatter diffraction data of a fatigued metal film, where the aim is to develop a model, which consistently and physically meaningfully captures the metal's microstructural changes that are caused by external loading.},
  doi      = {10.1016/j.apnum.2019.06.020},
  keywords = {Stochastic state space model, Parameter identification, Uncertainty quantification, Profile likelihood, Adjoint approach, Fokker-Planck equation, Thermo-mechanical fatigue, Texture analysis},
  url      = {http://www.sciencedirect.com/science/article/pii/S0168927419301722},
}

@Article{FroehlichKes2018,
  author    = {Fröhlich, Fabian and Kessler, Thomas and Weindl, Daniel and Shadrin, Alexey and Schmiester, Leonard and Hache, Hendrik and Muradyan, Artur and Schütte, Moritz and Lim, Ji-Hyun and Heinig, Matthias and Theis, Fabian J. and Lehrach, Hans and Wierling, Christoph and Lange, Bodo and Hasenauer, Jan},
  title     = {Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model},
  journal   = {Cell Systems},
  year      = {2018},
  volume    = {7},
  number    = {6},
  pages     = {567--579.e6},
  issn      = {2405-4712},
  abstract  = {Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.
Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.},
  comment   = {doi: 10.1016/j.cels.2018.10.013},
  doi       = {10.1016/j.cels.2018.10.013},
  publisher = {Elsevier},
  url       = {https://doi.org/10.1016/j.cels.2018.10.013},
}

@Article{AlabertLoo2020,
  author          = {Alabert, Constance and Loos, Carolin and Voelker-Albert, Moritz and Graziano, Simona and Forné, Ignasi and Reveron-Gomez, Nazaret and Schuh, Lea and Hasenauer, Jan and Marr, Carsten and Imhof, Axel and Groth, Anja},
  journal         = {Cell reports},
  title           = {Domain Model Explains Propagation Dynamics and Stability of Histone H3K27 and H3K36 Methylation Landscapes.},
  year            = {2020},
  issn            = {2211-1247},
  month           = jan,
  pages           = {1223--1234.e8},
  volume          = {30},
  abstract        = {Chromatin states must be maintained during cell proliferation to uphold cellular identity and genome integrity. Inheritance of histone modifications is central in this process. However, the histone modification landscape is challenged by incorporation of new unmodified histones during each cell cycle, and the principles governing heritability remain unclear. We take a quantitative computational modeling approach to describe propagation of histone H3K27 and H3K36 methylation states. We measure combinatorial H3K27 and H3K36 methylation patterns by quantitative mass spectrometry on subsequent generations of histones. Using model comparison, we reject active global demethylation and invoke the existence of domains defined by distinct methylation endpoints. We find that H3K27me3 on pre-existing histones stimulates the rate of de novo H3K27me3 establishment, supporting a read-write mechanism in timely chromatin restoration. Finally, we provide a detailed quantitative picture of the mutual antagonism between H3K27 and H3K36 methylation and propose that it stabilizes epigenetic states across cell division.},
  citation-subset = {IM},
  country         = {United States},
  doi             = {10.1016/j.celrep.2019.12.060},
  issue           = {4},
  nlm-id          = {101573691},
  owner           = {NLM},
  pii             = {S2211-1247(19)31717-6},
  pmid            = {31995760},
  pubmodel        = {Print},
  pubstate        = {ppublish},
  revised         = {2020-07-30},
}

@Article{SchmiesterWei2020,
  author          = {Schmiester, Leonard and Weindl, Daniel and Hasenauer, Jan},
  journal         = {Journal of mathematical biology},
  title           = {Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach.},
  year            = {2020},
  issn            = {1432-1416},
  month           = jul,
  abstract        = {Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.},
  citation-subset = {IM},
  country         = {Germany},
  doi             = {10.1007/s00285-020-01522-w},
  issn-linking    = {0303-6812},
  keywords        = {Dynamical modeling; Optimization; Parameter estimation; Qualitative data; Systems Biology},
  nlm-id          = {7502105},
  owner           = {NLM},
  pii             = {10.1007/s00285-020-01522-w},
  pmid            = {32696085},
  pubmodel        = {Print-Electronic},
  pubstate        = {aheadofprint},
  revised         = {2020-07-22},
}

@Article{KapferSta2019,
  author   = {Eva-Maria Kapfer and Paul Stapor and Jan Hasenauer},
  journal  = {IFAC-PapersOnLine},
  title    = {Challenges in the calibration of large-scale ordinary differential equation models},
  year     = {2019},
  issn     = {2405-8963},
  note     = {8th Conference on Foundations of Systems Biology in Engineering FOSBE 2019},
  number   = {26},
  pages    = {58 - 64},
  volume   = {52},
  abstract = {Mathematical models based on ordinary differential equations have been employed with great success to study complex biological systems. With soaring data availability, more and more models of increasing size are being developed. When working with these large-scale models, several challenges arise, such as high computation times or poor identifiability of model parameters. In this work, we review and illustrate the most common challenges using a published model of cellular metabolism. We summarize currently available methods to deal with some of these challenges while focusing on reproducibility and reusability of models, efficient and robust model simulation and parameter estimation.},
  doi      = {10.1016/j.ifacol.2019.12.236},
  keywords = {Differential equations, Dynamic modelling, Steady states, Large-scale systems, Parameter estimation, Reproducibility},
  url      = {http://www.sciencedirect.com/science/article/pii/S2405896319321196},
}

@Article{LinesPas2019,
  author   = {Glenn {Terje Lines} and Łukasz Paszkowski and Leonard Schmiester and Daniel Weindl and Paul Stapor and Jan Hasenauer},
  journal  = {IFAC-PapersOnLine},
  title    = {Efficient computation of steady states in large-scale ODE models of biochemical reaction networks},
  year     = {2019},
  issn     = {2405-8963},
  note     = {8th Conference on Foundations of Systems Biology in Engineering FOSBE 2019},
  number   = {26},
  pages    = {32 - 37},
  volume   = {52},
  abstract = {In systems and computational biology, ordinary differential equations are used for the mechanistic modelling of biochemical networks. These models can easily have hundreds of states and parameters. Typically most parameters are unknown and estimated by fitting model output to observation. During parameter estimation the model needs to be solved repeatedly, sometimes millions of times. This can then be a computational bottleneck, and limits the employment of such models. In many situations the experimental data provides information about the steady state of the biochemical reaction network. In such cases one only needs to obtain the equilibrium state for a given set of model parameters. In this paper we exploit this fact and solve the steady state problem directly rather than integrating the ODE forward in time until steady state is reached. We use Newton’s method - like some previous studies - and develop several improvements to achieve robust convergence. To address the reliance of Newtons method on good initial guesses, we propose a continuation method. We show that the method works robustly in this setting and achieves a speed up of up to 100 compared to using ODE solves.},
  doi      = {10.1016/j.ifacol.2019.12.232},
  keywords = {Differential equations, Dynamic modelling, Large-scale systems, Steady states, Steady-state stability, Steady-state errors, Parameter estimation},
  url      = {http://www.sciencedirect.com/science/article/pii/S2405896319321135},
}

@Article{VillaverdeRai2019,
  author   = {Alejandro F. Villaverde and Elba Raimúndez and Jan Hasenauer and Julio R. Banga},
  journal  = {IFAC-PapersOnLine},
  title    = {A Comparison of Methods for Quantifying Prediction Uncertainty in Systems Biology},
  year     = {2019},
  issn     = {2405-8963},
  note     = {8th Conference on Foundations of Systems Biology in Engineering FOSBE 2019},
  number   = {26},
  pages    = {45 - 51},
  volume   = {52},
  abstract = {The parameters of dynamical models of biological processes always possess some degree of uncertainty. This parameter uncertainty translates into an uncertainty of model predictions. The trajectories of unmeasured state variables are examples of such predictions. Quantifying the uncertainty associated with a given prediction is an important problem for model developers and users. However, the nonlinearity and complexity of most dynamical models renders it nontrivial. Here, we evaluate three state-of-the-art approaches for prediction uncertainty quantification using two models of different sizes and computational complexities. We discuss the trade-offs between applicability and statistical interpretability of the different methods, and provide guidelines for their application.},
  doi      = {10.1016/j.ifacol.2019.12.234},
  keywords = {Computational methods, Dynamic models, Nonlinear systems, Observability, Prediction error methods, State estimation, Uncertainty},
  url      = {http://www.sciencedirect.com/science/article/pii/S2405896319321159},
}

@Article{VillaverdeFroe2018,
  author   = {Villaverde, Alejandro F and Fröhlich, Fabian and Weindl, Daniel and Hasenauer, Jan and Banga, Julio R},
  journal  = {Bioinformatics},
  title    = {{Benchmarking optimization methods for parameter estimation in large kinetic models}},
  year     = {2018},
  issn     = {1367-4803},
  month    = {08},
  number   = {5},
  pages    = {830-838},
  volume   = {35},
  abstract = {{Kinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to surmount these issues, it is difficult to choose the best one for a given problem a priori. A systematic comparison of parameter estimation methods for problems with tens to hundreds of optimization variables is currently missing, and smaller studies provided contradictory findings.We use a collection of benchmarks to evaluate the performance of two families of optimization methods: (i) multi-starts of deterministic local searches and (ii) stochastic global optimization metaheuristics; the latter may be combined with deterministic local searches, leading to hybrid methods. A fair comparison is ensured through a collaborative evaluation and a consideration of multiple performance metrics. We discuss possible evaluation criteria to assess the trade-off between computational efficiency and robustness. Our results show that, thanks to recent advances in the calculation of parametric sensitivities, a multi-start of gradient-based local methods is often a successful strategy, but a better performance can be obtained with a hybrid metaheuristic. The best performer combines a global scatter search metaheuristic with an interior point local method, provided with gradients estimated with adjoint-based sensitivities. We provide an implementation of this method to render it available to the scientific community.The code to reproduce the results is provided as Supplementary Material and is available at Zenodo https://doi.org/10.5281/zenodo.1304034.Supplementary data are available at Bioinformatics online.}},
  doi      = {10.1093/bioinformatics/bty736},
  eprint   = {https://academic.oup.com/bioinformatics/article-pdf/35/5/830/27994799/bty736.pdf},
  url      = {https://doi.org/10.1093/bioinformatics/bty736},
}

@Article{WangSta2019,
  author   = {Dantong Wang and Paul Stapor and Jan Hasenauer},
  journal  = {IFAC-PapersOnLine},
  title    = {Dirac mixture distributions for the approximation of mixed effects models},
  year     = {2019},
  issn     = {2405-8963},
  note     = {8th Conference on Foundations of Systems Biology in Engineering FOSBE 2019},
  number   = {26},
  pages    = {200 - 206},
  volume   = {52},
  abstract = {Mixed effect modeling is widely used to study cell-to-cell and patient-to-patient variability. The population statistics of mixed effect models is usually approximated using Dirac mixture distributions obtained using Monte-Carlo, quasi Monte-Carlo, and sigma point methods. Here, we propose the use of a method based on the Cramér-von Mises Distance, which has been introduced in the context of filtering. We assess the accuracy of the different methods using several problems and provide the first scalability study for the Cramér-von Mises Distance method. Our results indicate that for a given number of points, the method based on the modified Cramér-von Mises Distance method tends to achieve a better approximation accuracy than Monte-Carlo and quasi Monte-Carlo methods. In contrast to sigma-point methods, the method based on the modified Cramér-von Mises Distance allows for a flexible number of points and a more accurate approximation for nonlinear problems.},
  doi      = {10.1016/j.ifacol.2019.12.258},
  keywords = {Mixed effect model, Dirac mixture distribution, Monte Carlo method, Sigma point method, Differential equations},
  url      = {http://www.sciencedirect.com/science/article/pii/S2405896319321433},
}

@Article{GerosaChi2020,
  author    = {Luca Gerosa and Christopher Chidley and Fabian Fröhlich and Gabriela Sanchez and Sang Kyun Lim and Jeremy Muhlich and Jia-Yun Chen and Sreeram Vallabhaneni and Gregory J. Baker and Denis Schapiro and Mariya I. Atanasova and Lily A. Chylek and Tujin Shi and Lian Yi and Carrie D. Nicora and Allison Claas and Thomas S.C. Ng and Rainer H. Kohler and Douglas A. Lauffenburger and Ralph Weissleder and Miles A. Miller and Wei-Jun Qian and H. Steven Wiley and Peter K. Sorger},
  journal   = {Cell Systems},
  title     = {Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells},
  year      = {2020},
  issn      = {2405-4712},
  abstract  = {Summary
Targeted inhibition of oncogenic pathways can be highly effective in halting the rapid growth of tumors but often leads to the emergence of slowly dividing persister cells, which constitute a reservoir for the selection of drug-resistant clones. In BRAFV600E melanomas, RAF and MEK inhibitors efficiently block oncogenic signaling, but persister cells emerge. Here, we show that persister cells escape drug-induced cell-cycle arrest via brief, sporadic ERK pulses generated by transmembrane receptors and growth factors operating in an autocrine/paracrine manner. Quantitative proteomics and computational modeling show that ERK pulsing is enabled by rewiring of mitogen-activated protein kinase (MAPK) signaling: from an oncogenic BRAFV600E monomer-driven configuration that is drug sensitive to a receptor-driven configuration that involves Ras-GTP and RAF dimers and is highly resistant to RAF and MEK inhibitors. Altogether, this work shows that pulsatile MAPK activation by factors in the microenvironment generates a persistent population of melanoma cells that rewires MAPK signaling to sustain non-genetic drug resistance.},
  doi       = {10.1016/j.cels.2020.10.002},
  keywords  = {systems pharmacology, targeted therapy, non-genetic drug resistance, signaling plasticity, cancer persistence, BRAF melanoma, MAPK pathway, kinetic modeling, kinase inhibitors},
  timestamp = {2020-11-09},
  url       = {http://www.sciencedirect.com/science/article/pii/S2405471220303707},
}

@Article{StenEli2020,
  author    = {Sebastian Sten and Fredrik Elinder and Gunnar Cedersund and Maria Engström},
  journal   = {NeuroImage},
  title     = {A quantitative analysis of cell-specific contributions and the role of anesthetics to the neurovascular coupling},
  year      = {2020},
  issn      = {1053-8119},
  pages     = {116827},
  volume    = {215},
  abstract  = {The neurovascular coupling (NVC) connects neuronal activity to hemodynamic responses in the brain. This connection is the basis for the interpretation of functional magnetic resonance imaging data. Despite the central role of this coupling, we lack detailed knowledge about cell-specific contributions and our knowledge about NVC is mainly based on animal experiments performed during anesthesia. Anesthetics are known to affect neuronal excitability, but how this affects the vessel diameters is not known. Due to the high complexity of NVC data, mathematical modeling is needed for a meaningful analysis. However, neither the relevant neuronal subtypes nor the effects of anesthetics are covered by current models. Here, we present a mathematical model including GABAergic interneurons and pyramidal neurons, as well as the effect of an anesthetic agent. The model is consistent with data from optogenetic experiments from both awake and anesthetized animals, and it correctly predicts data from experiments with different pharmacological modulators. The analysis suggests that no downstream anesthetic effects are necessary if one of the GABAergic interneuron signaling pathways include a Michaelis-Menten expression. This is the first example of a quantitative model that includes both the cell-specific contributions and the effect of an anesthetic agent on the NVC.},
  doi       = {10.1016/j.neuroimage.2020.116827},
  keywords  = {Functional hyperemia, Mathematical modeling, Cerebral hemodynamics, Systems biology, Functional magnetic resonance imaging (fMRI), Blood oxygen level dependent (BOLD) response},
  timestamp = {2020-11-09},
  url       = {http://www.sciencedirect.com/science/article/pii/S1053811920303141},
}

@PhdThesis{Sten2020,
  author      = {Sten, Sebastian},
  school      = {Linköping UniversityLinköping UniversityLinköping University, Division of Diagnostics and Specialist Medicine, Faculty of Medicine and Health Sciences, Center for Medical Image Science and Visualization (CMIV)},
  title       = {Mathematical modeling of neurovascular coupling},
  year        = {2020},
  abstract    = {The brain is critically dependent on the continuous supply of oxygen and glucose, which is carried and delivered by blood. When a brain region is activated, metabolism of these substrates increases rapidly, but is quickly offset by a substantially higher increase in blood flow to that region, resulting in a brief oversupply of these substrates. This phenomenon is referred to as functional hyperemia, and forms the foundation of functional neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI), which captures a Blood Oxygen Level-Dependent (BOLD) signal. fMRI exploits these BOLD signals to infer brain activity, an approach that has revolutionized the research of brain function over the last 30 years. Due to the indirect nature of this measure, a deeper understanding of the connection between brain activity and hemodynamic changes — a neurovascular coupling (NVC) — is essential in order to fully interpret such functional imaging data. NVC connects the synaptic activity of neurons with local changes in cerebral blood flow, cerebral blood volume, and cerebral metabolism of oxygen, through a complex signaling network, consisting of multiple different brain cells which release a myriad of distinct vasoactive messengers with specific vascular targets. To aid with this complexity, mathematical modeling can provide vital help using methods and tools from the field of Systems Biology. Previous models of the NVC exist, conventionally describing quasi-phenomenological steps translating neuronal activity into hemodynamic changes. However, no mechanistic mathematical model that describe the known intracellular mechanisms or hypotheses underlying the NVC, and which can account for a wide variety of NVC related measurements, currently exists. Therefore, in this thesis, we apply a Systems Biology approach to develop such intracellular mechanisms based models using in vivo experimental data consisting of different NVC related measures in rodents, primates, and humans. Paper I investigates two widely discussed hypotheses describing the NVC: the metabolic feedback hypothesis, and the vasoactive feed-forward hypothesis. We illustrate through multiple model rejections that only a model describing a combination of the two hypotheses can capture the qualitative features of the BOLD signal, as measured in humans. This combined model can describe data used for training, as well as predict independent validation data not previously seen by the model before. Paper II extends this model to describe the negative BOLD response, where the blood oxygenation drops below basal levels, which is commonly observed in clinical and cognitive studies. The model explains the negative BOLD response as the result of neuronal inhibition, describing and adequately predicting experimental data from two different experiments. In Paper III, we develop a first model including the cell-specific contributions of GABAergic interneurons and pyramidal neurons to functional hyperemia, using data of optogenetic and sensory stimuli in rodents for both awake and anesthesia conditions. The model captures the effect of the anesthetic as purely acting on the neuronal level if a Michaelis-Menten expression is included, and it also correctly predicts data from experiments with different pharmacological inhibitors. Finally, in Paper IV, we extend the model in Paper III to describe and predict a majority of the relevant hemodynamic NVC measures using data from rodents, primates, and humans. The model suggests an explanation for observed bi-modal behaviors, and can be used to generate new insights regarding the underpinnings of other complicated observed behaviors. This model constitutes the most complete mechanistic model of the NVC to date. This new model-based understanding opens the door for a more integrative approach to the analysis of neuroimaging data, with potential applications in both basic science and in the clinic.},
  doi         = {10.3384/diss.diva-167806},
  institution = {Linköping University, Division of Diagnostics and Specialist Medicine},
  isbn        = {9789179298388},
  issn        = {0345-0082},
  number      = {1742},
  pages       = {108},
  series      = {Linköping University Medical Dissertations},
  timestamp   = {2020-11-09},
}

@Article{TsipaPit2020,
  author    = {Tsipa, Argyro and Pitt, Jake Alan and Banga, Julio R. and Mantalaris, Athanasios},
  journal   = {Bioprocess and Biosystems Engineering},
  title     = {A dual-parameter identification approach for data-based predictive modeling of hybrid gene regulatory network-growth kinetics in Pseudomonas putida mt-2},
  year      = {2020},
  issn      = {1615-7605},
  number    = {9},
  pages     = {1671--1688},
  volume    = {43},
  abstract  = {Data integration to model-based description of biological systems incorporating gene dynamics improves the performance of microbial systems. Bioprocess performance, typically predicted using empirical Monod-type models, is essential for a sustainable bioeconomy. To replace empirical models, we updated a hybrid gene regulatory network-growth kinetic model, predicting aromatic pollutants degradation and biomass growth in Pseudomonas putida mt-2. We modeled a complex biological system including extensive information to understand the role of the regulatory elements in toluene biodegradation and biomass growth. The updated model exhibited extra complications such as the existence of oscillations and discontinuities. As parameter estimation of complex biological models remains a key challenge, we used the updated model to present a dual-parameter identification approach (the ‘dual approach’) combining two independent methodologies. Approach I handled the complexity by incorporation of demonstrated biological knowledge in the model-development process and combination of global sensitivity analysis and optimisation. Approach II complemented Approach I handling multimodality, ill-conditioning and overfitting through regularisation estimation, global optimisation, and identifiability analysis. To systematically quantify the biological system, we used a vast amount of high-quality time-course data. The dual approach resulted in an accurately calibrated kinetic model (NRMSE: 0.17055) efficiently handling the additional model complexity. We tested model validation using three independent experimental data sets, achieving greater predictive power (NRMSE: 0.18776) than the individual approaches (NRMSE I: 0.25322, II: 0.25227) and increasing model robustness. These results demonstrated data-driven predictive modeling potentially leading to bioprocess’ model-based control and optimisation.},
  doi       = {10.1007/s00449-020-02360-2},
  refid     = {Tsipa2020},
  timestamp = {2020-11-09},
  url       = {https://doi.org/10.1007/s00449-020-02360-2},
}

@Article{Kuritz2020.03.30.015909,
  author       = {Kuritz, Karsten and Bonny, Alain R and Fonseca, Jo{\~a}o Pedro and Allg{\"o}wer, Frank},
  journal      = {bioRxiv},
  title        = {PDE-constrained optimization for estimating population dynamics over cell cycle from static single cell measurements},
  year         = {2020},
  abstract     = {Motivation Understanding how cell cycle responds and adapts dynamically to a broad range of stresses and changes in the cellular environment is crucial for the treatment of various pathologies, including cancer. However, measuring changes in cell cycle progression is experimentally challenging, and model inference computationally expensive.Results Here, we introduce a computational framework that allows the inference of changes in cell cycle progression from static single-cell measurements. We modeled population dynamics with partial differential equations (PDE), and derive parameter gradients to estimate time- and cell cycle position-dependent progression changes efficiently. Additionally, we show that computing parameter sensitivities for the optimization problem by solving a system of PDEs is computationally feasible and allows efficient and exact estimation of parameters. We showcase our framework by estimating the changes in cell cycle progression in K562 cells treated with Nocodazole and identify an arrest in M-phase transition that matches the expected behavior of microtubule polymerization inhibition.Conclusions Our results have two major implications: First, this framework can be scaled to high-throughput compound screens, providing a fast, stable, and efficient protocol to generate new insights into changes in cell cycle progression. Second, knowledge of the cell cycle stage- and time-dependent progression function allows transformation from pseudotime to real-time thereby enabling real-time analysis of molecular rates in response to treatments.Availability MAPiT toolbox (Karsten Kuritz 2020) is available at github: https://github.com/karstenkuritz/MAPiT.},
  doi          = {10.1101/2020.03.30.015909},
  elocation-id = {2020.03.30.015909},
  eprint       = {https://www.biorxiv.org/content/early/2020/03/31/2020.03.30.015909.full.pdf},
  publisher    = {Cold Spring Harbor Laboratory},
  timestamp    = {2020-11-09},
  url          = {https://www.biorxiv.org/content/early/2020/03/31/2020.03.30.015909},
}

@Article{PittGom2018,
  author    = {Jake Alan Pitt and Lucian Gomoescu and Constantinos C. Pantelides and Benoît Chachuat and Julio R. Banga},
  journal   = {IFAC-PapersOnLine},
  title     = {Critical Assessment of Parameter Estimation Methods in Models of Biological Oscillators},
  year      = {2018},
  issn      = {2405-8963},
  note      = {7th Conference on Foundation of Systems Biology in Engineering FOSBE 2018},
  number    = {19},
  pages     = {72 - 75},
  volume    = {51},
  abstract  = {Many biological systems exhibit oscillations in relation to key physiological or cellular functions, such as circadian rhythms, mitosis and DNA synthesis. Mathematical modelling provides a powerful approach to analysing these biosystems. Applying parameter estimation methods to calibrate these models can prove a very challenging task in practice, due to the presence of local solutions, lack of identifiability, and risk of overfitting. This paper presents a comparison of three state-of-the-art methods: frequentist, Bayesian and set-membership estimation. We use the Fitzhugh-Nagumo model with synthetic data as a case study. The computational performance and robustness of these methods is discussed, with a particular focus on their predictive capability using cross-validation.},
  doi       = {10.1016/j.ifacol.2018.09.040},
  keywords  = {biological oscillators, model calibration, regularisation, overfitting, identifiability, frequentist estimation, Bayesian estimation, set-membership estimation},
  timestamp = {2020-11-09},
  url       = {http://www.sciencedirect.com/science/article/pii/S2405896318316999},
}

@MastersThesis{Watanabe2019,
  author    = {Watanabe, Simon Berglund},
  school    = {Chalmers University of Technology / Department of Mathematical Sciences},
  title     = {Identifiability of parameters in PBPK models},
  year      = {2019},
  abstract  = {Inthefieldofpharmacologics,physiologically-basedpharmacokinetic(PBPK)models can be used for predicting the pharmacokinetics of a drug compound in the body. These models are often a system of ordinary differential equations (ODEs) that describe the transport of a drug between different compartments of the body. The models depend on several parameters, some of which cannot be measured experimentally and instead these parameters are often estimated from experimental data using maximum likelihood. However, in many applications in systems biology, estimates will suffer from unidentifiability issues, meaning that well-determined estimates cannot be inferred from the data [17]. Thisproblemcomesintwoforms,structuralunidentifiabilityandpracticalunidentifiability,bothofwhichcanbeanalyzedwiththeprofilelikelihoodmethoddeveloped by Raue et.al [14]. The profile likelihood method is a numerical method for calculating likelihood-based confidence intervals of the parameters, which are then used to assess identifiability. In this project the profile likelihood method is implemented in MATLAB and used to perform identifiability analysis on key model parameters for three PBPK models using simulated data. Thus, the results of this project are both a showcase of the profilelikelihoodmethodandananalysisoftheidentifiabilityofparametersinsome specific models used for pulmonary drug delivery. The results indicate that if very precise measurements could be taken then all parameters considered would be identifiable. When a reasonable measurement error is applied on the simulated data the same is not true. Some parameters, such as the in-vivo pulmonary permeability and deposition fraction will remain identifiable, but most other parameters will suffer from practical unidentifiability. With a reasonable measurement error the identifiability of most model parameters will also be dependent on the particular error realization. To address these issues, additional dataisconsideredbyobservinghowtheuncertaintyinparameterestimatesimpacts observables. Bythismethod(alsosuggestedbyRaue[14])additionalmeasurements are introduced in an effective manner to potentially resolve unidentifiabilities. Keywords: structural unidentifiability, practical unidentifiability, pulmonary drug delivery, maximum likelihood estimation.},
  timestamp = {2020-11-09},
  url       = {https://hdl.handle.net/20.500.12380/256855},
}

@Article{ErdemBen2020,
  author       = {Erdem, Cemal and Bensman, Ethan M. and Mutsuddy, Arnab and Saint-Antoine, Michael M. and Bouhaddou, Mehdi and Blake, Robert C. and Dodd, Will and Gross, Sean M. and Heiser, Laura M. and Feltus, F. Alex and Birtwistle, Marc R.},
  journal      = {bioRxiv},
  title        = {A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models},
  year         = {2020},
  abstract     = {The current era of big biomedical data accumulation and availability brings data integration opportunities for leveraging its totality to make new discoveries and/or clinically predictive models. Black-box statistical and machine learning methods are powerful for such integration, but often cannot provide mechanistic reasoning, particularly on the single-cell level. While single-cell mechanistic models clearly enable such reasoning, they are predominantly {\textquotedblleft}small-scale{\textquotedblright}, and struggle with the scalability and reusability required for meaningful data integration. Here, we present an open-source pipeline for scalable, single-cell mechanistic modeling from simple, annotated input files that can serve as a foundation for mechanistic data integration. As a test case, we convert one of the largest existing single-cell mechanistic models to this format, demonstrating robustness and reproducibility of the approach. We show that the model cell line context can be changed with simple replacement of input file parameter values. We next use this new model to test alternative mechanistic hypotheses for the experimental observations that interferon-gamma (IFNG) inhibits epidermal growth factor (EGF)-induced cell proliferation. Model- based analysis suggested, and experiments support that these observations are better explained by IFNG-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFNG-induced upregulation of p21 expression. Overall, this new pipeline enables large-scale, single-cell, and mechanistically-transparent modeling as a data integration modality complementary to machine learning.Competing Interest StatementThe authors have declared no competing interest.},
  doi          = {10.1101/2020.11.09.373407},
  elocation-id = {2020.11.09.373407},
  eprint       = {https://www.biorxiv.org/content/early/2020/11/10/2020.11.09.373407.full.pdf},
  publisher    = {Cold Spring Harbor Laboratory},
  timestamp    = {2020-11-16},
  url          = {https://www.biorxiv.org/content/early/2020/11/10/2020.11.09.373407},
}

@Article{StaedterSch2021,
  author    = {Städter, Philipp and Schälte, Yannik and Schmiester, Leonard and Hasenauer, Jan and Stapor, Paul L.},
  journal   = {Scientific Reports},
  title     = {Benchmarking of numerical integration methods for ODE models of biological systems},
  year      = {2021},
  issn      = {2045-2322},
  number    = {1},
  pages     = {2696},
  volume    = {11},
  abstract  = {Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.},
  doi       = {10.1038/s41598-021-82196-2},
  refid     = {Städter2021},
  timestamp = {2021-02-19},
  url       = {https://doi.org/10.1038/s41598-021-82196-2},
}

@Article{Schmiester2021.02.06.430039,
  author       = {Schmiester, Leonard and Weindl, Daniel and Hasenauer, Jan},
  journal      = {bioRxiv},
  title        = {Efficient gradient-based parameter estimation for dynamic models using qualitative data},
  year         = {2021},
  abstract     = {Motivation Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence.Results Here, we propose an efficient and reliable framework for estimating the parameters of ordinary differential equation models from qualitative data. In this framework, we derive a semi-analytical algorithm for gradient calculation of the optimal scaling method developed for qualitative data. This enables the use of efficient gradient-based optimization algorithms. We demonstrate that the use of gradient information improves performance of optimization and uncertainty quantification on several application examples. On average, we achieve a speedup of more than one order of magnitude compared to gradient-free optimization. Additionally, in some examples, the gradient-based approach yields substantially improved objective function values and quality of the fits. Accordingly, the proposed framework substantially improves the parameterization of models from qualitative data.Availability The proposed approach is implemented in the open-source Python Parameter EStimation TOolbox (pyPESTO). All application examples and code to reproduce this study are available at https://doi.org/10.5281/zenodo.4507613.Competing Interest StatementThe authors have declared no competing interest.},
  doi          = {10.1101/2021.02.06.430039},
  elocation-id = {2021.02.06.430039},
  eprint       = {https://www.biorxiv.org/content/early/2021/02/08/2021.02.06.430039.full.pdf},
  publisher    = {Cold Spring Harbor Laboratory},
  timestamp    = {2021-02-19},
  url          = {https://www.biorxiv.org/content/early/2021/02/08/2021.02.06.430039},
}

@Article{RaimundezDud2021,
  author    = {Elba Raimúndez and Erika Dudkin and Jakob Vanhoefer and Emad Alamoudi and Simon Merkt and Lara Fuhrmann and Fan Bai and Jan Hasenauer},
  journal   = {Epidemics},
  title     = {COVID-19 outbreak in Wuhan demonstrates the limitations of publicly available case numbers for epidemiological modeling},
  year      = {2021},
  issn      = {1755-4365},
  pages     = {100439},
  volume    = {34},
  abstract  = {Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.},
  doi       = {10.1016/j.epidem.2021.100439},
  keywords  = {Compartment model, SEIRD, Parameter estimation, Model selection, Uncertainty analysis},
  timestamp = {2021-02-19},
  url       = {https://www.sciencedirect.com/science/article/pii/S1755436521000037},
}

@Article{vanRosmalenSmi2021,
  author   = {R.P. {van Rosmalen} and R.W. Smith and V.A.P. {Martins dos Santos} and C. Fleck and M. Suarez-Diez},
  journal  = {Metabolic Engineering},
  title    = {Model reduction of genome-scale metabolic models as a basis for targeted kinetic models},
  year     = {2021},
  issn     = {1096-7176},
  pages    = {74-84},
  volume   = {64},
  abstract = {Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.},
  doi      = {10.1016/j.ymben.2021.01.008},
  keywords = {Metabolic engineering, DBTL cycle, Model reduction, Model optimisation, Model-driven design, Synthetic biology},
  url      = {https://www.sciencedirect.com/science/article/pii/S1096717621000161},
}

@Article{StenPod2021,
  author       = {Sten, Sebastian and Pod{\'e}us, Henrik and Sundqvist, Nicolas and Elinder, Fredrik and Engstr{\"o}m, Maria and Cedersund, Gunnar},
  journal      = {bioRxiv},
  title        = {A multi-data based quantitative model for the neurovascular coupling in the brain},
  year         = {2021},
  abstract     = {The neurovascular coupling (NVC) forms the foundation for functional imaging techniques of the brain, since NVC connects neural activity with observable hemodynamic changes. Many aspects of the NVC have been studied both experimentally and with mathematical models: various combinations of blood volume and flow, electrical activity, oxygen saturation measures, blood oxygenation level-dependent (BOLD) response, and optogenetics have been measured and modeled in rodents, primates, or humans. We now present a first inter-connected mathematical model that describes all such data types simultaneously. The model can predict independent validation data not used for training. Using simulations, we show for example how complex bimodal behaviors appear upon stimulation. These simulations thus demonstrate how our new quantitative model, incorporating most of the core aspects of the NVC, can be used to mechanistically explain each of its constituent datasets.Competing Interest StatementThe authors have declared no competing interest.},
  doi          = {10.1101/2021.03.25.437053},
  elocation-id = {2021.03.25.437053},
  eprint       = {https://www.biorxiv.org/content/early/2021/03/26/2021.03.25.437053.full.pdf},
  publisher    = {Cold Spring Harbor Laboratory},
  url          = {https://www.biorxiv.org/content/early/2021/03/26/2021.03.25.437053},
}

@PhdThesis{Gaspari2021,
  author  = {Gaspari, Erika},
  school  = {Wageningen University},
  title   = {Model-driven design of Mycoplasma as a vaccine chassis},
  year    = {2021},
  address = {Wageningen},
  comment = {WU thesis 7758 Includes bibliographical references. - With summaries in English, Italian and Spanish
10.18174/539593},
  doi     = {10.18174/539593},
  issn    = {9789463956864},
  url     = {https://edepot.wur.nl/539593},
}

@Article{VanhoeferMat2021,
  author    = {Jakob Vanhoefer and Marta R. A. Matos and Dilan Pathirana and Yannik Schälte and Jan Hasenauer},
  journal   = {Journal of Open Source Software},
  title     = {yaml2sbml: Human-readable and -writable specification of {ODE} models and their conversion to {SBML}},
  year      = {2021},
  number    = {61},
  pages     = {3215},
  volume    = {6},
  doi       = {10.21105/joss.03215},
  publisher = {The Open Journal},
  url       = {https://doi.org/10.21105/joss.03215},
}

@Article{BastBuc2021,
  author   = {Lisa Bast and Michèle C. Buck and Judith S. Hecker and Robert A.J. Oostendorp and Katharina S. Götze and Carsten Marr},
  journal  = {iScience},
  title    = {Computational modeling of stem and progenitor cell kinetics identifies plausible hematopoietic lineage hierarchies},
  year     = {2021},
  issn     = {2589-0042},
  number   = {2},
  pages    = {102120},
  volume   = {24},
  abstract = {Summary
Classically, hematopoietic stem cell (HSC) differentiation is assumed to occur via progenitor compartments of decreasing plasticity and increasing maturity in a specific, hierarchical manner. The classical hierarchy has been challenged in the past by alternative differentiation pathways. We abstracted experimental evidence into 10 differentiation hierarchies, each comprising 7 cell type compartments. By fitting ordinary differential equation models with realistic waiting time distributions to time-resolved data of differentiating HSCs from 10 healthy human donors, we identified plausible lineage hierarchies and rejected others. We found that, for most donors, the classical model of hematopoiesis is preferred. Surprisingly, multipotent lymphoid progenitor differentiation into granulocyte-monocyte progenitors is plausible in 90% of samples. An in silico analysis confirmed that, even for strong noise, the classical model can be identified robustly. Our computational approach infers differentiation hierarchies in a personalized fashion and can be used to gain insights into kinetic alterations of diseased hematopoiesis.},
  doi      = {10.1016/j.isci.2021.102120},
  keywords = {stem cells research, in silico biology, systems biology},
  url      = {https://www.sciencedirect.com/science/article/pii/S2589004221000882},
}

@Article{TomasoniPar2021,
  author   = {Tomasoni, Danilo and Paris, Alessio and Giampiccolo, Stefano and Reali, Federico and Simoni, Giulia and Marchetti, Luca and Kaddi, Chanchala and Neves-Zaph, Susana and Priami, Corrado and Azer, Karim and Lombardo, Rosario},
  journal  = {Communications Biology},
  title    = {{QSPcc} reduces bottlenecks in computational model simulations},
  year     = {2021},
  issn     = {2399-3642},
  number   = {1},
  pages    = {1022},
  volume   = {4},
  abstract = {Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances.},
  doi      = {10.1038/s42003-021-02553-9},
  refid    = {Tomasoni2021},
  url      = {https://doi.org/10.1038/s42003-021-02553-9},
}

@Misc{MaierHar2020,
  author        = {Corinna Maier and Niklas Hartung and Charlotte Kloft and Wilhelm Huisinga and Jana de Wiljes},
  title         = {Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology},
  year          = {2020},
  archiveprefix = {arXiv},
  eprint        = {2006.01061},
  primaryclass  = {stat.ML},
}

@phdthesis{Maier2021,
  author      = {Corinna Maier},
  title       = {Bayesian data assimilation and reinforcement learning for model-informed precision dosing in oncology},
  type        = {doctoralthesis},
  pages       = {x, 138},
  school      = {Universit{\"a}t Potsdam},
  doi       = {10.25932/publishup-51587},
  year        = {2021},
}

@Article{GudinaAli2021,
  author   = {Esayas Kebede Gudina and Solomon Ali and Eyob Girma and Addisu Gize and Birhanemeskel Tegene and Gadissa Bedada Hundie and Wondewosen Tsegaye Sime and Rozina Ambachew and Alganesh Gebreyohanns and Mahteme Bekele and Abhishek Bakuli and Kira Elsbernd and Simon Merkt and Lorenzo Contento and Michael Hoelscher and Jan Hasenauer and Andreas Wieser and Arne Kroidl},
  journal  = {The Lancet Global Health},
  title    = {{Seroepidemiology and model-based prediction of SARS-CoV-2 in Ethiopia: longitudinal cohort study among front-line hospital workers and communities}},
  year     = {2021},
  issn     = {2214-109X},
  number   = {11},
  pages    = {e1517-e1527},
  volume   = {9},
  abstract = {Summary
Background
Over 1 year since the first reported case, the true COVID-19 burden in Ethiopia remains unknown due to insufficient surveillance. We aimed to investigate the seroepidemiology of SARS-CoV-2 among front-line hospital workers and communities in Ethiopia.
Methods
We did a population-based, longitudinal cohort study at two tertiary teaching hospitals involving hospital workers, rural residents, and urban communities in Jimma and Addis Ababa. Hospital workers were recruited at both hospitals, and community participants were recruited by convenience sampling including urban metropolitan settings, urban and semi-urban settings, and rural communities. Participants were eligible if they were aged 18 years or older, had provided written informed consent, and were willing to provide blood samples by venepuncture. Only one participant per household was recruited. Serology was done with Elecsys anti-SARS-CoV-2 anti-nucleocapsid assay in three consecutive rounds, with a mean interval of 6 weeks between tests, to obtain seroprevalence and incidence estimates within the cohorts.
Findings
Between Aug 5, 2020, and April 10, 2021, we did three survey rounds with a total of 1104 hospital workers and 1229 community residents participating. SARS-CoV-2 seroprevalence among hospital workers increased strongly during the study period: in Addis Ababa, it increased from 10·9% (95% credible interval [CrI] 8·3–13·8) in August, 2020, to 53·7% (44·8–62·5) in February, 2021, with an incidence rate of 2223 per 100 000 person-weeks (95% CI 1785–2696); in Jimma Town, it increased from 30·8% (95% CrI 26·9–34·8) in November, 2020, to 56·1% (51·1–61·1) in February, 2021, with an incidence rate of 3810 per 100 000 person-weeks (95% CI 3149–4540). Among urban communities, an almost 40% increase in seroprevalence was observed in early 2021, with incidence rates of 1622 per 100 000 person-weeks (1004–2429) in Jimma Town and 4646 per 100 000 person-weeks (2797–7255) in Addis Ababa. Seroprevalence in rural communities increased from 18·0% (95% CrI 13·5–23·2) in November, 2020, to 31·0% (22·3–40·3) in March, 2021.
Interpretation
SARS-CoV-2 spread in Ethiopia has been highly dynamic among hospital worker and urban communities. We can speculate that the greatest wave of SARS-CoV-2 infections is currently evolving in rural Ethiopia, and thus requires focused attention regarding health-care burden and disease prevention.
Funding
Bavarian State Ministry of Sciences, Research, and the Arts; Germany Ministry of Education and Research; EU Horizon 2020 programme; Deutsche Forschungsgemeinschaft; and Volkswagenstiftung.},
  doi      = {10.1016/S2214-109X(21)00386-7},
  url      = {https://www.sciencedirect.com/science/article/pii/S2214109X21003867},
}

@Article{StaporSch2022,
  author   = {Stapor, Paul and Schmiester, Leonard and Wierling, Christoph and Merkt, Simon and Pathirana, Dilan and Lange, Bodo M. H. and Weindl, Daniel and Hasenauer, Jan},
  journal  = {Nature Communications},
  title    = {{Mini-batch optimization enables training of ODE models on large-scale datasets}},
  year     = {2022},
  issn     = {2041-1723},
  number   = {1},
  pages    = {34},
  volume   = {13},
  abstract = {Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.},
  doi      = {10.1038/s41467-021-27374-6},
  refid    = {Stapor2022},
  url      = {https://doi.org/10.1038/s41467-021-27374-6},
}

@Article{SchmuckerFar2022,
  author    = {Schmucker, Robin and Farina, Gabriele and Faeder, James and Fröhlich, Fabian and Saglam, Ali Sinan and Sandholm, Tuomas},
  journal   = {PLOS Computational Biology},
  title     = {Combination treatment optimization using a pan-cancer pathway model},
  year      = {2022},
  month     = {12},
  number    = {12},
  pages     = {1-22},
  volume    = {17},
  abstract  = {The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.},
  doi       = {10.1371/journal.pcbi.1009689},
  publisher = {Public Library of Science},
  url       = {https://doi.org/10.1371/journal.pcbi.1009689},
}

@Article{SchuhLoo2020,
  author    = {Schuh, Lea and Loos, Carolin and Pokrovsky, Daniil and Imhof, Axel and Rupp, Ralph A. W. and Marr, Carsten},
  journal   = {Cell Systems},
  title     = {H4K20 Methylation Is Differently Regulated by Dilution and Demethylation in Proliferating and Cell-Cycle-Arrested Xenopus Embryos},
  year      = {2020},
  issn      = {2405-4712},
  month     = dec,
  number    = {6},
  pages     = {653--662.e8},
  volume    = {11},
  abstract  = {DNA replication during cell division leads to dilution of histone modifications and can thus affect chromatin-mediated gene regulation, raising the question of how the cell-cycle shapes the histone modification landscape, particularly during embryogenesis. We tackled this problem by manipulating the cell cycle during early Xenopus laevis embryogenesis and analyzing in vivo histone H4K20 methylation kinetics. The global distribution of un-, mono-, di-, and tri-methylated histone H4K20 was measured by mass spectrometry in normal and cell-cycle-arrested embryos over time. Using multi-start maximum likelihood optimization and quantitative model selection, we found that three specific biological methylation rate constants were required to explain the measured H4K20 methylation state kinetics. While demethylation is essential for regulating H4K20 methylation kinetics in non-cycling cells, demethylation is very likely dispensable in rapidly dividing cells of early embryos, suggesting that cell-cycle-mediated dilution of H4K20 methylation is an essential regulatory component for shaping its epigenetic landscape during early development.A record of this paper?s transparent peer review process is included in the Supplemental Information.},
  comment   = {doi: 10.1016/j.cels.2020.11.003},
  doi       = {10.1016/j.cels.2020.11.003},
  publisher = {Elsevier},
  url       = {https://doi.org/10.1016/j.cels.2020.11.003},
}

@Article{VillaverdePat2021,
  author   = {Villaverde, Alejandro F and Pathirana, Dilan and Fröhlich, Fabian and Hasenauer, Jan and Banga, Julio R},
  journal  = {Briefings in Bioinformatics},
  title    = {{A protocol for dynamic model calibration}},
  year     = {2021},
  issn     = {1477-4054},
  month    = {10},
  note     = {bbab387},
  abstract = {{Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.}},
  doi      = {10.1093/bib/bbab387},
  eprint   = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab387/40534209/bbab387.pdf},
  url      = {https://doi.org/10.1093/bib/bbab387},
}

@Article{SchaelteHas2020,
  author   = {Schälte, Yannik and Hasenauer, Jan},
  journal  = {Bioinformatics},
  title    = {{Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation}},
  year     = {2020},
  issn     = {1367-4803},
  month    = {07},
  number   = {Supplement_1},
  pages    = {i551-i559},
  volume   = {36},
  abstract = {{Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, as ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC.We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling-based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes and stochastically interacting agents, and noise models including normal, Laplace and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications.The developed algorithms are made publicly available as part of the open-source python toolbox pyABC (https://github.com/icb-dcm/pyabc).Supplementary data are available at Bioinformatics online.}},
  doi      = {10.1093/bioinformatics/btaa397},
  eprint   = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_1/i551/33488985/btaa397.pdf},
  url      = {https://doi.org/10.1093/bioinformatics/btaa397},
}

@Article{AdlungSta2021,
  author   = {Lorenz Adlung and Paul Stapor and Christian Tönsing and Leonard Schmiester and Luisa E. Schwarzmüller and Lena Postawa and Dantong Wang and Jens Timmer and Ursula Klingmüller and Jan Hasenauer and Marcel Schilling},
  journal  = {Cell Reports},
  title    = {Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells},
  year     = {2021},
  issn     = {2211-1247},
  number   = {6},
  pages    = {109507},
  volume   = {36},
  abstract = {Summary
Survival or apoptosis is a binary decision in individual cells. However, at the cell-population level, a graded increase in survival of colony-forming unit-erythroid (CFU-E) cells is observed upon stimulation with erythropoietin (Epo). To identify components of Janus kinase 2/signal transducer and activator of transcription 5 (JAK2/STAT5) signal transduction that contribute to the graded population response, we extended a cell-population-level model calibrated with experimental data to study the behavior in single cells. The single-cell model shows that the high cell-to-cell variability in nuclear phosphorylated STAT5 is caused by variability in the amount of Epo receptor (EpoR):JAK2 complexes and of SHP1, as well as the extent of nuclear import because of the large variance in the cytoplasmic volume of CFU-E cells. 24–118 pSTAT5 molecules in the nucleus for 120 min are sufficient to ensure cell survival. Thus, variability in membrane-associated processes is sufficient to convert a switch-like behavior at the single-cell level to a graded population-level response.},
  doi      = {10.1016/j.celrep.2021.109507},
  keywords = {single-cell modeling, JAK/STAT, signal transduction, Epo, heterogeneity, cell fate decision, apoptosis, CFU-E cells, transcription factor, mathematical modeling},
  url      = {https://www.sciencedirect.com/science/article/pii/S2211124721009372},
}

@Article{SluijsMaa2022,
  author   = {van Sluijs, Bob and Maas, Roel J. M. and van der Linden, Ardjan J. and de Greef, Tom F. A. and Huck, Wilhelm T. S.},
  journal  = {Nature Communications},
  title    = {A microfluidic optimal experimental design platform for forward design of cell-free genetic networks},
  year     = {2022},
  issn     = {2041-1723},
  number   = {1},
  pages    = {3626},
  volume   = {13},
  abstract = {Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.},
  doi      = {10.1038/s41467-022-31306-3},
  refid    = {van Sluijs2022},
}

@Article{VillaverdeRai2022,
  author  = {Villaverde, Alejandro F. and Raimúndez, Elba and Hasenauer, Jan and Banga, Julio R.},
  journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  title   = {Assessment of Prediction Uncertainty Quantification Methods in Systems Biology},
  year    = {2022},
  pages   = {1-12},
  doi     = {10.1109/TCBB.2022.3213914},
}

@Article{MishraWan2023,
  author   = {Shekhar Mishra and Ziyu Wang and Michael J. Volk and Huimin Zhao},
  journal  = {Metabolic Engineering},
  title    = {Design and application of a kinetic model of lipid metabolism in Saccharomyces cerevisiae},
  year     = {2023},
  issn     = {1096-7176},
  pages    = {12-18},
  volume   = {75},
  abstract = {Lipid biosynthesis plays a vital role in living cells and has been increasingly engineered to overproduce various lipid-based chemicals. However, owing to the tightly constrained and interconnected nature of lipid biosynthesis, both understanding and engineering of lipid metabolism remain challenging, even with the help of mathematical models. Here we report the development of a kinetic metabolic model of lipid metabolism in Saccharomyces cerevisiae that integrates fatty acid biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism, storage lipids, lumped sterol synthesis, and the synthesis and transport of relevant target-chemicals, such as fatty acids and fatty alcohols. The model was trained on lipidomic data of a reference S. cerevisiae strain, single knockout mutants, and lipid overproduction strains reported in literature. The model was used to design mutants for fatty alcohol overproduction and the lipidomic analysis of the resultant mutant strains coupled with model-guided hypothesis led to discovery of a futile cycle in the triacylglycerol biosynthesis pathway. In addition, the model was used to explain successful and unsuccessful mutant designs in metabolic engineering literature. Thus, this kinetic model of lipid metabolism can not only enable the discovery of new phenomenon in lipid metabolism but also the engineering of mutant strains for overproduction of lipids.},
  doi      = {10.1016/j.ymben.2022.11.003},
  keywords = {Lipid metabolism, Kinetic model, Free fatty acid, Fatty alcohol},
  url      = {https://www.sciencedirect.com/science/article/pii/S1096717622001380},
}

@Article{MassonisVil2022,
  author   = {Massonis, Gemma and Villaverde, Alejandro F and Banga, Julio R},
  journal  = {Bioinformatics},
  title    = {{Improving dynamic predictions with ensembles of observable models}},
  year     = {2022},
  issn     = {1367-4803},
  month    = {11},
  note     = {btac755},
  abstract = {{Dynamic mechanistic modelling in systems biology has been hampered by the complexity and variability associated with the underlying interactions, and by uncertain and sparse experimental measurements. Ensemble modelling, a concept initially developed in statistical mechanics, has been introduced in biological applications with the aim of mitigating those issues. Ensemble modelling uses a collection of different models compatible with the observed data to describe the phenomena of interest. However, since systems biology models often suffer from lack of identifiability and observability, ensembles of models are particularly unreliable when predicting non-observable states.We present a strategy to assess and improve the reliability of a class of model ensembles. In particular, we consider kinetic models described using ordinary differential equations (ODEs) with a fixed structure. Our approach builds an ensemble with a selection of the parameter vectors found when performing parameter estimation with a global optimization metaheuristic. This technique enforces diversity during the sampling of parameter space and it can quantify the uncertainty in the predictions of state trajectories. We couple this strategy with structural identifiability and observability analysis, and when these tests detect possible prediction issues we obtain model reparameterizations that surmount them. The end result is an ensemble of models with the ability to predict the internal dynamics of a biological process. We demonstrate our approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway.The code that implements the methodology and reproduces the results is available at https://doi.org/10.5281/zenodo.6782638Supplementary data are available at Bioinformatics online.}},
  doi      = {10.1093/bioinformatics/btac755},
  eprint   = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac755/47214747/btac755.pdf},
  url      = {https://doi.org/10.1093/bioinformatics/btac755},
}

@Article{AlbadryHoe2022,
  author   = {Albadry, Mohamed and Höpfl, Sebastian and Ehteshamzad, Nadia and König, Matthias and Böttcher, Michael and Neumann, Jasna and Lupp, Amelie and Dirsch, Olaf and Radde, Nicole and Christ, Bruno and Christ, Madlen and Schwen, Lars Ole and Laue, Hendrik and Klopfleisch, Robert and Dahmen, Uta},
  journal  = {Scientific Reports},
  title    = {Periportal steatosis in mice affects distinct parameters of pericentral drug metabolism},
  year     = {2022},
  issn     = {2045-2322},
  number   = {1},
  pages    = {21825},
  volume   = {12},
  abstract = {Little is known about the impact of morphological disorders in distinct zones on metabolic zonation. It was described recently that periportal fibrosis did affect the expression of CYP proteins, a set of pericentrally located drug-metabolizing enzymes. Here, we investigated whether periportal steatosis might have a similar effect. Periportal steatosis was induced in C57BL6/J mice by feeding a high-fat diet with low methionine/choline content for either two or four weeks. Steatosis severity was quantified using image analysis. Triglycerides and CYP activity were quantified in photometric or fluorometric assay. The distribution of CYP3A4, CYP1A2, CYP2D6, and CYP2E1 was visualized by immunohistochemistry. Pharmacokinetic parameters of test drugs were determined after injecting a drug cocktail (caffeine, codeine, and midazolam). The dietary model resulted in moderate to severe mixed steatosis confined to periportal and midzonal areas. Periportal steatosis did not affect the zonal distribution of CYP expression but the activity of selected CYPs was associated with steatosis severity. Caffeine elimination was accelerated by microvesicular steatosis, whereas midazolam elimination was delayed in macrovesicular steatosis. In summary, periportal steatosis affected parameters of pericentrally located drug metabolism. This observation calls for further investigations of the highly complex interrelationship between steatosis and drug metabolism and underlying signaling mechanisms.},
  doi      = {10.1038/s41598-022-26483-6},
  refid    = {Albadry2022},
}

@Article{SundqvistSte2022,
  author    = {Sundqvist, Nicolas and Sten, Sebastian and Thompson, Peter and Andersson, Benjamin Jan and Engström, Maria and Cedersund, Gunnar},
  journal   = {PLOS Computational Biology},
  title     = {Mechanistic model for human brain metabolism and its connection to the neurovascular coupling},
  year      = {2022},
  month     = {12},
  number    = {12},
  pages     = {1-24},
  volume    = {18},
  abstract  = {The neurovascular and neurometabolic couplings (NVC and NMC) connect cerebral activity, blood flow, and metabolism. This interconnection is used in for instance functional imaging, which analyses the blood-oxygen-dependent (BOLD) signal. The mechanisms underlying the NVC are complex, which warrants a model-based analysis of data. We have previously developed a mechanistically detailed model for the NVC, and others have proposed detailed models for cerebral metabolism. However, existing metabolic models are still not fully utilizing available magnetic resonance spectroscopy (MRS) data and are not connected to detailed models for NVC. Therefore, we herein present a new model that integrates mechanistic modelling of both MRS and BOLD data. The metabolic model covers central metabolism, using a minimal set of interactions, and can describe time-series data for glucose, lactate, aspartate, and glutamate, measured after visual stimuli. Statistical tests confirm that the model can describe both estimation data and predict independent validation data, not used for model training. The interconnected NVC model can simultaneously describe BOLD data and can be used to predict expected metabolic responses in experiments where metabolism has not been measured. This model is a step towards a useful and mechanistically detailed model for cerebral blood flow and metabolism, with potential applications in both basic research and clinical applications.},
  doi       = {10.1371/journal.pcbi.1010798},
  publisher = {Public Library of Science},
}

@Article{MeyerSoe2022,
  author   = {Kristian Meyer and Mikkel {Søes Ibsen} and Lisa Vetter-Joss and Ernst {Broberg Hansen} and Jens Abildskov},
  journal  = {Journal of Chromatography A},
  title    = {Industrial ion-exchange chromatography development using discontinuous Galerkin methods coupled with forward sensitivity analysis},
  year     = {2022},
  issn     = {0021-9673},
  pages    = {463741},
  abstract = {In this work, a discontinuous Galerkin method coupled with forward sensitivity analysis (DG-FSA) is presented. The DG-FSA method is used to reduce computational cost required for model-based ion-exchange chromatography development using industrial load samples. As an example, the design of an anion-exchange chromatography step is considered. This step is used to purify an experimental peptide product called Protein G from Novo Nordisk A/S (Bagsvrd, Denmark). The results demonstrate, that a fourth order DG-FSA method can reduce computational cost of inverse problems by a factor ×16 compared to a second (low) order DG-FSA method. Furthermore, the fourth-order DG-FSA method enable the computation of probability distributions of optimized processing conditions given uncertainty in model parameters or inputs. This analysis is not possible within a reasonable timeframe when applying the second (low) order DG-FSA method. The design procedure facilitates the optimization of the Protein G purification step. In an experimental validation run, the productivity is increased by 70% while sacrificing 4% yield at a similar purity constraint compared to an experiment with baseline performance.},
  doi      = {10.1016/j.chroma.2022.463741},
  keywords = {Discontinuous Galerkin method, Forward sensitivity analysis, Model calibration, Uncertainty quantification, Process optimization},
  url      = {https://www.sciencedirect.com/science/article/pii/S0021967322009323},
}

@Article{LakrisenkoSta2023,
  author       = {Lakrisenko, Polina and Stapor, Paul and Grein, Stephan and Paszkowski, Łukasz and Pathirana, Dilan and Fröhlich, Fabian and Lines, Glenn Terje and Weindl, Daniel and Hasenauer, Jan},
  journal      = {PLOS Computational Biology},
  title        = {Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks},
  year         = {2023},
  month        = {01},
  number       = {1},
  pages        = {1-19},
  volume       = {19},
  abstract     = {Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical.},
  creationdate = {2023-01-09T08:36:51},
  doi          = {10.1371/journal.pcbi.1010783},
  publisher    = {Public Library of Science},
  url          = {https://doi.org/10.1371/journal.pcbi.1010783},
}

@Article{ContentoCas2023,
  author           = {Lorenzo Contento and Noemi Castelletti and Elba Raimúndez and Ronan {Le Gleut} and Yannik Schälte and Paul Stapor and Ludwig Christian Hinske and Michael Hoelscher and Andreas Wieser and Katja Radon and Christiane Fuchs and Jan Hasenauer},
  journal          = {Epidemics},
  title            = {Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts},
  year             = {2023},
  issn             = {1755-4365},
  pages            = {100681},
  volume           = {43},
  abstract         = {Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.},
  creationdate     = {2023-04-15T07:59:57},
  doi              = {10.1016/j.epidem.2023.100681},
  keywords         = {Compartmental model, Parameter estimation, Uncertainty quantification, COVID-19},
  modificationdate = {2024-06-28T08:27:57},
  url              = {https://www.sciencedirect.com/science/article/pii/S1755436523000178},
}

@Article{FroehlichGer2023,
  author       = {Fröhlich, Fabian and Gerosa, Luca and Muhlich, Jeremy and Sorger, Peter K},
  journal      = {Molecular Systems Biology},
  title        = {Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance},
  year         = {2023},
  number       = {2},
  pages        = {e10988},
  volume       = {19},
  abstract     = {Abstract BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAFV600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAFV600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAFV600E-driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.},
  creationdate = {2023-04-15T08:11:24},
  doi          = {10.15252/msb.202210988},
  eprint       = {https://www.embopress.org/doi/pdf/10.15252/msb.202210988},
  keywords     = {allosteric interactions, drug resistance, kinetic modeling, MAPK pathway, rewiring},
  url          = {https://www.embopress.org/doi/abs/10.15252/msb.202210988},
}

@Article{FroehlichSor2022,
  author           = {Fröhlich, Fabian and Sorger, Peter K.},
  journal          = {PLOS Computational Biology},
  title            = {Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models},
  year             = {2022},
  month            = {07},
  number           = {7},
  pages            = {1-28},
  volume           = {18},
  abstract         = {Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using experimental data. Optimization-based calibration of ODE models on is often challenging, even for low-dimensional problems. Multiple hypotheses have been advanced to explain why biochemical model calibration is challenging, including non-identifiability of model parameters, but there are few comprehensive studies that test these hypotheses, likely because tools for performing such studies are also lacking. Nonetheless, reliable model calibration is essential for uncertainty analysis, model comparison, and biological interpretation. We implemented an established trust-region method as a modular Python framework (fides) to enable systematic comparison of different approaches to ODE model calibration involving a variety of Hessian approximation schemes. We evaluated fides on a recently developed corpus of biologically realistic benchmark problems for which real experimental data are available. Unexpectedly, we observed high variability in optimizer performance among different implementations of the same mathematical instructions (algorithms). Analysis of possible sources of poor optimizer performance identified limitations in the widely used Gauss-Newton, BFGS and SR1 Hessian approximation schemes. We addressed these drawbacks with a novel hybrid Hessian approximation scheme that enhances optimizer performance and outperforms existing hybrid approaches. When applied to the corpus of test models, we found that fides was on average more reliable and efficient than existing methods using a variety of criteria. We expect fides to be broadly useful for ODE constrained optimization problems in biochemical models and to be a foundation for future methods development.},
  creationdate     = {2023-04-15T08:12:41},
  doi              = {10.1371/journal.pcbi.1010322},
  modificationdate = {2024-02-23T18:10:55},
  publisher        = {Public Library of Science},
  url              = {https://doi.org/10.1371/journal.pcbi.1010322},
}

@Article{ErdemMut2022,
  author       = {Erdem, Cemal and Mutsuddy, Arnab and Bensman, Ethan M. and Dodd, William B. and Saint-Antoine, Michael M. and Bouhaddou, Mehdi and Blake, Robert C. and Gross, Sean M. and Heiser, Laura M. and Feltus, F. Alex and Birtwistle, Marc R.},
  journal      = {Nature Communications},
  title        = {A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling},
  year         = {2022},
  issn         = {2041-1723},
  number       = {1},
  pages        = {3555},
  volume       = {13},
  abstract     = {Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.},
  creationdate = {2023-04-15T08:13:58},
  doi          = {10.1038/s41467-022-31138-1},
  refid        = {Erdem2022},
  url          = {https://doi.org/10.1038/s41467-022-31138-1},
}

@Article{ContentoSta2023,
  author           = {Lorenzo Contento and Paul Stapor and Daniel Weindl and Jan Hasenauer},
  journal          = {bioRxiv},
  title            = {A more expressive spline representation for {SBML} models improves code generation performance in {AMICI}},
  year             = {2023},
  abstract         = {Spline interpolants are commonly used for discretizing and estimating functions in mathematical models. While splines can be encoded in the Systems Biology Markup Language (SBML) using piecewise functions, the resulting formulas are very complex and difficult to derive by hand. Tools to create such formulas exist but only deal with numeric data and thus cannot be used for function estimation. Similarly, simulation tools suffer from several limitations when handling splines. For example, in the AMICI library splines with large numbers of nodes lead to long model import times. We have developed a set of SBML annotations to mark assignment rules as spline formulas. These compact representations are human-readable and easy to edit, in contrast to the piecewise representation. Different boundary conditions and extrapolation methods can also be specified. By extending AMICI to create and recognize these annotations, model import can be sped up significantly. This allows practitioners to increase the expressivity of their models. While the performance improvement is limited to AMICI, our tools for creating spline formulas can be used for other tools as well and our syntax for compact spline representation may be a starting point for an SBML-native way to represent spline interpolants.Competing Interest StatementThe authors have declared no competing interest.},
  creationdate     = {2023-07-06T10:25:17},
  doi              = {10.1101/2023.06.29.547120},
  elocation-id     = {2023.06.29.547120},
  eprint           = {https://www.biorxiv.org/content/early/2023/07/01/2023.06.29.547120.full.pdf},
  modificationdate = {2023-07-06T10:25:30},
  publisher        = {Cold Spring Harbor Laboratory},
  url              = {https://www.biorxiv.org/content/early/2023/07/01/2023.06.29.547120},
}

@InBook{Froehlich2023,
  author           = {Fr{\"o}hlich, Fabian},
  editor           = {Nguyen, Lan K.},
  pages            = {59--86},
  publisher        = {Springer US},
  title            = {A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Models of Cellular Signal Transduction},
  year             = {2023},
  address          = {New York, NY},
  isbn             = {978-1-0716-3008-2},
  abstract         = {Aberrant signal transduction leads to complex diseases such as cancer. To rationally design treatment strategies with small molecule inhibitors, computational models have to be employed. Energy- and rule-based models allow the construction of mechanistic ordinary differential equation models based on structural insights. The detailed, energy-based description often generates large models, which are difficult to calibrate on experimental data. In this chapter, we provide a detailed, interactive protocol for the programmatic formulation and calibration of such large, energy- and rule-based models of cellular signal transduction based on an example model describing the action of RAF inhibitors on MAPK signaling. An interactive version of this chapter is available as Jupyter Notebook at github.com/FFroehlich/energy{\_}modeling{\_}chapter.},
  booktitle        = {Computational Modeling of Signaling Networks},
  creationdate     = {2023-07-06T10:31:07},
  doi              = {10.1007/978-1-0716-3008-2_3},
  modificationdate = {2023-07-06T10:36:35},
  url              = {https://doi.org/10.1007/978-1-0716-3008-2_3},
}

@Article{BuckBas2023,
  author           = {Michèle C. Buck and Lisa Bast and Judith S. Hecker and Jennifer Rivière and Maja Rothenberg-Thurley and Luisa Vogel and Dantong Wang and Immanuel Andrä and Fabian J. Theis and Florian Bassermann and Klaus H. Metzeler and Robert A.J. Oostendorp and Carsten Marr and Katharina S. Götze},
  journal          = {iScience},
  title            = {Progressive Disruption of Hematopoietic Architecture from Clonal Hematopoiesis to {MDS}},
  year             = {2023},
  issn             = {2589-0042},
  pages            = {107328},
  abstract         = {Summary
Clonal hematopoiesis of indeterminate potential (CHIP) describes the age-related acquisition of somatic mutations in hematopoietic stem/progenitor cells (HSPC) leading to clonal blood cell expansion. Although CHIP mutations drive myeloid malignancies like myelodysplastic syndromes (MDS) it is unknown if clonal expansion is attributable to changes in cell type kinetics, or involves reorganization of the hematopoietic hierarchy. Using computational modeling we analyzed differentiation and proliferation kinetics of cultured hematopoietic stem cells (HSC) from 8 healthy individuals, 7 CHIP, and 10 MDS patients. While the standard hematopoietic hierarchy explained HSPC kinetics in healthy samples, 57% of CHIP and 70% of MDS samples were best described with alternative hierarchies. Deregulated kinetics were found at various HSPC compartments with high inter-individual heterogeneity in CHIP and MDS, while altered HSC rates were most relevant in MDS. Quantifying kinetic heterogeneity in detail, we show that reorganization of the HSPC compartment is detectable in the premalignant CHIP state.},
  creationdate     = {2023-07-17T09:12:31},
  doi              = {10.1016/j.isci.2023.107328},
  keywords         = {clonal hematopoiesis, myelodysplastic syndrome, computational modeling, hematopoietic hierarchy},
  modificationdate = {2023-07-17T09:12:50},
  url              = {https://www.sciencedirect.com/science/article/pii/S2589004223014050},
}

@Article{TunedalVio2023,
  author           = {Tunedal, Kajsa and Viola, Federica and Garcia, Belén Casas and Bolger, Ann and Nyström, Fredrik H. and Östgren, Carl Johan and Engvall, Jan and Lundberg, Peter and Dyverfeldt, Petter and Carlhäll, Carl-Johan and Cedersund, Gunnar and Ebbers, Tino},
  journal          = {The Journal of Physiology},
  title            = {Haemodynamic effects of hypertension and type 2 diabetes: Insights from a {4D} flow {MRI-based} personalized cardiovascular mathematical model},
  year             = {2023},
  number           = {n/a},
  volume           = {n/a},
  abstract         = {Abstract Type 2 diabetes (T2D) and hypertension increase the risk of cardiovascular diseases mediated by whole-body changes to metabolism, cardiovascular structure and haemodynamics. The haemodynamic changes related to hypertension and T2D are complex and subject-specific, however, and not fully understood. We aimed to investigate the haemodynamic mechanisms in T2D and hypertension by comparing the haemodynamics between healthy controls and subjects with T2D, hypertension, or both. For all subjects, we combined 4D flow magnetic resonance imaging data, brachial blood pressure and a cardiovascular mathematical model to create a comprehensive subject-specific analysis of central haemodynamics. When comparing the subject-specific haemodynamic parameters between the four groups, the predominant haemodynamic difference is impaired left ventricular relaxation in subjects with both T2D and hypertension compared to subjects with only T2D, only hypertension and controls. The impaired relaxation indicates that, in this cohort, the long-term changes in haemodynamic load of co-existing T2D and hypertension cause diastolic dysfunction demonstrable at rest, whereas either disease on its own does not. However, through subject-specific predictions of impaired relaxation, we show that altered relaxation alone is not enough to explain the subject-specific and group-related differences; instead, a combination of parameters is affected in T2D and hypertension. These results confirm previous studies that reported more adverse effects from the combination of T2D and hypertension compared to either disease on its own. Furthermore, this shows the potential of personalized cardiovascular models in providing haemodynamic mechanistic insights and subject-specific predictions that could aid in the understanding and treatment planning of patients with T2D and hypertension. Key points The combination of 4D flow magnetic resonance imaging data and a cardiovascular mathematical model allows for a comprehensive analysis of subject-specific haemodynamic parameters that otherwise cannot be derived non-invasively. Using this combination, we show that diastolic dysfunction in subjects with both type 2 diabetes (T2D) and hypertension is the main group-level difference between controls, subjects with T2D, subjects with hypertension, and subjects with both T2D and hypertension. These results suggest that, in this relatively healthy population, the additional load of both hypertension and T2D affects the haemodynamic function of the left ventricle, whereas each disease on its own is not enough to cause significant effects under resting conditions. Finally, using the subject-specific model, we show that the haemodynamic effects of diastolic dysfunction alone are not sufficient to explain all the observed haemodynamic differences. Instead, additional subject-specific variations in cardiac and vascular function combine to explain the complex haemodynamics of subjects affected by hypertension and/or T2D.},
  creationdate     = {2023-07-27T08:14:11},
  doi              = {10.1113/JP284652},
  eprint           = {https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1113/JP284652},
  keywords         = {cardiovascular modelling, clinical data, computer modelling, diabetes, hemodynamic, hypertension, mathematical model},
  modificationdate = {2023-07-27T08:19:05},
  url              = {https://physoc.onlinelibrary.wiley.com/doi/abs/10.1113/JP284652},
}

@Article{RaimundezFed2023,
  author           = {Elba Raim{\'{u}}ndez and Michael Fedders and Jan Hasenauer},
  journal          = {{iScience}},
  title            = {Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes},
  year             = {2023},
  month            = {sep},
  pages            = {108083},
  creationdate     = {2023-10-04T14:12:00},
  doi              = {10.1016/j.isci.2023.108083},
  modificationdate = {2023-10-04T14:12:00},
  publisher        = {Elsevier {BV}},
}

@Article{Mendes2023,
  author           = {Mendes, Pedro},
  journal          = {Frontiers in Cell and Developmental Biology},
  title            = {Reproducibility and FAIR principles: the case of a segment polarity network model},
  year             = {2023},
  issn             = {2296-634X},
  volume           = {11},
  abstract         = {The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case. I analyze a computational model of the segment polarity network in Drosophila embryos published in 2000. Despite the high number of citations to this publication, 23 years later the model is barely accessible, and consequently not interoperable. Following the text of the original publication allowed successfully encoding the model for the open source software COPASI. Subsequently saving the model in the SBML format allowed it to be reused in other open source software packages. Submission of this SBML encoding of the model to the BioModels database enables its findability and accessibility. This demonstrates how the FAIR principles can be successfully enabled by using open source software, widely adopted standards, and public repositories, facilitating reproducibility and reuse of computational cell biology models that will outlive the specific software used.},
  creationdate     = {2023-10-28T19:05:54},
  doi              = {10.3389/fcell.2023.1201673},
  modificationdate = {2023-10-28T19:05:54},
  url              = {https://www.frontiersin.org/articles/10.3389/fcell.2023.1201673},
}

@Misc{HuckBal2023,
  author           = {Wilhelm Huck and Mathieu Baltussen and Thijs de Jong and Quentin Duez and William Robinson},
  title            = {Chemical reservoir computation in a self-organizing reaction network},
  year             = {2023},
  creationdate     = {2023-11-18T09:09:45},
  doi              = {10.21203/rs.3.rs-3487081/v1},
  modificationdate = {2023-11-18T09:10:08},
  publisher        = {Research Square Platform LLC},
}

@Article{LangPen2024,
  author           = {Lang, Paul F. and Penas, David R. and Banga, Julio R. and Weindl, Daniel and Novak, Bela},
  journal          = {PLOS Computational Biology},
  title            = {Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells},
  year             = {2024},
  month            = {01},
  number           = {1},
  pages            = {1-24},
  volume           = {20},
  abstract         = {The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability. We developed a comprehensive model structure of the full cell cycle that qualitatively explains the behaviour of human retinal pigment epithelial-1 cells. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. After optimisation with a parallel global optimisation metaheuristic we obtained excellent agreements between simulations and measurements. The PEtab specification of the optimisation problem facilitates reuse of model, data and/or optimisation results. Future perturbation experiments will improve parameter identifiability and allow for testing model predictive power. Such a predictive model may aid in drug discovery for cell cycle-related disorders.},
  creationdate     = {2024-01-24T20:02:16},
  doi              = {10.1371/journal.pcbi.1011151},
  modificationdate = {2024-02-23T18:10:08},
  publisher        = {Public Library of Science},
  url              = {https://doi.org/10.1371/journal.pcbi.1011151},
}

@Article{SluijsZho2024,
  author           = {van Sluijs, Bob and Zhou, Tao and Helwig, Britta and Baltussen, Mathieu G. and Nelissen, Frank H. T. and Heus, Hans A. and Huck, Wilhelm T. S.},
  journal          = {Nature Communications},
  title            = {Iterative design of training data to control intricate enzymatic reaction networks},
  year             = {2024},
  issn             = {2041-1723},
  month            = feb,
  number           = {1},
  volume           = {15},
  creationdate     = {2024-02-23T17:09:35},
  doi              = {10.1038/s41467-024-45886-9},
  modificationdate = {2024-02-23T17:09:35},
  publisher        = {Springer Science and Business Media LLC},
}

@Article{KissVen2024,
  author           = {Kiss, Anna E and Venkatasubramani, Anuroop V and Pathirana, Dilan and Krause, Silke and Sparr, Aline Campos and Hasenauer, Jan and Imhof, Axel and Müller, Marisa and Becker, Peter B},
  journal          = {Nucleic Acids Research},
  title            = {{Processivity and specificity of histone acetylation by the male-specific lethal complex}},
  year             = {2024},
  issn             = {0305-1048},
  month            = {02},
  pages            = {gkae123},
  abstract         = {{Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus. We used targeted mass spectrometry to examine the activity of MOF in the male-specific lethal core (4-MSL) complex on nucleosome array substrates. This complex is part of the Dosage Compensation Complex (DCC) that activates X-chromosomal genes in male Drosophila. During short reaction times, MOF acetylated H4K16 efficiently and with excellent selectivity. Upon longer incubation, the enzyme progressively acetylated lysines 12, 8 and 5, leading to a mixture of oligo-acetylated H4. Mathematical modeling suggests that MOF recognizes and acetylates H4K16 with high selectivity, but remains substrate-bound and continues to acetylate more N-terminal H4 lysines in a processive manner. The 4-MSL complex lacks non-coding roX RNA, a critical component of the DCC. Remarkably, addition of RNA to the reaction non-specifically suppressed H4 oligo-acetylation in favor of specific H4K16 acetylation. Because RNA destabilizes the MSL-nucleosome interaction in vitro we speculate that RNA accelerates enzyme-substrate turn-over in vivo, thus limiting the processivity of MOF, thereby increasing specific H4K16 acetylation.}},
  creationdate     = {2024-02-28T18:25:06},
  doi              = {10.1093/nar/gkae123},
  eprint           = {https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkae123/56756494/gkae123.pdf},
  modificationdate = {2024-02-28T18:25:06},
  url              = {https://doi.org/10.1093/nar/gkae123},
}

@Article{DoresicGre2024,
  author           = {Domagoj Dore{\v s}i{\'c} and Stephan Grein and Jan Hasenauer},
  journal          = {bioRxiv},
  title            = {Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data},
  year             = {2024},
  abstract         = {Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a non-linear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. Here, we propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO).Competing Interest StatementThe authors have declared no competing interest.},
  creationdate     = {2024-04-20T13:05:06},
  doi              = {10.1101/2024.01.26.577371},
  elocation-id     = {2024.01.26.577371},
  eprint           = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371.full.pdf},
  modificationdate = {2024-04-20T13:05:06},
  publisher        = {Cold Spring Harbor Laboratory},
  url              = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371},
}

@Article{MerktAli2024,
  author           = {Merkt, Simon and Ali, Solomon and Gudina, Esayas Kebede and Adissu, Wondimagegn and Gize, Addisu and Muenchhoff, Maximilian and Graf, Alexander and Krebs, Stefan and Elsbernd, Kira and Kisch, Rebecca and Betizazu, Sisay Sirgu and Fantahun, Bereket and Bekele, Delayehu and Rubio-Acero, Raquel and Gashaw, Mulatu and Girma, Eyob and Yilma, Daniel and Zeynudin, Ahmed and Paunovic, Ivana and Hoelscher, Michael and Blum, Helmut and Hasenauer, Jan and Kroidl, Arne and Wieser, Andreas},
  journal          = {Nature Communications},
  title            = {Long-term monitoring of SARS-CoV-2 seroprevalence and variants in Ethiopia provides prediction for immunity and cross-immunity},
  year             = {2024},
  issn             = {2041-1723},
  month            = apr,
  number           = {1},
  volume           = {15},
  creationdate     = {2024-04-29T08:30:06},
  doi              = {10.1038/s41467-024-47556-2},
  modificationdate = {2024-04-29T08:30:06},
  publisher        = {Springer Science and Business Media LLC},
}

@PhdThesis{Mutsuddy2024,
  author           = {Mutsuddy, Arnab},
  school           = {Clemson University},
  title            = {Single cell pharmacodynamic modeling of cancer cell lines},
  year             = {2024},
  creationdate     = {2024-05-30T09:51:58},
  modificationdate = {2024-05-30T09:53:40},
  url              = {https://tigerprints.clemson.edu/all_dissertations/3572},
}

@Misc{PhilippsKoe2024,
  author           = {Maren Philipps and Antonia Körner and Jakob Vanhoefer and Dilan Pathirana and Jan Hasenauer},
  title            = {Non-Negative Universal Differential Equations With Applications in Systems Biology},
  year             = {2024},
  archiveprefix    = {arXiv},
  creationdate     = {2024-06-28T08:27:59},
  eprint           = {2406.14246},
  modificationdate = {2024-06-28T08:27:59},
  primaryclass     = {q-bio.QM},
  url              = {https://arxiv.org/abs/2406.14246},
}

@Article{BaltussenJon2024,
  author           = {Baltussen, Mathieu G. and de Jong, Thijs J. and Duez, Quentin and Robinson, William E. and Huck, Wilhelm T. S.},
  journal          = {Nature},
  title            = {Chemical reservoir computation in a self-organizing reaction network},
  year             = {2024},
  issn             = {1476-4687},
  month            = jun,
  creationdate     = {2024-06-29T14:03:08},
  doi              = {10.1038/s41586-024-07567-x},
  modificationdate = {2024-06-29T14:03:08},
  publisher        = {Springer Science and Business Media LLC},
}

@Article{DuezWie2024,
  author           = {Duez, Quentin and van de Wiel, Jeroen and van Sluijs, Bob and Ghosh, Souvik and Baltussen, Mathieu G. and Derks, Max T. G. M. and Roithová, Jana and Huck, Wilhelm T. S.},
  journal          = {Journal of the American Chemical Society},
  title            = {Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility–Mass Spectrometry},
  year             = {2024},
  issn             = {1520-5126},
  month            = jul,
  number           = {30},
  pages            = {20778--20787},
  volume           = {146},
  creationdate     = {2024-08-01T09:37:07},
  doi              = {10.1021/jacs.4c04218},
  modificationdate = {2024-08-01T09:37:07},
  publisher        = {American Chemical Society (ACS)},
}

@Article{SchmiesterBra2024,
  author           = {Schmiester, Leonard and Brasó-Maristany, Fara and González-Farré, Blanca and Pascual, Tomás and Gavilá, Joaquín and Tekpli, Xavier and Geisler, Jürgen and Kristensen, Vessela N. and Frigessi, Arnoldo and Prat, Aleix and Köhn-Luque, Alvaro},
  journal          = {Clinical Cancer Research},
  title            = {{Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition}},
  year             = {2024},
  issn             = {1078-0432},
  month            = {07},
  pages            = {OF1-OF9},
  abstract         = {{Development of a computational biomarker to predict, prior to treatment, the response to CDK4/6 inhibition (CDK4/6i) in combination with endocrine therapy in patients with breast cancer.A mechanistic mathematical model that accounts for protein signaling and drug mechanisms of action was developed and trained on extensive, publicly available data from breast cancer cell lines. The model was built to provide a patient-specific response score based on the expression of six genes (CCND1, CCNE1, ESR1, RB1, MYC, and CDKN1A). The model was validated in five independent cohorts of 148 patients in total with early-stage or advanced breast cancer treated with endocrine therapy and CDK4/6i. Response was measured either by evaluating Ki67 levels and PAM50 risk of relapse (ROR) after neoadjuvant treatment or by evaluating progression-free survival (PFS).The model showed significant association with patient’s outcomes in all five cohorts. The model predicted high Ki67 [area under the curve; AUC (95\\% confidence interval, CI) of 0.80 (0.64–0.92), 0.81 (0.60–1.00) and 0.80 (0.65–0.93)] and high PAM50 ROR [AUC of 0.78 (0.64–0.89)]. This observation was not obtained in patients treated with chemotherapy. In the other cohorts, patient stratification based on the model prediction was significantly associated with PFS [hazard ratio (HR) = 2.92 (95\\% CI, 1.08–7.86), P = 0.034 and HR = 2.16 (1.02 4.55), P = 0.043].A mathematical modeling approach accurately predicts patient outcome following CDK4/6i plus endocrine therapy that marks a step toward more personalized treatments in patients with Luminal B breast cancer.}},
  creationdate     = {2024-08-01T09:42:58},
  doi              = {10.1158/1078-0432.CCR-24-0244},
  eprint           = {https://aacrjournals.org/clincancerres/article-pdf/doi/10.1158/1078-0432.CCR-24-0244/3478451/ccr-24-0244.pdf},
  modificationdate = {2024-08-01T09:42:58},
  url              = {https://doi.org/10.1158/1078-0432.CCR-24-0244},
}

@Article{JakstaiteZho2024,
  author           = {Jakštaitė, ‪Miglė and Zhou, Tao and Nelissen, Frank and Huck, Wilhelm T.S. and van Sluijs, Bob},
  title            = {Active learning maps the emergent dynamics of enzymatic reaction networks.},
  year             = {2024},
  month            = aug,
  abstract         = {The dynamic properties of enzymatic reaction networks (ERNs) are difficult to predict due to the emergence of allosteric interactions, product inhibitions and the competition for resources, that all only materialize once the networks have been assembled. Combining experimental kinetics studies with computational modelling allows us to extract information on these emergent dynamic properties and build predictive models. Here, we utilized the pentose phosphate pathway to demonstrate that previously reported approaches to construct maximally informative datasets can be significantly improved by pulsing both enzymes and substrates into microfluidic flow reactors (instead of substrates only). Our method augments information available from online databases, to map the emergent dynamic behaviours of a network.},
  creationdate     = {2024-09-10T07:54:04},
  doi              = {10.26434/chemrxiv-2024-vxfkz},
  keywords         = {Enzymatic reaction networks, optimal design, microfluidics, pentose phosphate pathway, emergent dynamics, biocatalysis, flow reactors, maximally informative data, kinetic modeling},
  modificationdate = {2024-09-10T07:54:20},
  publisher        = {American Chemical Society (ACS)},
}

@InBook{BarthelKun2024,
  author           = {Barthel, Lars and Kunz, Philipp and King, Rudibert and Meyer, Vera},
  editor           = {Kwade, Arno and Kampen, Ingo},
  pages            = {467--490},
  publisher        = {Springer Nature Switzerland},
  title            = {Harnessing Genetic and Microfluidic Approaches to Model Shear Stress Response in Cell Wall Mutants of the Filamentous Cell Factory Aspergillus niger},
  year             = {2024},
  address          = {Cham},
  isbn             = {978-3-031-63164-1},
  abstract         = {Aspergillus niger is an efficient biotechnological cell factory harnessed for the production of proteins, enzymes, platform chemicals and secondary metabolites. Critical for productivity of filamentous fungi such as A. niger is their hyphal growth and the ability of their cell walls to withstand shear stress conditions during submerged bioreactor cultivations. As chitin is thought to be fundamental for the rigidity of fungal hyphae, we generated a chitin synthase knock-out library for A. niger and studied the impact of these genetic modifications on its ability to withstand shear stress during submerged bioreactor cultivations. We isolated a double deletion mutant in which the genes chsE and chsD were inactivated and observed a reduction in chitin content by 60{\%}, cell wall thickness by 20{\%}, hyphal diameter by 60{\%} and maximum growth rate by 25{\%}. We furthermore developed a parallelized microfluidic growth chamber system that enabled us to track the growth of the double mutant and the respective control strain using a confocal laser scanning microscope, and, additionally, to compare their growth responses when subjected to different shear stress conditions. A newly developed mathematical model allowed us to compute and evaluate lag-phase, maximum growth rate and shear stress response based on the microscopic images obtained. Overall, this microfluidic approach clearly identified differences in shear stress response between both strains and has high potential as scale-down screening tool to predict growth characteristics under submerged bioreactor cultivations.},
  booktitle        = {Dispersity, Structure and Phase Changes of Proteins and Bio Agglomerates in Biotechnological Processes},
  creationdate     = {2024-09-29T16:46:24},
  doi              = {10.1007/978-3-031-63164-1_15},
  modificationdate = {2024-09-29T16:46:24},
  url              = {https://doi.org/10.1007/978-3-031-63164-1_15},
}

@Article{ArmisteadHoe2024,
  author           = {Armistead, Joy and Höpfl, Sebastian and Goldhausen, Pierre and Müller-Hartmann, Andrea and Fahle, Evelin and Hatzold, Julia and Franzen, Rainer and Brodesser, Susanne and Radde, Nicole E. and Hammerschmidt, Matthias},
  journal          = {Cell Death & Disease},
  title            = {A sphingolipid rheostat controls apoptosis versus apical cell extrusion as alternative tumour-suppressive mechanisms},
  year             = {2024},
  issn             = {2041-4889},
  month            = oct,
  number           = {10},
  volume           = {15},
  creationdate     = {2024-10-17T16:27:55},
  doi              = {10.1038/s41419-024-07134-2},
  modificationdate = {2024-10-17T16:28:08},
  publisher        = {Springer Science and Business Media LLC},
}

@Article{LakrisenkoPat2024,
  author           = {Lakrisenko, Polina and Pathirana, Dilan and Weindl, Daniel and Hasenauer, Jan},
  journal          = {PLOS ONE},
  title            = {Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states},
  year             = {2024},
  month            = {10},
  number           = {10},
  pages            = {1-19},
  volume           = {19},
  abstract         = {Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. In many cases, steady-state computation is a part of model simulation, either due to steady-state data or an assumption that the system is at steady state at the initial time point. Various methods are available for steady-state and gradient computation. Yet, the most efficient pair of methods (one for steady states, one for gradients) for a particular model is often not clear. In order to facilitate the selection of methods, we explore six method pairs for computing the steady state and sensitivities at steady state using six real-world problems. The method pairs involve numerical integration or Newton’s method to compute the steady-state, and—for both forward and adjoint sensitivity analysis—numerical integration or a tailored method to compute the sensitivities at steady-state. Our evaluation shows that all method pairs provide accurate steady-state and gradient values, and that the two method pairs that combine numerical integration for the steady-state with a tailored method for the sensitivities at steady-state were the most robust, and amongst the most computationally-efficient. We also observed that while Newton’s method for steady-state computation yields a substantial speedup compared to numerical integration, it may lead to a large number of simulation failures. Overall, our study provides a concise overview across current methods for computing sensitivities at steady state. While our study shows that there is no universally-best method pair, it also provides guidance to modelers in choosing the right methods for a problem at hand.},
  creationdate     = {2025-01-06T14:49:28},
  doi              = {10.1371/journal.pone.0312148},
  modificationdate = {2025-01-06T14:49:48},
  publisher        = {Public Library of Science},
  url              = {https://doi.org/10.1371/journal.pone.0312148},
}

@Article{SmithMal2025,
  author           = {Smith, Lucian and Malik-Sheriff, Rahuman S. and Nguyen, Tung V. N. and Hermjakob, Henning and Karr, Jonathan and Shaikh, Bilal and Drescher, Logan and Moraru, Ion I. and Schaff, James C. and Agmon, Eran and Patrie, Alexander A. and Blinov, Michael L. and Hellerstein, Joseph L. and May, Elebeoba E. and Nickerson, David P. and Gennari, John H. and Sauro, Herbert M.},
  journal          = {bioRxiv},
  title            = {Using {SED-ML} for reproducible curation: Verifying {BioModels} across multiple simulation engines},
  year             = {2025},
  abstract         = {The BioModels Repository contains over 1000 manually curated mechanistic models drawn from published literature, most of which are encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation. Therefore, it can be challenging to reproduce any given figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run a specific experiment that corresponds to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them invalid. To make these models and their results more reproducible, we report here on our work updating, correcting and providing new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. Here, we use a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible.Author summary Reproducing computationally-derived scientific results seems like it should be straightforward, but is often elusive. Code is lost, file formats change, and knowledge of what was done is only partially recorded and/or forgotten. Model repositories such as BioModels address this failing in the Systems Biology domain by encoding models in a standard format that can reproduce a figure from the paper from which it was drawn. Here, we delved into the BioModels repository to ensure that every curated model additionally contained instructions on what to do with that model, and then tested those instructions on a variety of simulation platforms. Not only did this improve the BioModels repository itself, but also improved the infrastructure necessary to run these validation comparisons in the future.Author contributions LS: Writing, Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Software, Validation. RMS: Reading, Writing, Data Curation, Methodology TN: Reading, Data Curation, Methodology HH: Reading JK: Conceptualization, Data Curation, Investigation, Methodology, Software. BS: Software LD: Software IIM: Reading, Conceptualization, Funding JCS: Software, Methodology EA: Reading, Writing AAP: Software MLB: Reading, Writing JH: Writing, Methodology EM: Reading, Writing DPN: Reading, Writing, Methodology JG: Reading, Writing, Methodology HMS: Reading, Writing, FundingCompeting Interest StatementThe authors have declared no competing interest.},
  creationdate     = {2025-01-27T09:08:10},
  doi              = {10.1101/2025.01.16.633337},
  elocation-id     = {2025.01.16.633337},
  eprint           = {https://www.biorxiv.org/content/early/2025/01/20/2025.01.16.633337.full.pdf},
  modificationdate = {2025-01-27T09:08:41},
  publisher        = {Cold Spring Harbor Laboratory},
  url              = {https://www.biorxiv.org/content/early/2025/01/20/2025.01.16.633337},
}

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