List of publications using AMICI. Total number is 95.
If you applied AMICI in your work and your publication is missing, please let us know via a new GitHub issue.
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Smith, Lucian, Rahuman S. Malik-Sheriff, Tung V. N. Nguyen, Henning
Hermjakob, Jonathan Karr, Bilal Shaikh, Logan Drescher, et al. 2025.
“Using SED-ML for Reproducible Curation: Verifying
BioModels Across Multiple Simulation Engines.”
bioRxiv. https://doi.org/10.1101/2025.01.16.633337.
Armistead, Joy, Sebastian Höpfl, Pierre Goldhausen, Andrea
Müller-Hartmann, Evelin Fahle, Julia Hatzold, Rainer Franzen, Susanne
Brodesser, Nicole E. Radde, and Matthias Hammerschmidt. 2024. “A
Sphingolipid Rheostat Controls Apoptosis Versus Apical Cell Extrusion as
Alternative Tumour-Suppressive Mechanisms.” Cell Death &
Disease 15 (10). https://doi.org/10.1038/s41419-024-07134-2.
Baltussen, Mathieu G., Thijs J. de Jong, Quentin Duez, William E.
Robinson, and Wilhelm T. S. Huck. 2024. “Chemical Reservoir
Computation in a Self-Organizing Reaction Network.”
Nature, June. https://doi.org/10.1038/s41586-024-07567-x.
Barthel, Lars, Philipp Kunz, Rudibert King, and Vera Meyer. 2024.
“Harnessing Genetic and Microfluidic Approaches to Model Shear
Stress Response in Cell Wall Mutants of the Filamentous Cell Factory
Aspergillus Niger.” In Dispersity, Structure and Phase
Changes of Proteins and Bio Agglomerates in Biotechnological
Processes, edited by Arno Kwade and Ingo Kampen, 467–90. Cham:
Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63164-1_15.
Dorešić, Domagoj, Stephan Grein, and Jan Hasenauer. 2024.
“Efficient Parameter Estimation for ODE Models of Cellular
Processes Using Semi-Quantitative Data.” bioRxiv. https://doi.org/10.1101/2024.01.26.577371.
Duez, Quentin, Jeroen van de Wiel, Bob van Sluijs, Souvik Ghosh, Mathieu
G. Baltussen, Max T. G. M. Derks, Jana Roithová, and Wilhelm T. S. Huck.
2024. “Quantitative Online Monitoring of an Immobilized Enzymatic
Network by Ion Mobility–Mass Spectrometry.” Journal of the
American Chemical Society 146 (30): 20778–87. https://doi.org/10.1021/jacs.4c04218.
Jakštaitė, Miglė, Tao Zhou, Frank Nelissen, Wilhelm T. S. Huck, and Bob
van Sluijs. 2024. “Active Learning Maps the Emergent Dynamics of
Enzymatic Reaction Networks.” August. https://doi.org/10.26434/chemrxiv-2024-vxfkz.
Kiss, Anna E, Anuroop V Venkatasubramani, Dilan Pathirana, Silke Krause,
Aline Campos Sparr, Jan Hasenauer, Axel Imhof, Marisa Müller, and Peter
B Becker. 2024. “Processivity and specificity
of histone acetylation by the male-specific lethal
complex.” Nucleic Acids Research, February,
gkae123. https://doi.org/10.1093/nar/gkae123.
Lakrisenko, Polina, Dilan Pathirana, Daniel Weindl, and Jan Hasenauer.
2024. “Benchmarking Methods for Computing Local Sensitivities in
Ordinary Differential Equation Models at Dynamic and Steady
States.” PLOS ONE 19 (10): 1–19. https://doi.org/10.1371/journal.pone.0312148.
Lang, Paul F., David R. Penas, Julio R. Banga, Daniel Weindl, and Bela
Novak. 2024. “Reusable Rule-Based Cell Cycle Model Explains
Compartment-Resolved Dynamics of 16 Observables in RPE-1 Cells.”
PLOS Computational Biology 20 (1): 1–24. https://doi.org/10.1371/journal.pcbi.1011151.
Merkt, Simon, Solomon Ali, Esayas Kebede Gudina, Wondimagegn Adissu,
Addisu Gize, Maximilian Muenchhoff, Alexander Graf, et al. 2024.
“Long-Term Monitoring of SARS-CoV-2 Seroprevalence and Variants in
Ethiopia Provides Prediction for Immunity and Cross-Immunity.”
Nature Communications 15 (1). https://doi.org/10.1038/s41467-024-47556-2.
Mutsuddy, Arnab. 2024. “Single Cell Pharmacodynamic Modeling of
Cancer Cell Lines.” PhD thesis, Clemson University. https://tigerprints.clemson.edu/all_dissertations/3572.
Philipps, Maren, Antonia Körner, Jakob Vanhoefer, Dilan Pathirana, and
Jan Hasenauer. 2024. “Non-Negative Universal Differential
Equations with Applications in Systems Biology.” https://arxiv.org/abs/2406.14246.
Schmiester, Leonard, Fara Brasó-Maristany, Blanca González-Farré, Tomás
Pascual, Joaquín Gavilá, Xavier Tekpli, Jürgen Geisler, et al. 2024.
“Computational Model Predicts Patient
Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and
CDK4/6 Inhibition.” Clinical Cancer Research,
July, OF1–9. https://doi.org/10.1158/1078-0432.CCR-24-0244.
Sluijs, Bob van, Tao Zhou, Britta Helwig, Mathieu G. Baltussen, Frank H.
T. Nelissen, Hans A. Heus, and Wilhelm T. S. Huck. 2024.
“Iterative Design of Training Data to Control Intricate Enzymatic
Reaction Networks.” Nature Communications 15 (1). https://doi.org/10.1038/s41467-024-45886-9.
Buck, Michèle C., Lisa Bast, Judith S. Hecker, Jennifer Rivière, Maja
Rothenberg-Thurley, Luisa Vogel, Dantong Wang, et al. 2023.
“Progressive Disruption of Hematopoietic Architecture from Clonal
Hematopoiesis to MDS.” iScience, 107328. https://doi.org/10.1016/j.isci.2023.107328.
Contento, Lorenzo, Noemi Castelletti, Elba Raimúndez, Ronan Le Gleut,
Yannik Schälte, Paul Stapor, Ludwig Christian Hinske, et al. 2023.
“Integrative Modelling of Reported Case Numbers and Seroprevalence
Reveals Time-Dependent Test Efficiency and Infectious Contacts.”
Epidemics 43: 100681. https://doi.org/10.1016/j.epidem.2023.100681.
Contento, Lorenzo, Paul Stapor, Daniel Weindl, and Jan Hasenauer. 2023.
“A More Expressive Spline Representation for SBML
Models Improves Code Generation Performance in
AMICI.” bioRxiv. https://doi.org/10.1101/2023.06.29.547120.
Fröhlich, Fabian. 2023. “A Practical Guide for the Efficient
Formulation and Calibration of Large, Energy- and Rule-Based Models of
Cellular Signal Transduction.” In Computational Modeling of
Signaling Networks, edited by Lan K. Nguyen, 59–86. New York, NY:
Springer US. https://doi.org/10.1007/978-1-0716-3008-2_3.
Fröhlich, Fabian, Luca Gerosa, Jeremy Muhlich, and Peter K Sorger. 2023.
“Mechanistic Model of MAPK Signaling Reveals How Allostery and
Rewiring Contribute to Drug Resistance.” Molecular Systems
Biology 19 (2): e10988. https://doi.org/10.15252/msb.202210988.
Huck, Wilhelm, Mathieu Baltussen, Thijs de Jong, Quentin Duez, and
William Robinson. 2023. “Chemical Reservoir Computation in a
Self-Organizing Reaction Network.” Research Square Platform LLC.
https://doi.org/10.21203/rs.3.rs-3487081/v1.
Lakrisenko, Polina, Paul Stapor, Stephan Grein, Łukasz Paszkowski, Dilan
Pathirana, Fabian Fröhlich, Glenn Terje Lines, Daniel Weindl, and Jan
Hasenauer. 2023. “Efficient Computation of Adjoint Sensitivities
at Steady-State in ODE Models of Biochemical Reaction Networks.”
PLOS Computational Biology 19 (1): 1–19. https://doi.org/10.1371/journal.pcbi.1010783.
Mendes, Pedro. 2023. “Reproducibility and FAIR Principles: The
Case of a Segment Polarity Network Model.” Frontiers in Cell
and Developmental Biology 11. https://doi.org/10.3389/fcell.2023.1201673.
Mishra, Shekhar, Ziyu Wang, Michael J. Volk, and Huimin Zhao. 2023.
“Design and Application of a Kinetic Model of Lipid Metabolism in
Saccharomyces Cerevisiae.” Metabolic Engineering 75:
12–18. https://doi.org/10.1016/j.ymben.2022.11.003.
Raimúndez, Elba, Michael Fedders, and Jan Hasenauer. 2023.
“Posterior Marginalization Accelerates Bayesian Inference for
Dynamical Models of Biological Processes.”
iScience, September, 108083. https://doi.org/10.1016/j.isci.2023.108083.
Tunedal, Kajsa, Federica Viola, Belén Casas Garcia, Ann Bolger, Fredrik
H. Nyström, Carl Johan Östgren, Jan Engvall, et al. 2023.
“Haemodynamic Effects of Hypertension and Type 2 Diabetes:
Insights from a 4D Flow MRI-based Personalized Cardiovascular Mathematical
Model.” The Journal of Physiology n/a (n/a). https://doi.org/10.1113/JP284652.
Albadry, Mohamed, Sebastian Höpfl, Nadia Ehteshamzad, Matthias König,
Michael Böttcher, Jasna Neumann, Amelie Lupp, et al. 2022.
“Periportal Steatosis in Mice Affects Distinct Parameters of
Pericentral Drug Metabolism.” Scientific Reports 12 (1):
21825. https://doi.org/10.1038/s41598-022-26483-6.
Erdem, Cemal, Arnab Mutsuddy, Ethan M. Bensman, William B. Dodd, Michael
M. Saint-Antoine, Mehdi Bouhaddou, Robert C. Blake, et al. 2022.
“A Scalable, Open-Source Implementation of a Large-Scale
Mechanistic Model for Single Cell Proliferation and Death
Signaling.” Nature Communications 13 (1): 3555. https://doi.org/10.1038/s41467-022-31138-1.
Fröhlich, Fabian, and Peter K. Sorger. 2022. “Fides: Reliable
Trust-Region Optimization for Parameter Estimation of Ordinary
Differential Equation Models.” PLOS Computational
Biology 18 (7): 1–28. https://doi.org/10.1371/journal.pcbi.1010322.
Massonis, Gemma, Alejandro F Villaverde, and Julio R Banga. 2022.
“Improving dynamic predictions with ensembles
of observable models.” Bioinformatics, November.
https://doi.org/10.1093/bioinformatics/btac755.
Meyer, Kristian, Mikkel Søes Ibsen, Lisa Vetter-Joss, Ernst Broberg
Hansen, and Jens Abildskov. 2022. “Industrial Ion-Exchange
Chromatography Development Using Discontinuous Galerkin Methods Coupled
with Forward Sensitivity Analysis.” Journal of Chromatography
A, 463741. https://doi.org/10.1016/j.chroma.2022.463741.
Schmucker, Robin, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali
Sinan Saglam, and Tuomas Sandholm. 2022. “Combination Treatment
Optimization Using a Pan-Cancer Pathway Model.” PLOS
Computational Biology 17 (12): 1–22. https://doi.org/10.1371/journal.pcbi.1009689.
Sluijs, Bob van, Roel J. M. Maas, Ardjan J. van der Linden, Tom F. A. de
Greef, and Wilhelm T. S. Huck. 2022. “A Microfluidic Optimal
Experimental Design Platform for Forward Design of Cell-Free Genetic
Networks.” Nature Communications 13 (1): 3626. https://doi.org/10.1038/s41467-022-31306-3.
Stapor, Paul, Leonard Schmiester, Christoph Wierling, Simon Merkt, Dilan
Pathirana, Bodo M. H. Lange, Daniel Weindl, and Jan Hasenauer. 2022.
“Mini-batch optimization enables training of
ODE models on large-scale datasets.” Nature
Communications 13 (1): 34. https://doi.org/10.1038/s41467-021-27374-6.
Sundqvist, Nicolas, Sebastian Sten, Peter Thompson, Benjamin Jan
Andersson, Maria Engström, and Gunnar Cedersund. 2022.
“Mechanistic Model for Human Brain Metabolism and Its Connection
to the Neurovascular Coupling.” PLOS Computational
Biology 18 (12): 1–24. https://doi.org/10.1371/journal.pcbi.1010798.
Villaverde, Alejandro F., Elba Raimúndez, Jan Hasenauer, and Julio R.
Banga. 2022. “Assessment of Prediction Uncertainty Quantification
Methods in Systems Biology.” IEEE/ACM Transactions on
Computational Biology and Bioinformatics, 1–12. https://doi.org/10.1109/TCBB.2022.3213914.
Adlung, Lorenz, Paul Stapor, Christian Tönsing, Leonard Schmiester,
Luisa E. Schwarzmüller, Lena Postawa, Dantong Wang, et al. 2021.
“Cell-to-Cell Variability in JAK2/STAT5 Pathway Components and
Cytoplasmic Volumes Defines Survival Threshold in Erythroid Progenitor
Cells.” Cell Reports 36 (6): 109507. https://doi.org/10.1016/j.celrep.2021.109507.
Bast, Lisa, Michèle C. Buck, Judith S. Hecker, Robert A. J. Oostendorp,
Katharina S. Götze, and Carsten Marr. 2021. “Computational
Modeling of Stem and Progenitor Cell Kinetics Identifies Plausible
Hematopoietic Lineage Hierarchies.” iScience 24 (2):
102120. https://doi.org/10.1016/j.isci.2021.102120.
Gaspari, Erika. 2021. “Model-Driven Design of Mycoplasma as a
Vaccine Chassis.” PhD thesis, Wageningen: Wageningen University.
https://doi.org/10.18174/539593.
Gudina, Esayas Kebede, Solomon Ali, Eyob Girma, Addisu Gize,
Birhanemeskel Tegene, Gadissa Bedada Hundie, Wondewosen Tsegaye Sime, et
al. 2021. “Seroepidemiology and model-based
prediction of SARS-CoV-2 in Ethiopia: longitudinal cohort study among
front-line hospital workers and communities.” The
Lancet Global Health 9 (11): e1517–27. https://doi.org/10.1016/S2214-109X(21)00386-7.
Maier, Corinna. 2021. “Bayesian Data Assimilation and
Reinforcement Learning for Model-Informed Precision Dosing in
Oncology.” Doctoralthesis, Universität Potsdam. https://doi.org/10.25932/publishup-51587.
Raimúndez, Elba, Erika Dudkin, Jakob Vanhoefer, Emad Alamoudi, Simon
Merkt, Lara Fuhrmann, Fan Bai, and Jan Hasenauer. 2021. “COVID-19
Outbreak in Wuhan Demonstrates the Limitations of Publicly Available
Case Numbers for Epidemiological Modeling.” Epidemics
34: 100439. https://doi.org/10.1016/j.epidem.2021.100439.
Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2021.
“Efficient Gradient-Based Parameter Estimation for Dynamic Models
Using Qualitative Data.” bioRxiv. https://doi.org/10.1101/2021.02.06.430039.
Städter, Philipp, Yannik Schälte, Leonard Schmiester, Jan Hasenauer, and
Paul L. Stapor. 2021. “Benchmarking of Numerical Integration
Methods for ODE Models of Biological Systems.” Scientific
Reports 11 (1): 2696. https://doi.org/10.1038/s41598-021-82196-2.
Sten, Sebastian, Henrik Podéus, Nicolas Sundqvist, Fredrik Elinder,
Maria Engström, and Gunnar Cedersund. 2021. “A Multi-Data Based
Quantitative Model for the Neurovascular Coupling in the Brain.”
bioRxiv. https://doi.org/10.1101/2021.03.25.437053.
Tomasoni, Danilo, Alessio Paris, Stefano Giampiccolo, Federico Reali,
Giulia Simoni, Luca Marchetti, Chanchala Kaddi, et al. 2021.
“QSPcc Reduces Bottlenecks in Computational Model
Simulations.” Communications Biology 4 (1): 1022. https://doi.org/10.1038/s42003-021-02553-9.
van Rosmalen, R. P., R. W. Smith, V. A. P. Martins dos Santos, C. Fleck,
and M. Suarez-Diez. 2021. “Model Reduction of Genome-Scale
Metabolic Models as a Basis for Targeted Kinetic Models.”
Metabolic Engineering 64: 74–84. https://doi.org/10.1016/j.ymben.2021.01.008.
Vanhoefer, Jakob, Marta R. A. Matos, Dilan Pathirana, Yannik Schälte,
and Jan Hasenauer. 2021. “Yaml2sbml: Human-Readable and -Writable
Specification of ODE Models and Their Conversion to
SBML.” Journal of Open Source Software 6
(61): 3215. https://doi.org/10.21105/joss.03215.
Villaverde, Alejandro F, Dilan Pathirana, Fabian Fröhlich, Jan
Hasenauer, and Julio R Banga. 2021. “A
protocol for dynamic model calibration.” Briefings in
Bioinformatics, October. https://doi.org/10.1093/bib/bbab387.
Alabert, Constance, Carolin Loos, Moritz Voelker-Albert, Simona
Graziano, Ignasi Forné, Nazaret Reveron-Gomez, Lea Schuh, et al. 2020.
“Domain Model Explains Propagation Dynamics and Stability of
Histone H3K27 and H3K36 Methylation Landscapes.” Cell
Reports 30 (January): 1223–1234.e8. https://doi.org/10.1016/j.celrep.2019.12.060.
Erdem, Cemal, Ethan M. Bensman, Arnab Mutsuddy, Michael M.
Saint-Antoine, Mehdi Bouhaddou, Robert C. Blake, Will Dodd, et al. 2020.
“A Simple and Efficient Pipeline for Construction, Merging,
Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic
Models.” bioRxiv. https://doi.org/10.1101/2020.11.09.373407.
Gerosa, Luca, Christopher Chidley, Fabian Fröhlich, Gabriela Sanchez,
Sang Kyun Lim, Jeremy Muhlich, Jia-Yun Chen, et al. 2020.
“Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable
Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells.” Cell
Systems. https://doi.org/10.1016/j.cels.2020.10.002.
Kuritz, Karsten, Alain R Bonny, João Pedro Fonseca, and Frank Allgöwer.
2020. “PDE-Constrained Optimization for Estimating Population
Dynamics over Cell Cycle from Static Single Cell Measurements.”
bioRxiv. https://doi.org/10.1101/2020.03.30.015909.
Maier, Corinna, Niklas Hartung, Charlotte Kloft, Wilhelm Huisinga, and
Jana de Wiljes. 2020. “Reinforcement Learning and Bayesian Data
Assimilation for Model-Informed Precision Dosing in Oncology.” https://arxiv.org/abs/2006.01061.
Schälte, Yannik, and Jan Hasenauer. 2020. “Efficient exact inference for dynamical systems with
noisy measurements using sequential approximate Bayesian
computation.” Bioinformatics 36 (Supplement_1):
i551–59. https://doi.org/10.1093/bioinformatics/btaa397.
Schmiester, Leonard, Daniel Weindl, and Jan Hasenauer. 2020.
“Parameterization of Mechanistic Models from Qualitative Data
Using an Efficient Optimal Scaling Approach.” Journal of
Mathematical Biology, July. https://doi.org/10.1007/s00285-020-01522-w.
Schuh, Lea, Carolin Loos, Daniil Pokrovsky, Axel Imhof, Ralph A. W.
Rupp, and Carsten Marr. 2020. “H4K20 Methylation Is Differently
Regulated by Dilution and Demethylation in Proliferating and
Cell-Cycle-Arrested Xenopus Embryos.” Cell Systems 11
(6): 653–662.e8. https://doi.org/10.1016/j.cels.2020.11.003.
Sten, Sebastian. 2020. “Mathematical Modeling of Neurovascular
Coupling.” Linköping University Medical Dissertations. PhD
thesis, Linköping UniversityLinköping UniversityLinköping University,
Division of Diagnostics; Specialist Medicine, Faculty of Medicine;
Health Sciences, Center for Medical Image Science; Visualization (CMIV);
Linköping University, Division of Diagnostics; Specialist Medicine. https://doi.org/10.3384/diss.diva-167806.
Sten, Sebastian, Fredrik Elinder, Gunnar Cedersund, and Maria Engström.
2020. “A Quantitative Analysis of Cell-Specific Contributions and
the Role of Anesthetics to the Neurovascular Coupling.”
NeuroImage 215: 116827. https://doi.org/10.1016/j.neuroimage.2020.116827.
Tsipa, Argyro, Jake Alan Pitt, Julio R. Banga, and Athanasios
Mantalaris. 2020. “A Dual-Parameter Identification Approach for
Data-Based Predictive Modeling of Hybrid Gene Regulatory Network-Growth
Kinetics in Pseudomonas Putida Mt-2.” Bioprocess and
Biosystems Engineering 43 (9): 1671–88. https://doi.org/10.1007/s00449-020-02360-2.
Dharmarajan, Lekshmi, Hans-Michael Kaltenbach, Fabian Rudolf, and Joerg
Stelling. 2019. “A Simple and Flexible Computational Framework for
Inferring Sources of Heterogeneity from Single-Cell Dynamics.”
Cell Systems 8 (1): 15–26.e11. https://doi.org/10.1016/j.cels.2018.12.007.
Fischer, David S., Anna K. Fiedler, Eric Kernfeld, Ryan M. J. Genga,
Aimée Bastidas-Ponce, Mostafa Bakhti, Heiko Lickert, Jan Hasenauer, Rene
Maehr, and Fabian J. Theis. 2019. “Inferring Population Dynamics
from Single-Cell RNA-Sequencing Time Series Data.” Nature
Biotechnology 37: 461–68. https://doi.org/10.1038/s41587-019-0088-0.
Gregg, Robert W, Saumendra N Sarkar, and Jason E Shoemaker. 2019.
“Mathematical Modeling of the cGAS Pathway Reveals Robustness of
DNA Sensing to TREX1 Feedback.” Journal of Theoretical
Biology 462 (February): 148–57. https://doi.org/10.1016/j.jtbi.2018.11.001.
Kapfer, Eva-Maria, Paul Stapor, and Jan Hasenauer. 2019.
“Challenges in the Calibration of Large-Scale Ordinary
Differential Equation Models.” IFAC-PapersOnLine 52
(26): 58–64. https://doi.org/10.1016/j.ifacol.2019.12.236.
Nousiainen, Kari, Jukka Intosalmi, and Harri Lähdesmäki. 2019. “A
Mathematical Model for Enhancer Activation Kinetics During Cell
Differentiation.” In Algorithms for Computational
Biology, edited by Ian Holmes, Carlos Martı́n-Vide, and Miguel A.
Vega-Rodrı́guez, 191–202. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-18174-1_14.
Pedretscher, B., B. Kaltenbacher, and O. Pfeiler. 2019. “Parameter
Identification and Uncertainty Quantification in Stochastic State Space
Models and Its Application to Texture Analysis.” Applied
Numerical Mathematics 146: 38–54. https://doi.org/10.1016/j.apnum.2019.06.020.
Pitt, Jake Alan, and Julio R Banga. 2019. “Parameter Estimation in
Models of Biological Oscillators: An Automated Regularised Estimation
Approach.” BMC Bioinformatics 20 (February): 82. https://doi.org/10.1186/s12859-019-2630-y.
Schmiester, Leonard, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer, and
Daniel Weindl. 2019. “Efficient
parameterization of large-scale dynamic models based on relative
measurements.” Bioinformatics, July. https://doi.org/10.1093/bioinformatics/btz581.
Terje Lines, Glenn, Łukasz Paszkowski, Leonard Schmiester, Daniel
Weindl, Paul Stapor, and Jan Hasenauer. 2019. “Efficient
Computation of Steady States in Large-Scale ODE Models of Biochemical
Reaction Networks.” IFAC-PapersOnLine 52 (26): 32–37. https://doi.org/10.1016/j.ifacol.2019.12.232.
Villaverde, Alejandro F., Elba Raimúndez, Jan Hasenauer, and Julio R.
Banga. 2019. “A Comparison of Methods for Quantifying Prediction
Uncertainty in Systems Biology.” IFAC-PapersOnLine 52
(26): 45–51. https://doi.org/10.1016/j.ifacol.2019.12.234.
Wang, Dantong, Paul Stapor, and Jan Hasenauer. 2019. “Dirac
Mixture Distributions for the Approximation of Mixed Effects
Models.” IFAC-PapersOnLine 52 (26): 200–206. https://doi.org/10.1016/j.ifacol.2019.12.258.
Watanabe, Simon Berglund. 2019. “Identifiability of Parameters in
PBPK Models.” Master’s thesis, Chalmers University of Technology
/ Department of Mathematical Sciences. https://hdl.handle.net/20.500.12380/256855.
Ballnus, Benjamin, Steffen Schaper, Fabian J Theis, and Jan Hasenauer.
2018. “Bayesian Parameter Estimation for Biochemical Reaction
Networks Using Region-Based Adaptive Parallel Tempering.”
Bioinformatics 34 (13): i494–501. https://doi.org/10.1093/bioinformatics/bty229.
Bast, Lisa, Filippo Calzolari, Michael Strasser, Jan Hasenauer, Fabian
J. Theis, Jovica Ninkovic, and Carsten Marr. 2018. “Subtle Changes
in Clonal Dynamics Underlie the Age-Related Decline in
Neurogenesis.” Cell Reports. https://doi.org/10.1016/j.celrep.2018.11.088.
Fröhlich, Fabian, Thomas Kessler, Daniel Weindl, Alexey Shadrin, Leonard
Schmiester, Hendrik Hache, Artur Muradyan, et al. 2018. “Efficient
Parameter Estimation Enables the Prediction of Drug Response Using a
Mechanistic Pan-Cancer Pathway Model.” Cell Systems 7
(6): 567–579.e6. https://doi.org/10.1016/j.cels.2018.10.013.
Fröhlich, Fabian, Anita Reiser, Laura Fink, Daniel Woschée, Thomas
Ligon, Fabian Joachim Theis, Joachim Oskar Rädler, and Jan Hasenauer.
2018. “Multi-Experiment Nonlinear Mixed Effect Modeling of
Single-Cell Translation Kinetics After Transfection.” Npj
Systems Biology and Applications 5 (1): 1. https://doi.org/10.1038/s41540-018-0079-7.
Kaltenbacher, Barbara, and Barbara Pedretscher. 2018. “Parameter
Estimation in SDEs via the Fokker–Planck Equation: Likelihood Function
and Adjoint Based Gradient Computation.” Journal of
Mathematical Analysis and Applications 465 (2): 872–84. https://doi.org/10.1016/j.jmaa.2018.05.048.
Loos, Carolin, Sabrina Krause, and Jan Hasenauer. 2018.
“Hierarchical Optimization for the Efficient Parametrization of
ODE Models.” Bioinformatics 34 (24):
4266–73. https://doi.org/10.1093/bioinformatics/bty514.
Loos, Carolin, Katharina Moeller, Fabian Fröhlich, Tim Hucho, and Jan
Hasenauer. 2018. “A Hierarchical, Data-Driven Approach to Modeling
Single-Cell Populations Predicts Latent Causes of Cell-to-Cell
Variability.” Cell Systems 6 (5): 593–603. https://doi.org/10.1016/j.cels.2018.04.008.
Pitt, Jake Alan, Lucian Gomoescu, Constantinos C. Pantelides, Benoît
Chachuat, and Julio R. Banga. 2018. “Critical Assessment of
Parameter Estimation Methods in Models of Biological
Oscillators.” IFAC-PapersOnLine 51 (19): 72–75. https://doi.org/10.1016/j.ifacol.2018.09.040.
Schälte, Y., P. Stapor, and J. Hasenauer. 2018. “Evaluation of
Derivative-Free Optimizers for Parameter Estimation in Systems
Biology.” FAC-PapersOnLine 51 (19): 98–101. https://doi.org/10.1016/j.ifacol.2018.09.025.
Stapor, Paul, Fabian Fröhlich, and Jan Hasenauer. 2018.
“Optimization and Profile Calculation of ODE Models
Using Second Order Adjoint Sensitivity Analysis.”
Bioinformatics 34 (13): i151–59. https://doi.org/10.1093/bioinformatics/bty230.
Villaverde, Alejandro F, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer,
and Julio R Banga. 2018. “Benchmarking
optimization methods for parameter estimation in large kinetic
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