FreeFlux

FreeFlux is a Python package designed for ¹³C metabolic flux analysis of biological systems at isotopic steady state or transient state, making it suitable for both heterotrophic and autotrophic organisms. With FreeFlux, you can:

• Estimate metabolic fluxes
• Simulate labeling patterns of metabolite (fragments)
• Conduct constraint-based optimizations such as flux balance analysis and flux variability analysis

Our goal is to increase the accessibility of ¹³C fluxomics techniques to researchers in the metabolic phenotyping and engineering community.

To get started, check out our documentation, which provides an overview of FreeFlux's fundamental functions using a toy model. We've also included tutorials for practical models of E. coli and Synechocystis.

Installation

FreeFlux was tested in Python 3.7, 3.8, 3.9 and 3.10. It can be installed using pip from PyPI:

python -m pip install --upgrade pip
pip install freeflux

or from source (assuming you have git installed):

git clone https://github.com/Chaowu88/freeflux.git /path/to/freeflux
pip install /path/to/freeflux

Installation within an virtual environment is recommendated.

If FreeFlux is installed on a Linux platform with Python>=3.8, using JAX can significantly speed up the preparation step before flux estimation. JAX can be installed using the following command:

pip install "jax[cpu]>=0.4,<=0.4.18"

Solver installation

FreeFlux requires the numerical optimization framework OpenOpt for nonlinear regression. It can be installed by the following commands:

pip install openopt
pip install FuncDesigner

Note that the framework is known to work well in Python 3.7, but may have compatibility issues in Python 3.8 and above. In such cases, please refer to this link for solutions.

FreeFlux uses the modeling language Pyomo to formulate linear optimization problem. By default, solvers are not installed together with Pyomo and should be installed independently. For example, to install the glpk solver, run the following command:

conda install -c conda-forge glpk

Example Usage

A typical workflow with FreeFlux starts by building a model through reading metabolic reactions with atom transitions. Users can then call a handler, such as the fitter, simulator, or optimizer, to perform flux estimation, labeling pattern simulation, or constraint-based flux analysis, respectively. Various methods are provided for these handlers for data input and computation.

Below is an example script that performs flux estimation at steady state using the toy model:

MODEL_FILE = 'path/to/reactions.tsv'

from freeflux import Model

model = Model('demo')
model.read_from_file(MODEL_FILE)

with model.fitter('ss') as fit:
    fit.set_labeling_strategy(
        'AcCoA',
        labeling_pattern = ['01', '11'],
        percentage = [0.25, 0.25],
        purity = [1, 1]
    )
    fit.set_flux_bounds('all', bounds = [-100, 100])
    fit.set_measured_MDV(
        'Glu_12345',
        mean = [0.328,0.276,0.274,0.088,0.03,0.004],
        sd = [0.01,0.01,0.01,0.01,0.01,0.01]
    )
    fit.set_measured_flux('v1', mean = 10, sd = 1)
    fit.prepare()
    res = fit.solve()

For more information, please refer to the documentation.

License

FreeFlux is released under the GPL version 3 license, please see here for more details.

Citation

Wu et al. (2023) FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis, ACS Synthetic Biology 12(9):2707-2714. doi:10.1021/acssynbio.3c00265

Feel free to provide feedback at chao.wu@nrel.gov or chaowu09@gmail.com.