Conditional Flux Balance Analysis in Python

Introduction

This cFBA Python toolbox toolbox provides a set of functions to facilitate the implementation of conditional Flux Balance Analysis (cFBA) in Python for researchers interested in predicting metabolic responses in dynamic-cyclic environments.

What is cFBA?

cFBA is a mathematical framework [1] [2] that integrates stoichiometric modeling, dynamic Flux Balance Analysis (dFBA), and resource allocation to study metabolism in organisms living in cyclic environments. It allows researchers to simulate and analyze metabolic reactions under changing environmental conditions, such as day/night cycles, feast/famine conditions, aerobic/anaerobic exchanges, etc.

Key Features of cFBA

• Dynamic Simulation: cFBA allows for the simulation of metabolic reactions over time, capturing the dynamic behavior of biological systems.

• Resource Allocation: The method considers optimal resource allocation strategies, shedding light on how organisms prioritize metabolic pathways in response to changing conditions.

• Cyclic behavior: The method implements a constraint that maximizes biomass growth while maintaining the biomass composition equal at start and end point, simulating an organism adapted to cyclic environments.

How to Use This Documentation

This documentation provides detailed instructions on how to install and use the cFBA Python toolbox. Whether you’re new to cFBA or an experienced user, you’ll find everything you need to get started, including installation guides, usage examples, and troubleshooting tips.

References

[1]

Rügen, Marco, Bockmayr, Alexander, and Steuer, Ralf (2015), ‘Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA’, Scientific reports, 5, 15247.

[2]

Reimers, Alexandra-M, et al. (2017), ‘Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth’, Proceedings of the National Academy of Sciences, 114 (31), E6457-E65.

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