Flux balance analysis | EPFL Graph Search

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models (over 2000 reactions) in a few seconds on modern personal computers. The related method of metabolic pathway analysis seeks to find and list all possible pathways between metabolites. FBA finds applications in bioprocess engineering to systematically identify modifications to the metabolic networks of microbes used in fermentation processes that improve product yields of industrially important chemicals such as ethanol and succinic acid. It has also been used for the identification of putative drug targets in cancer and pathogens, rational design of culture media, and host–pathogen interactions. The results of FBA can be visualized using flux maps similar to the image on the right, which illustrates the steady-state fluxes carried by reactions in glycolysis. The thickness of the arrows is proportional to the flux through the reaction. FBA formalizes the system of equations describing the concentration changes in a metabolic network as the dot product of a matrix of the stoichiometric coefficients (the stoichiometric matrix S) and the vector v of the unsolved fluxes. The right-hand side of the dot product is a vector of zeros representing the system at steady state. Linear programming is then used to calculate a solution of fluxes corresponding to the steady state. Some of the earliest work in FBA dates back to the early 1980s. Papoutsakis demonstrated that it was possible to construct flux balance equations using a metabolic map. It was Watson, however, who first introduced the idea of using linear programming and an objective function to solve for the fluxes in a pathway.

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Vassily Hatzimanikatis, Ljubisa Miskovic, Michaël Roger Germain Moret

Large omics datasets are nowadays routinely generated to provide insights into cellular processes. Nevertheless, making sense of omics data and determining intracellular metabolic states remains challenging. Kinetic models of metabolism are crucial for int ...

2023

Constraint-based metabolic control analysis for rational strain engineering

Vassily Hatzimanikatis, Ljubisa Miskovic, Meriç Ataman, Tuure Eelis Hameri, Sofia Tsouka

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering ...

2021