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Flux balance analysis



Flux balance analysis (FBA) has been shown to be a very useful technique for analysis of metabolic capabilities of cellular systems. Living organisms survive, grow or strive with the help of the available nutrients they find in their environment. They transform the nutrients into molecules they can use through a complex set of chemical reactions called metabolism. About organisms for which the whole metabolism is approximately known, (for instance the bacteria E. Coli) one can address the following problem: given some known available nutrients, which set of metabolic fluxes maximizes the growth rate of the organism? The aim of metabolic flux balance analysis, which is a mathematical analysis of the metabolism, is to solve this problem.

Organisms like E. Coli can be cultivated in mediums with defined concentrations of nutrients, and their growth rate can be measured, so that results of FBA can be compared with experiments.

A good description of the basic concepts of FBA can be found at


A chemical system can be described by a set of differential equations. For instance, suppose a chemical system has two reactions: A + B -> C at rate V1 and C -> A at rate V2. This system ccan be described by the differential equations: d(A)/dt = V2 - V1, d(B)/dt = -V1, and d(C)/dt = V1 - V2. A system is at steady state when all three derivatives (rates of change) are 0. FBA involves carrying out a steady state analysis, using the stoichiometric matrix for the system in question.

The system is assumed to be optimised with respect to functions such as maximisation of biomass production or minimisation of nutrient utilisation, following which it is solved to obtain a steady state flux distribution. This flux distribution is then used to interpret the metabolic capabilities of the system. The dynamic mass balance of the metabolic system is described using the stoichiometric matrix, relating the flux rates of enzymatic reactions, \mathbf{v}_{n\times 1} to time derivatives of metabolite concentrations, \mathbf{x}_{m\times 1} as

\frac{d\mathbf{x}}{dt} = \mathbf{S}\,\mathbf{v}
\mathbf{v}=[v_1 \  v_2 \  ... \  v_{n}\  b_1\   b_2\  ...\  	b_{n_{ext}} ]^T

where vi signifies the internal fluxes, bi represents the exchange fluxes in the system and next is the number of external metabolites in the system. At steady state,

\frac{d\mathbf{x}}{dt} = \mathbf{S}\,\mathbf{v} = 0

Therefore, the required flux distribution belongs to the null space of \mathbf{S} . Since m < n , the system is under-determined and may be solved for \mathbf{v} fixing an optimisation criterion, following which, the system translates into a linear programming problem:

\min_{\mathbf{v}}\ \mathbf{c}^T\mathbf{v} \qquad \textrm{s. t.} \quad \mathbf{S}\,\mathbf{v}=0

where c represents the objective function composition, in terms of the fluxes. Further, we can constrain:

0 <  v_i < \infty

-\infty <  b_i < \infty

which necessitates all internal irreversible reactions to have a flux in the positive direction and allows exchange fluxes to be in either direction. Practically, a finite upper bound can be imposed, so that the problem does not become unbounded. This upper bound may also be decided based on the knowledge of cellular physiology.


FBA also has the capabilities to address effect of gene deletions and other types of perturbations on the system. Gene deletion studies can be performed by constraining the reaction flux(es) corresponding to the gene(s) (and therefore, of their corresponding proteins(s)), to zero. Effects of inhibitors of particular proteins can also be studied in a similar way, by constraining the upper bounds of their fluxes to any defined fraction of the normal flux, corresponding to the extents of inhibition.


  • Bonarius HPJ, Schmid G, Tramper J (1997) Flux analysis of underdetermined metabolic networks: The quest for the missing constraints. Trends Biotech 15: 308–314.
  • Forster J, Famili I, Fu P, Palsson BO, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13: 244–253.
  • Edwards JS, Palsson BO (2000) The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. Proc Natl Acad Sci U S A 97: 5528–5533.
  • Edwards JS, Covert M, Palsson BO (2002) Metabolic modelling of microbes: The flux-balance approach. Environ Microbiol 4: 133–133.
  • Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotech 14: 491–496.
  • Alvarez-Vasquez F, Sims K, Cowart L, Okamoto Y, Voit E, et al. (2005) Simulation and validation of modelled sphingolipid metabolism in Saccharomyces cerevisiae. Nature 433: 425–430.
  • Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19: 125–130.
  • Raman K, Rajagopalan P, Chandra N (2005) Flux Balance Analysis of Mycolic Acid Pathway: Targets for Anti-Tubercular Drugs. PLoS Comput Biol 1(5): e46
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Flux_balance_analysis". A list of authors is available in Wikipedia.
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