Flux Balance Analysis

General concept

Simplex linear programming optimization can be used with a metabolic map described in matrix format. An objective function is optimized, such as the minimization of free energy dissipation, minimization of production or consumption of molecules or a biomass objective function[http://www.ncbi.nlm.nih.gov/pubmed/20430689,[http://dx.doi.org/10.1006/jtbi.1993.1203].

Items to consider when implementing

Whatever one optimizes must have biological relevance. It is assumed that evolution has optimized whatever is being optimized in silico. Also certain objectives, such as maximizing ATP, may cause exclude important pathways needed for growth, and are therefore usually not appropriate for genome-scale models.

Many FBA solutions have alternate optimal solutions. I.e., there are often many different flux distributions that provide the same objective value. See "Delineation of all alternate optimal solutions (AOS)"

Software packages with this method

Applications of interest

Fell and Small (1986).
The metabolic pathways constraint the flux balance in adipocyte synthesis of triacyglycerols, so LP was used to compute triacyglycerol synthesis under different P/O ratios. In adipocytes, the triacylglycerol synthesis was maximized while glucose uptake was minimized. This study showed its greatest impact in showing that mass balance constraints of each intermediate and coenzyme at steady state limit the range of potential reaction rates with pathways, and that LP is effective in quickly computing how these fluxes can change given variations in parameters. They also propose the idea of putting bound constraints on internal reactions.

Majewski and Domach.
Different constraints can be tested by modeling central metabolism and testing which constraints better recapitulate phenotypic measurements. A model of E. coli central metabolism was used to test two constraints with the assumption that the cells would maximize ATP and GTP production. Model simulations more support that capacity constraints in the TCA cycle are responsible for acetate overflow, while limitations in respiration rates seemed to only be important near the maximal growth rate.

Varma and Palsson. In this study, the biomass objective function was introduced with the appropriate values for non-growth-associated maintenance and the P/O value. In this study, the biomass objective function was tested with sensitivity and shadow-price analyses, and its sensitivity to different P/O ratios was assessed. Growth rate predictions were improved by the inclusion of an experimentally-based non-growth associated ATP maintenance demand. See also [http://www.ncbi.nlm.nih.gov/pubmed/21322280].

Relevant references

M.R. Watson. metabolic maps for the apple II. 1984

Varma and Palsson. Metabolic Capabilities of Escherichia coli II. Optimal Growth Patterns. Journal of Theoretical Biology. 1993. 165:503-522

Famili I, Forster J, Nielsen J, Palsson BO. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci U S A. 2003 Nov 11;100(23):13134-9.

Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010 Mar;28(3):245-8.

Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010 Jun;13(3):344-9.

Related methods

Phenotypic phase plane

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