What is flux balance analysis?

Abstract

Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.Flux balance analysis (FBA) is a widely used approach for studying biochemical networks, in particular the genome-scale metabolic network reconstructions that have been built in the past decade [1][2][3][4][5] . These network reconstructions contain all of the known metabolic reactions in an organism and the genes that encode each enzyme. FBA calculates the flow of metabolites through this metabolic network, thereby making it possible to predict the growth rate of an organism or the rate of production of a biotechnologically important metabolite. With metabolic models for 35 organisms already available (http://systemsbiology.ucsd.edu/In_Silico_Organisms/Other_Organisms) and highthroughput technologies enabling the construction of many more each year 6, 7 , FBA is an important tool for harnessing the knowledge encoded in these models.In this primer, we illustrate the principles behind FBA by applying it to the prediction of the specific growth rate of Escherichia coli in the presence and absence of oxygen. The principles outlined can be applied in many other contexts to analyze the phenotypes and capabilities of organisms with different environmental and genetic perturbations (a supplementary tutorial provides six additional worked examples with figures and computer code). Flux balance analysis is based on constraintsThe first step in FBA is to mathematically represent metabolic reactions (Box 1). The core feature of this representation is a tabulation, in the form of a numerical matrix, of the stoichiometric coefficients of each reaction (Fig. 1a,b). These stoichiometries impose constraints on the flow of metabolites through the network. Constraints such as these lie at the heart of FBA, differentiating the approach from theory-based models based on biophysical equations that require many difficult-to-measure kinetic parameters 8,9 .Constraints are represented in two ways, as equations that balance reaction inputs and outputs and as inequalities that impose bounds on the system. The matrix of stoichiometries imposes flux (that is, mass) balance constraints on the system, ensuring that the total amount of any compound being produced must be equal to the total amount being consumed at steady state (Fig. 1c). Every reaction can also be given upper and lower bounds, which define the maximum and minimum allowable fluxes of the reactions. These balances and

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

“…To this end, we performed a two‐step computational analysis, which is based first on a molecular analysis of patient tumors followed by a genome‐scale metabolic modeling (GSMM) analysis in the second step. A genome‐scale metabolic model (GSMM) is a computer program built around a set of reactions that comprise a metabolic network, accompanied by a mapping of genes and proteins to the reactions they catalyze within the network (Orth et al , 2010). GSMM of human metabolism has become feasible in recent years thanks to the publication of the first full‐fledged genome‐scale human metabolic models [Recon1 (Duarte et al , 2007; Ma et al , 2007)].…”

Genome sequencing and systems biology analysis of a lipase-producing bacterial strain

“…Constraint-Based Reconstruction and Analysis (COBRA) Toolbox in Matlab was used for flux analysis (Orth et al, 2010). FBA was performed using optimize CbModel, which allowed for the prediction of the growth rate and product synthesis rate of the strain.…”

Flux variability analysis approach of autism related metabolism in stoichiometric model of mitochondria

“…With Flux Balance Analysis (FBA) it is possible to find out the best possible flux solution in the model for particular objective function, for example fat synthesis in adipose tissue from glucose (Fell and Small, 1986). Although this method shows only one of the many best solutions, it is very fast calculation method and takes only few seconds to get the results (Orth et al, 2010). Flux Variability Analysis (FVA) shows minimum and maximum flux of reactions in the network to maintain some state of the network (Gudmundsson and Thiele, 2010).…”

“…The analysis of models and reconstructions are based on different methods like Flux Balance Analysis (Orth et al, 2010), Flux Variability Analysis (Burgard et al, 2001;. With Flux Balance Analysis (FBA) it is possible to find out the best possible flux solution in the model for particular objective function, for example fat synthesis in adipose tissue from glucose (Fell and Small, 1986).…”