The biomass objective function

doi:10.1016/j.mib.2010.03.003

Current Opinion in Microbiology

Volume 13, Issue 3, June 2010, Pages 344-349

https://doi.org/10.1016/j.mib.2010.03.003 Get rights and content

Flux balance analysis (FBA) is a mathematical approach for analyzing the flow of metabolites through a metabolic network. To computationally predict cell growth using FBA, one has to determine the biomass objective function that describes the rate at which all of the biomass precursors are made in the correct proportions. Here we review fundamental issues associated with its formulation and use to compute optimal growth states.

Introduction

Flux balance analysis (FBA) [1] 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 [2, 3]. 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. An objective function, such as the biomass objective function, is necessary to compute an optimal network state and resulting flux distribution (unique or nonunique) in a constraint-based reconstruction as the solution space is often very large for genome-scale networks [4]. With metabolic models becoming available for a growing number of organisms [5] and high-throughput technologies enabling the construction of many more each year [6], FBA is an important tool for harnessing the knowledge encoded in these models.

Genome-scale models are used to compute a variety of phenotypic states. How the genome-scale metabolic network supports the growth of a cell has been a topic of much interest. Here we, (1) discuss the computation of cellular yields and growth rates and how they differ, (2) outline the formulation of a detailed biomass objective function, and (3) review several studies that have focused on the use of the objective function.

Section snippets

Computing cellular yields and growth rates

Metabolic network reconstructions contain the known biochemical conversions inside the cell and allow for the computation of both topological properties and biophysical capabilities. The vast majority of cellular metabolic conversions are enzymatically catalyzed with a few occurring spontaneously. A curated metabolic reconstruction can be utilized as a comprehensive part list of the cell, allowing for detailed and accurate computation of the conversion of substrates into products by the cell.

The formulation of the biomass objective function

The formulation of a detailed biomass objective function for use in examining metabolic networks is dependent on knowing the composition of the cell and energetic requirements necessary to generate biomass content from metabolic precursors (Figure 2). One can formulate a biomass objective function at a different levels of detail.

Brief review of studies examining cellular objective functions

Over the past two decades, a number of studies have been carried out to examine the use of objective function optimization with reconstructed networks towards predicting biological outcomes (Table 1). These studies have utilized small-scale central metabolic networks, as well as genome-scale reconstructions of bacteria and eukaryotic organisms. This set of studies can roughly be divided into two categories: (1) studies examining hypotheses on presumed cellular objective functions through

Conclusions

The biomass objective function describes the growth requirements of a cell. It is needed to perform a variety of Constraint-Based Reconstruction and Analysis (COBRA) methods [21]. It has a variety of uses ranging from the interpretation of evolutionary outcomes [22, 23, 24] to the introduction of a plasmid into a cell through the creation of additional metabolic burden [19]. Its use can allow for the computation of fluxes and provide insights into the functioning of cellular processes [25].

What

Acknowledgements

We would like to thank Jacob D Feala and Daniel C Zielinski for their valuable feedback on this manuscript. Studies performed at UCSD were supported by National Institutes of Health Grant R01 GM057089.

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View full text

The biomass objective function

Introduction

Section snippets

Computing cellular yields and growth rates

The formulation of the biomass objective function

Brief review of studies examining cellular objective functions

Conclusions

Acknowledgements

J Theor Biol

J Theor Biol

J Biol Chem

Metab Eng

J Biol Chem

J Biotechnol

Metabolic flux balancing: basic concepts, scientific and practical use

Nat Biotechnol

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

Nat Biotechnol

Applications of genome-scale metabolic reconstructions

Mol Syst Biol

Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states

Genome Res

Reconstruction of biochemical networks in microorganisms

Nat Rev Microbiol

The model organism as a system: integrating ‘omics’ data sets

Nat Rev Mol Cell Biol

Physiology of the Bacterial Cell: A Molecular Approach

A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

Mol Syst Biol

A protocol for generating a high-quality genome-scale metabolic reconstruction

Nat Protoc

Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization

PLoS Comput Biol

Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements

Biotechnol Bioeng

Optimization-based framework for inferring and testing hypothesized metabolic objective functions

Biotechnol Bioeng

Bayesian-based selection of metabolic objective functions

Bioinformatics

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BMC Bioinformatics

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Biotechnol Prog