In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

Edwards, Jeremy S.; Ibarra, Rafael U.; Palsson, Bernhard Ø.

doi:10.1038/84379

Cited by 865 publications

(674 citation statements)

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“…Once such biological information has been amassed, a mathematical approach can be used to predict metabolic behavior to the extent determined by the nature of the modeling framework. Thus flux balance analysis (FBA), [1][2][3] metabolic flux analysis (MFA), [4][5][6] and other flux-based approaches such as elementary flux modes, 7,8 which are based on steady-state assumptions, concentrate on predicting all metabolic fluxes of interest (for example, excretion rates of end-products, growth rate of biomass, exchange rate of cofactors, and so on). Dynamic approaches, on the other hand, will require dynamic measurements of various extra and intracellular variables for parameter identification.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

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Section: Introductionmentioning

confidence: 99%

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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“…Modeling regulatory circuits [29][30][31] and larger scale networks [32,33], reconstructing metabolic networks [34], investigating network robustness [35,36] and stochastic gene expression in biological systems [37], were among the necessary conditions for a full reemergence of systems biology.…”

Section: Twenty First Century Systems Biologymentioning

confidence: 99%

A unifying view of 21st century systems biology

Vidal

2009

FEBS Letters

119

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The idea that multi‐scale dynamic complex systems formed by interacting macromolecules and metabolites, cells, organs and organisms underlie some of the most fundamental aspects of life was proposed by a few visionaries half a century ago. We are witnessing a powerful resurgence of this idea made possible by the availability of nearly complete genome sequences, ever improving gene annotations and interactome network maps, the development of sophisticated informatic and imaging tools, and importantly, the use of engineering and physics concepts such as control and graph theory. Alongside four other fundamental “great ideas” as suggested by Sir Paul Nurse, namely, the gene, the cell, the role of chemistry in biological processes, and evolution by natural selection, systems‐level understanding of “What is Life” may materialize as one of the major ideas of biology.

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“…The third step is the determination of the possible solutions in this space that correspond to physiologically meaningful states. This constraint-based modeling procedure has been successfully utilized to study phenotypes in various model [15][16][17][18] and infectious [19,20] microorganisms. Recently, this approach has passed a significant milestone, namely the reconstruction of the human metabolic network [1].…”

Section: Biochemical Reaction Networkmentioning

confidence: 99%

Biochemical and statistical network models for systems biology

Price

Shmulevich

2007

Current Opinion in Biotechnology

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IntroductionThe creation of models of the integrated functions of genes and proteins in cells is of fundamental and immediate importance to the emerging field of computational systems biology. Some of the most successful attempts at cell-scale modeling to date have been based on piecing together networks that represent hundreds of experimentally-determined biochemical interactions, while others have been very successful at inferring statistical networks from large amounts of high-throughput data. These networks (metabolic, regulatory, or signaling) can be analyzed, and predictions about cellular behavior made and tested. Many types of models have been built and applied to study cellular behavior and in this review we focus on two broad types: biochemical network models and statistical inference models. Through iterative model prediction, experimentation, and network refinement, the molecular circuitry and functions of biological networks can be elucidated. The construction of genomescale models that integrate the myriad components that produce cellular behavior is a fundamental goal of systems biology today. Biochemical Reaction NetworksBiochemical reaction networks represent the underlying chemistry of the system, and thus at a minimum represent stoichiometric relationships between inter-converted biomolecules. The stoichiometry of biochemical reaction networks can now be reconstructed at the genome-scale, and at smaller scale with sufficient detail to generate kinetic models. These biochemical reaction networks represent many years of accumulated experimental data and can be interrogated in silico to determine their functional states. Genome-scale models based on biochemical networks provide a comprehensive, yet concise, description of cellular functions.For metabolism the reconstruction of the biochemical reaction network is a well-established procedure [1][2][3][4][5][6][7], while methods for the reconstruction of the associated regulatory [8,9] and signaling networks [10][11][12] with stoichiometric detail are being developed. The typically used formalism is to reconstruct the stoichiometric matrix, where each row represents a molecular compound and each column represents a reaction. For metabolism, these networks are often focused on just the metabolites, where the existence of a protein that catalyzes this reaction is used to allow that reaction to be present in the network. It is also possible (and truer to the realities in the system) to represent the proteins themselves as compounds in the network, which enables the integration of proteomics, metabolomics, and flux data (Figure 1). For regulatory and signaling networks, the inclusion of the proteins as compounds is essential. This process * To whom correspondence should be addressed: ishmulevich@sytemsbiology.org. + Present Address: Department of Chemical and Biomolecular Engineering University of Illinois, Urbana-Champaign Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our c...

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In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

Cited by 865 publications

References 40 publications

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

A unifying view of 21st century systems biology

Biochemical and statistical network models for systems biology

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