Integrating metabolic, transcriptional regulatory and signal transduction models in<i>Escherichia coli</i>

Covert, Markus W.; Xiao, Nan; Chen, Tiffany J.; Karr, Jonathan R.

doi:10.1093/bioinformatics/btn352

Cited by 285 publications

(219 citation statements)

References 22 publications

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“…However, the accuracy of FBA in predicting the metabolic fluxes is limited due to the incomplete information available; e.g., there is broad flux solution space causing multiple intracellular flux solutions for a given optimal objective state. The key issue to overcome such problems is to reduce the flux solution space of the genome-scale model by using additional constraints (22)(23)(24)(25)(26)(27)(28)(29)(30)(31). Even though these constraints are invaluable in improving the prediction, they require complex information, such as transcriptional regulation and a signaling mechanism, and are condition dependent.…”

Section: Assessment Of the Prediction Accuracy Of Fba With Grouping Rmentioning

confidence: 99%

“…Other constraints that can be applied include transcriptome data under various environmental changes (22)(23)(24)(25)(26), thermodynamic constraints (27)(28)(29)(30), and molecular crowding of various biomolecules in limited cytoplasmic space (31). However, to apply these condition-dependent and objective function-specific constraints to the models, biologically meaningful knowledge and information on environmental and genetic conditions are required.…”

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confidence: 99%

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Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

Park

Kim

Lee

2010

Proc. Natl. Acad. Sci. U.S.A.

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Flux balance analysis (FBA) of a genome-scale metabolic model allows calculation of intracellular fluxes by optimizing an objective function, such as maximization of cell growth, under given constraints, and has found numerous applications in the field of systems biology and biotechnology. Due to the underdetermined nature of the system, however, it has limitations such as inaccurate prediction of fluxes and existence of multiple solutions for an optimal objective value. Here, we report a strategy for accurate prediction of metabolic fluxes by FBA combined with systematic and conditionindependent constraints that restrict the achievable flux ranges of grouped reactions by genomic context and flux-converging pattern analyses. Analyses of three types of genomic contexts, conserved genomic neighborhood, gene fusion events, and co-occurrence of genes across multiple organisms, were performed to suggest a group of fluxes that are likely on or off simultaneously. The flux ranges of these grouped reactions were constrained by flux-converging pattern analysis. FBA of the Escherichia coli genome-scale metabolic model was carried out under several different genotypic (pykF, zwf, ppc, and sucA mutants) and environmental (altered carbon source) conditions by applying these constraints, which resulted in flux values that were in good agreement with the experimentally measured 13 C-based fluxes. Thus, this strategy will be useful for accurately predicting the intracellular fluxes of large metabolic networks when their experimental determination is difficult.Escherichia coli | flux balance analysis | grouping reaction constraints | 13 C-based flux | genome-scale metabolic model T he accumulation of omics data, including genome, transcriptome, proteome, metabolome, and fluxome, provides an opportunity to understand cellular physiology and characteristics at multiple levels (1, 2). Among them, the fluxome profiling allows quantification of metabolic fluxes, which collectively represent the cellular metabolic characteristics. For successful metabolic engineering, it is important to understand and predict the changes of metabolic fluxes after modifying interesting genes, pathways, and regulatory circuits. On the basis of systems-level understanding of cellular status through fluxome profiling, rational and systematic development of an improved strain becomes possible (3-6).One of the popular methods that has been used to examine cellular metabolic fluxes and predict their changes in perturbed conditions is flux balance analysis (FBA) (7-12). To calculate the optimal flux distribution in the multidimensional flux solution space of metabolic network, FBA optimizes an objective function, such as maximizing cell growth rate, under pseudosteady state with mass balance and other constraints (13). Given the fact that the information used to reconstruct the metabolic networks is incomplete, multiple equivalent solutions can be computed for the states of these networks (14). One method that has been presented to overcome the problem of mul...

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Section: Assessment Of the Prediction Accuracy Of Fba With Grouping Rmentioning

confidence: 99%

mentioning

confidence: 99%

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

Park

Kim

Lee

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Thus, large-scale metabolic (FBA) and regulatory network (Boolean) models have been used as a scaffold with which ODE-based models can be integrated to study detailed models of sub-cellular networks in the context of their global effects (Covert et al, 2008;Lee et al, 2008). In this article we integrate two dynamic models in the regulatory and metabolic cellular level to describe the mxylene degrading behaviour of P. putida.…”

Section: Discussionmentioning

confidence: 99%

Linking genes to microbial growth kinetics—An integrated biochemical systems engineering approach

Koutinas

Kiparissides

Silva‐Rocha

et al. 2011

Metabolic Engineering

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“…E. coli). Scientists build models (11,23,24,29,31) to be able to predict how fast the microbe will grow on various sources of food, as well as how its growth changes in different conditions.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Techniques for Optimization of anE. ColiCultivation Model

Fidanova

Roeva

2013

Biotechnology & Biotechnological Equipment

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Integrating metabolic, transcriptional regulatory and signal transduction models inEscherichia coli

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 285 publications

References 22 publications

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses

Linking genes to microbial growth kinetics—An integrated biochemical systems engineering approach

Metaheuristic Techniques for Optimization of anE. ColiCultivation Model

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