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PLoS Comput Biol. 2008 May 23;4(5):e1000086. doi: 10.1371/journal.pcbi.1000086.

Dynamic analysis of integrated signaling, metabolic, and regulatory networks.

Author information

  • 1Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, Virginia, United States of America.

Erratum in

  • PLoS Comput Biol. 2008 Jun;4(6). doi: 10.1371/annotation/5594348b-de00-446a-bdd0-ec56e70b3553. Min Lee, Jong [corrected to Lee, Jong Min].

Abstract

Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)-based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for "fast" reactions and incorporates "slow" reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporated kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here.

PMID:
18483615
PMCID:
PMC2377155
DOI:
10.1371/journal.pcbi.1000086
[PubMed - indexed for MEDLINE]
Free PMC Article
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