The global transcriptional regulatory network for metabolism inEscherichia coliexhibits few dominant functional states

Abstract

A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.systems biology ͉ transcriptional regulation T wo prominent paradigms in modern biology about which there has been much discussion are the reductionist and integrative (or systems) approaches to biological discovery and understanding (1-5). The utility of the systems approach is demonstrated when computational models are used to integrate large amounts of molecular data to interpret and predict the range of cellular phenotypes. We investigated the utility of the systems approach by using a genome-scale reconstruction of the integrated transcriptional regulatory and metabolic network for Escherichia coli (iMC1010 v1

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

“…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.…”

“…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.…”

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

“…The methods designed to analyze the underlying network structure of E. coli metabolism, some characterizing its interplay with regulation, have been developed to determine a number of physiological features. These features include the most probable active pathways and utilized metabolites under all possible growth conditions 67,69,73,75 , the existence of alternate optimal solutions and their physiological significance 65 , conserved intracellular pools of metabolites 68 , coupled reaction activities 66 and their relationship to gene co-expression 77 , metabolite coupling 71 , metabolite utilization 72 , the organization of metabolic networks 64, 76 , strategies for E. coli to incorporate metabolic redundancy 78 , and the dominant functional states of the network across various environments 70,74,79 . These findings are both driven by biased approaches utilizing FBA and biomass objective function optimization and by unbiased approaches such as graph-based analyses (see Fig.…”

“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

“…coli is generally viewed as having the most complete characterization of any model organism 98,99 . Due to the incorporation of thousands of metabolic interactions with relatively high reliability (e.g., 92% of the genes included in the latest reconstruction of E. coli 19 have experimentally determined annotated functions 99 , Table 1), validated genomescale reconstructions of E. coli have become popular resources for the analysis of various network properties [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] . The methods designed to analyze the underlying network structure of E. coli metabolism, some characterizing its interplay with regulation, have been developed to determine a number of physiological features.…”