Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

By Hiroyuki Kurata, Quanyu Zhao, Ryuichi Okuda, Kazuyuki Shimizu. BMC Systems Biology, 2007

378 citations recorded by the source corpus.

BackgroundIn systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.ResultsTo link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.ConclusionThe ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8a73010996f5630074d549632bd191021ba92e4e

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Citations in the corpus

Generative models of the human connectome (2015, neutral): “…find that in order to accurately reproduce the connectome’s topology our models required information about node’s pairwise similarity (homophily), which agrees with earlier modeling studies of the primate connectome (Costa et al., 2007a) and human functional brain networks (Vértes et al., 2012).”
Applications of genome-scale metabolic reconstructions (2009, background): “Coupled reaction sets have been used for a variety of purposes, including prediction of novel drug targets in Mycobacterium tuberculosis (Jamshidi and Palsson, 2007).”
Conserved and variable architecture of human white matter connectivity (2011, neutral): “We in fact find that there is a strong coupling between topological structure and physical structure in the human brain, consistent with previous results in other species (Costa et al., 2007).”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, positive): “[441] proposed a computational reconstruction approach to the problem of netwo rk organization in cortical networks and showed that the organization of cortical networks is not ent ir ly determined by spatial constraints.”
Complex network measures of brain connectivity: Uses and interpretations (2010, neutral): “Preliminary steps have already been taken in this direction (Costa et al., 2007a).”
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory (2010, neutral): “A similar approach has been discussed by Costa and colleagues [61], who also employed a normalization to correct for large baseline correlations in sparse binary matrices.”
Using graph theory to analyze biological networks (2011, neutral): “For instance, it has been used to describe spatial growth in brain networks during development [91] or to predict the connectivity of primate cortical networks [92].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Since all reactions of a HCR-set changes, if any of its reactions is targeted, HCR-sets help in prioritizing potential drug target lists (Papin et al., 2004; Jamshidi & Palsson, 2007; Xi et al., 2011).”
Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis (2010, result): “For comparison, a similar study (34) using the metabolic model alone predicted phenotypes of metabolic gene KOs with an accuracy of only 55%.”
Flux balance analysis of biological systems: applications and challenges (2009, background): “…the understanding of microbial metabolism and the capabilities of a metabolic network (for metabolic engineering applications), to insights obtained from gene deletion analyses, which can be applied for the identification of potential drug targets, in case of pathogenic organisms [32, 46].”