Abstract
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.
Highlights
In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions
The power law formula uses the change in an enzyme activity profile between wild-type and a mutant to calculate the elementary mode coefficients (EMCs) of the mutant, thereby simulating the flux distribution of the mutant
Enzyme Control Flux (ECF) algorithm Elementary mode analysis Biological networks can be represented by a stoichiometric matrix (S)
Summary
Network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. A mathematical approach is required to integrate heterogeneous data, such as transcriptome, proteome, metabolome, and fluxome, to build comprehensive metabolic models. Network-based pathway analysis becomes a core method for constructing a mathematical model that predicts the flux distribution for large-scale metabolic networks. Recent network-based metabolic pathway analysis has focused on two approaches, elementary modes (EMs) [5] and extreme pathways[6]. Both consist of a convex set of vectors used to characterize all steady-state flux distributions of a biochemical network. Elementary mode or extreme pathway analysis enables an understanding of a large-scale network, predicting optimal or suboptimal metabolic fluxes under constrained conditions
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