Abstract
Cellular systems shift metabolic states by adjusting gene expression and enzyme activities to adapt to physiological and environmental changes. Biochemical and genetic studies are identifying how metabolic regulation affects the selection of metabolic phenotypes. However, how metabolism influences its regulatory architecture still remains unexplored. We present a new method of extreme pathway analysis (the minimal set of conically independent metabolic pathways) to deduce regulatory structures from pure pathway information. Applying our method to metabolic networks of human red blood cells and Escherichia coli, we shed light on how metabolic regulation are organized by showing which reactions within metabolic networks are more prone to transcriptional or allosteric regulation. Applied to a human genome-scale metabolic system, our method detects disease-associated reactions. Thus, our study deepens the understanding of the organizing principle of cellular metabolic regulation and may contribute to metabolic engineering, synthetic biology, and disease treatment.
Highlights
Organisms are constantly faced with variable internal physiological states and environmental conditions
The results demonstrate a significant correlation between the topology of a metabolic network and its regulatory architecture
The resulting equivalent reaction set on Shannon entropy (EqSet) sequence is evaluated by a p-value, which is the probability that the rank summation of the regulatory EqSets on a random sequence is lower than that on the sequence given by our algorithm. (See Methods and Statement I in S1 Text)
Summary
Organisms are constantly faced with variable internal physiological states and environmental conditions. It is well known that cellular response to internal and environmental perturbations is often reflected and/or mediated through changes in metabolism [3] Such metabolic changes are often accomplished through both genetic and post-transcriptional controls, such as transcriptional regulation of gene expression and allosteric regulation of enzymes [4, 5]. Incorporating regulatory rules into constraint-based metabolic models allows researchers to more accurately predict the metabolic phenotype under different environmental and genetic perturbations [7,8,9,10,11,12,13,14,15,16].
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