ArticlePLoS computational biology2018
Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.PLoS computational biology · 2025Article
- Biological and genetic determinants of glycolysis: Phosphofructokinase isoforms boost energy status of stored red blood cells and transfusion outcomes.Cell metabolism · 2024Article
- MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics.PLoS computational biology · 2021Article
- Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis.Metabolites · 2020Review
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4 authors.
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Abstract
Allosteric regulation has traditionally been described by mathematically-complex allosteric rate laws in the form of ratios of polynomials derived from the application of simplifying kinetic assumptions. Alternatively, an approach that explicitly describes all known ligand-binding events requires no simplifying assumptions while allowing for the computation of enzymatic states. Here, we employ such a modeling approach to examine the "catalytic potential" of an enzyme-an enzyme's capacity to catalyze a biochemical reaction. The catalytic potential is the fundamental result of multiple ligand-binding events that represents a "tug of war" among the various regulators and substrates within the network. This formalism allows for the assessment of interacting allosteric enzymes and development of a network-level understanding of regulation. We first define the catalytic potential and use it to characterize the response of three key kinases (hexokinase, phosphofructokinase, and pyruvate kinase) in human red blood cell glycolysis to perturbations in ATP utilization. Next, we examine the sensitivity of the catalytic potential by using existing personalized models, finding that the catalytic potential allows for the identification of subtle but important differences in how individuals respond to such perturbations. Finally, we explore how the catalytic potential can help to elucidate how enzymes work in tandem to maintain a homeostatic state. Taken together, this work provides an interpretation and visualization of the dynamic interactions and network-level effects of interacting allosteric enzymes.
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