ArticleScientific reports2021
A structural property for reduction of biochemical networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- COCOA.jl-a Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks.Bioinformatics (Oxford, England) · 2026Article
- The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026Review
- A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions.NPJ systems biology and applications · 2025Article
- Kinetic modules are sources of concentration robustness in biochemical networks.Science advances · 2025Article
- The effective deficiency of biochemical networks.Scientific reports · 2023Article
- Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony.PLoS computational biology · 2023Article
- The unraveling of balanced complexes in metabolic networks.Scientific reports · 2023Article
- The hidden simplicity of metabolic networks is revealed by multireaction dependencies.Science advances · 2022Article
- Multiscale kinetic analysis of proteins.Current opinion in structural biology · 2022Review
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4 authors.
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Abstract
Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.
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