ReviewGenes & development2010
Classic and contemporary approaches to modeling biochemical reactions.
Review in Genes & development, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
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- A structural bio-chemo-mechanical model for vascular smooth muscle cell traction force microscopy.Biomechanics and modeling in mechanobiology · 2023Article
- Unified tumor growth mechanisms from multimodel inference and dataset integration.PLoS computational biology · 2023Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
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- Systems approaches to investigate the role of NF-κB signaling in aging.The Biochemical journal · 2022Review
- Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework.Scientific reports · 2022Article
- Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways.Neuroinformatics · 2022Review
- Can Systems Biology Advance Clinical Precision Oncology?Cancers · 2021Review
- Kynurenine pathway activation and deviation to anthranilic and kynurenic acid in fibrosing chronic graft-versus-host disease.Cell reports. Medicine · 2021Article
- Using optimal control to understand complex metabolic pathways.BMC bioinformatics · 2020Article
- Effects of spatial heterogeneity on bacterial genetic circuits.PLoS computational biology · 2020Article
- Cell signaling model for arterial mechanobiology.PLoS computational biology · 2020Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Recent interest in modeling biochemical networks raises questions about the relationship between often complex mathematical models and familiar arithmetic concepts from classical enzymology, and also about connections between modeling and experimental data. This review addresses both topics by familiarizing readers with key concepts (and terminology) in the construction, validation, and application of deterministic biochemical models, with particular emphasis on a simple enzyme-catalyzed reaction. Networks of coupled ordinary differential equations (ODEs) are the natural language for describing enzyme kinetics in a mass action approximation. We illustrate this point by showing how the familiar Briggs-Haldane formulation of Michaelis-Menten kinetics derives from the outer (or quasi-steady-state) solution of a dynamical system of ODEs describing a simple reaction under special conditions. We discuss how parameters in the Michaelis-Menten approximation and in the underlying ODE network can be estimated from experimental data, with a special emphasis on the origins of uncertainty. Finally, we extrapolate from a simple reaction to complex models of multiprotein biochemical networks. The concepts described in this review, hitherto of interest primarily to practitioners, are likely to become important for a much broader community of cellular and molecular biologists attempting to understand the promise and challenges of "systems biology" as applied to biochemical mechanisms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.