ArticleFrontiers in genetics2012
Reduction of dynamical biochemical reactions networks in computational biology.
Article in Frontiers in genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 106 citations in OpenAlex.
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- Enhancing viral dynamics modeling: reliable initial estimation and validity conditions for quasi-steady state approximation (QSSA).BMC biology · 2025Article
- Mathematical basis and toolchain for hierarchical optimization of biochemical networks.PLoS computational biology · 2024Article
- Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.PLoS computational biology · 2024Article
- Parameter Estimation for Kinetic Models of Chemical Reaction Networks from Partial Experimental Data of Species' Concentrations.Bioengineering (Basel, Switzerland) · 2023Article
- HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.PLoS computational biology · 2021Article
- A structural property for reduction of biochemical networks.Scientific reports · 2021Article
- Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches.Frontiers in molecular biosciences · 2021Article
- It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology.PLoS computational biology · 2020Article
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
- Deciphering noise amplification and reduction in open chemical reaction networks.Journal of the Royal Society, Interface · 2018Article
- Self-organized criticality and pattern emergence through the lens of tropical geometry.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Evaluating model reduction under parameter uncertainty.BMC systems biology · 2018Article
- Model reduction enables Turing instability analysis of large reaction-diffusion models.Journal of the Royal Society, Interface · 2018Article
- Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics.Chemical science · 2017Article
- Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.Bulletin of mathematical biology · 2017Article
- Inferring catalysis in biological systems.IET systems biology · 2016Article
- Quasisolitons in self-diffusive excitable systems, or Why asymmetric diffusivity obeys the Second Law.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler models, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state (QSS) and quasi-equilibrium approximations (QE), and provide practical recipes for model reduction of linear and non-linear networks. We also discuss the application of model reduction to the problem of parameter identification, via backward pruning machine learning techniques.
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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.