2012/01/01 by Ovidiu Radulescu, Alexander N. Gorban, Andreï Zinovyev +3 · 111 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · #Bioinformatics #Biological system #Biology #Computer science #Dynamical systems theory #Gene Regulatory Network Analysis #Gene regulatory network #Mathematics #Microbial Metabolic Engineering and Bioproduction #Modelling biological systems #Nonlinear system #Parametric statistics #Physics #Protein Structure and Dynamics #Pruning #Reduction (mathematics) #Statistical physics #Systems biology #Theoretical computer science #physics.chem-ph #q-bio.MN
paper · pdf · doi:10.3389/fgene.2012.00131
published in Frontiers in Genetics 3, 131 (Frontiers Media)
openalex publication_date 2012/01/01 · arxiv created 2012/05/13 · arxiv updated 2014/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.