Modelling Cellular Signalling Systems
Overview
Authors
Affiliations
Cell signalling pathways and networks are complex and often non-linear. Signalling pathways can be represented as systems of biochemical reactions that can be modelled using differential equations. Computational modelling of cell signalling pathways is emerging as a tool that facilitates mechanistic understanding of complex biological systems. Mathematical models are also used to generate predictions that may be tested experimentally. In the present chapter, the various steps involved in building models of cell signalling pathways are discussed. Depending on the nature of the process being modelled and the scale of the model, different mathematical formulations, ranging from stochastic representations to ordinary and partial differential equations are discussed. This is followed by a brief summary of some recent modelling successes and the state of future models.
Visioli G, Romaniello A, Spinoglio L, Albanese G, Iannetti L, Gagliardi O Int J Mol Sci. 2024; 25(20).
PMID: 39456855 PMC: 11507981. DOI: 10.3390/ijms252011074.
Gharib E, Robichaud G Int J Mol Sci. 2024; 25(17).
PMID: 39273409 PMC: 11395697. DOI: 10.3390/ijms25179463.
Multiscale Modeling of Bistability in the Yeast Polarity Circuit.
Hladyshau S, Guan K, Nivedita N, Errede B, Tsygankov D, Elston T Cells. 2024; 13(16).
PMID: 39195248 PMC: 11352540. DOI: 10.3390/cells13161358.
A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.
Ma C, Gurkan-Cavusoglu E NPJ Syst Biol Appl. 2024; 10(1):71.
PMID: 38969664 PMC: 11226463. DOI: 10.1038/s41540-024-00397-7.
Bayesian parameter estimation for dynamical models in systems biology.
Linden N, Kramer B, Rangamani P PLoS Comput Biol. 2022; 18(10):e1010651.
PMID: 36269772 PMC: 9629650. DOI: 10.1371/journal.pcbi.1010651.