Assessing biological network dynamics: comparing numerical simulations with analytical decomposition of parameter space

• Published in: NPJ systems biology and applications Vol. 9; no. 1; p. 29
• Main Authors: Hari, Kishore, Duncan, William, Ibrahim, Mohammed Adil, Jolly, Mohit Kumar, Cummins, Breschine, Gedeon, Tomas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03-07-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/553/2393; 631/553/2710; Agreements; Behavior; Bioinformatics; Biology; Biomedical and Life Sciences; Boolean; Cell cycle; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Simulation; Correspondence; Decision making; Decomposition; Experimental methods; Gene Regulatory Networks - genetics; Life Sciences; Mathematical models; Models, Theoretical; Ordinary differential equations; Research methodology; Simulation; Statistical analysis; Systems Biology
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/s41540-023-00289-2

Mathematical modeling of the emergent dynamics of gene regulatory networks (GRN) faces a double challenge of (a) dependence of model dynamics on parameters, and (b) lack of reliable experimentally determined parameters. In this paper we compare two complementary approaches for describing GRN dynamics across unknown parameters: (1) parameter sampling and resulting ensemble statistics used by RACIPE (RAndom CIrcuit PErturbation), and (2) use of rigorous analysis of combinatorial approximation of the ODE models by DSGRN (Dynamic Signatures Generated by Regulatory Networks). We find a very good agreement between RACIPE simulation and DSGRN predictions for four different 2- and 3-node networks typically observed in cellular decision making. This observation is remarkable since the DSGRN approach assumes that the Hill coefficients of the models are very high while RACIPE assumes the values in the range 1-6. Thus DSGRN parameter domains, explicitly defined by inequalities between systems parameters, are highly predictive of ODE model dynamics within a biologically reasonable range of parameters.