Stochastic and deterministic multiscale models for systems biology: An auxin-transport case study

Twycross, Jamie; Band, Leah R.; Bennett, Malcolm J.; King, John R.; Krasnogor, Natalio

Abstract

Background: Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks.Results: In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system.Conclusions: Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models. © 2010 Twycross et al; licensee BioMed Central Ltd.

Citation

Twycross, J., Band, L. R., Bennett, M. J., King, J. R., & Krasnogor, N. (2010). Stochastic and deterministic multiscale models for systems biology: An auxin-transport case study. BMC Systems Biology, 4, https://doi.org/10.1186/1752-0509-4-34

• Journal Article Type: Article
• Acceptance Date: Mar 26, 2010
• Publication Date: Mar 26, 2010
• Deposit Date: Jan 9, 2020
• Journal: BMC Systems Biology
• Electronic ISSN: 1752-0509
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 4
• DOI: https://doi.org/10.1186/1752-0509-4-34
• Public URL: https://nottingham-repository.worktribe.com/output/3093342
• Publisher URL: https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-4-34