Mapping parameter spaces of biological switches.

TitleMapping parameter spaces of biological switches.
Publication TypeJournal Article
Year of Publication2021
AuthorsDiegmiller, R, Zhang, L, Gameiro, M, Barr, J, Alsous, JImran, Schedl, P, Shvartsman, SY, Mischaikow, K
JournalPLoS Comput Biol
Date Published2021 Feb
KeywordsAlgorithms, Animals, Computer Simulation, Drosophila, Female, In Situ Hybridization, Fluorescence, Linear Models, Models, Biological, Nonlinear Dynamics, Oocytes, RNA, Messenger

<p>Since the seminal 1961 paper of Monod and Jacob, mathematical models of biomolecular circuits have guided our understanding of cell regulation. Model-based exploration of the functional capabilities of any given circuit requires systematic mapping of multidimensional spaces of model parameters. Despite significant advances in computational dynamical systems approaches, this analysis remains a nontrivial task. Here, we use a nonlinear system of ordinary differential equations to model oocyte selection in Drosophila, a robust symmetry-breaking event that relies on autoregulatory localization of oocyte-specification factors. By applying an algorithmic approach that implements symbolic computation and topological methods, we enumerate all phase portraits of stable steady states in the limit when nonlinear regulatory interactions become discrete switches. Leveraging this initial exact partitioning and further using numerical exploration, we locate parameter regions that are dense in purely asymmetric steady states when the nonlinearities are not infinitely sharp, enabling systematic identification of parameter regions that correspond to robust oocyte selection. This framework can be generalized to map the full parameter spaces in a broad class of models involving biological switches.</p>

Alternate JournalPLoS Comput Biol
PubMed ID33556054
PubMed Central IDPMC7895388
Grant ListF31 HD098835 / HD / NICHD NIH HHS / United States
R01 GM126555 / GM / NIGMS NIH HHS / United States
R01 GM134204 / GM / NIGMS NIH HHS / United States
R35 GM126975 / GM / NIGMS NIH HHS / United States