Mapping parameter spaces of biological switches

Autor(es)

Diegmiller, Rocky ^{[1, 2]} ; Zhang, Lun ^{[3, 4]} ; Gameiro, Marcio ^{[5, 3]} ; Barr, Justinn ^{[6, 7]} ; Alsous, Jasmin Imran ^{[1, 2, 8]} ; Schedl, Paul ^[6] ; Shvartsman, Stanislav Y. ^{[1, 6, 2, 9]} ; Mischaikow, Konstantin ^{[3, 4]}

Afiliação do(s) autor(es)

^[1] Princeton Univ, Dept Chem & Biol Engn, Princeton, NJ 08544 - USA

^[2] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08544 - USA

^[3] Rutgers State Univ, Dept Math, Piscataway, NJ 08854 - USA

^[4] Rutgers State Univ, BioMaPS Inst, Piscataway, NJ 08854 - USA

^[5] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos, SP - Brazil

^[6] Princeton Univ, Dept Mol Biol, Princeton, NJ 08544 - USA

^[7] Univ Calif San Diego, Dept Pediat, Dept Biol Sci, La Jolla, CA 92093 - USA

^[8] MIT, Dept Biol, Cambridge, MA - USA

^[9] Simons Fdn, Flatiron Inst, New York, NY 10010 - USA

• Tipo de documento: Artigo Científico
• Fonte: PLOS COMPUTATIONAL BIOLOGY; v. 17, n. 2 FEB 2021.
• Citações Web of Science: 0

Resumo

Author summary Identification of qualitatively different regimes in models of biomolecular switches is essential for understanding dynamics of complex biological processes, including symmetry breaking in cells and cell networks. We demonstrate how topological methods, symbolic computation, and numerical simulations can be combined for systematic mapping of symmetry-broken states in a mathematical model of oocyte specification in Drosophila, a leading experimental system of animal oogenesis. Our algorithmic framework reveals global connectedness of parameter domains corresponding to robust oocyte specification and enables systematic navigation through multidimensional parameter spaces in a large class of biomolecular switches. 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. (AU)

• Processo FAPESP: 19/06249-7 - Aplicações de Métodos Computacionais e Topológicos em Sistemas Dinâmicos
• Beneficiário: Marcio Fuzeto Gameiro
• Modalidade de apoio: Auxílio à Pesquisa - Regular