title: Designing a stochastic genetic switch by coupling chaos and bistability.
abstract: In stem cell differentiation, a pluripotent stem cell becomes progressively specialized and generates specific cell types through a series of epigenetic processes. How cells can precisely determine their fate in a fluctuating environment is a currently unsolved problem. In this paper, we suggest an abstract gene regulatory network to describe mathematically the differentiation phenomenon featuring stochasticity, divergent cell fates, and robustness. The network consists of three functional motifs: an upstream chaotic motif, a buffering motif of incoherent feed forward loop capable of generating a pulse, and a downstream motif which is bistable. The dynamic behavior is typically a transient chaos with fractal basin boundaries. The trajectories take transiently chaotic journeys before divergently settling down to the bistable states. The ratio of the probability that the high state is achieved to the probability that the low state is reached can maintain a constant in a population of cells with varied molecular fluctuations. The ratio can be turned up or down when proper parameters are adjusted. The model suggests a possible mechanism for the robustness against fluctuations that is prominently featured in pluripotent cell differentiations and developmental phenomena.
authors.affiliation: State Key Laboratory for Mesoscopic Physics and School of Physics, Peking University, Beijing 100871, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhao
authors.affiliation: State Key Laboratory for Mesoscopic Physics and School of Physics, Peking University, Beijing 100871, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Ouyang
authors.affiliation: State Key Laboratory for Mesoscopic Physics and School of Physics, Peking University, Beijing 100871, China.
authors.forename: Hongli
authors.initials: H
authors.lastname: Wang
mesh_terms: D017709:Fractals; D005812:Genes, Switch; D017711:Nonlinear Dynamics; D011336:Probability; D013269:Stochastic Processes; D013997:Time Factors