Tse Margaret J, Chu Brian K, Roy Mahua, Read Elizabeth L
Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, California.
Department of Chemical Engineering and Materials Science, University of California Irvine, Irvine, California; Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, California.
Biophys J. 2015 Oct 20;109(8):1746-57. doi: 10.1016/j.bpj.2015.08.035.
Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks.
基因调控网络是多稳态动力系统,其中吸引子状态代表细胞表型。这些状态之间自发的、由噪声诱导的转变被认为是关键细胞过程的基础,包括细胞发育命运决定、波动环境中的表型可塑性以及致癌作用。因此,人们对开发理论和计算方法的兴趣日益浓厚,这些方法能够揭示多稳态基因网络中这些随机状态转变的动力学。我们应用了一种数值稀有事件采样算法,来研究一种普遍存在的基因调控网络基序——双稳态拨动开关的自发噪声诱导切换的转变路径,在该基序中,两个相互抑制的基因争夺主导表达。我们发现该方法能够有效地揭示详细的切换机制,这些机制涉及DNA调控位点占有率和蛋白质产物拷贝数的波动。此外,我们表明,控制调控蛋白与DNA结合和解离的速率参数对切换机制有强烈影响。在DNA结合/解离动力学缓慢的情况下,自发切换相对频繁发生,并且主要由DNA位点占有率的波动驱动。相反,在DNA结合/解离动力学快速的情况下,切换很少发生,并且由表达蛋白水平的波动驱动。我们的结果证明了自发的细胞表型转变如何涉及调控蛋白和DNA的集体行为。因此,能够模拟许多系统变量动态的计算方法非常适合探索基因网络中的动态机制。