Rare-event sampling of epigenetic landscapes and phenotype transitions.
Rare-event sampling of epigenetic landscapes and phenotype transitions.
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DOI：
10.1371/journal.pcbi.1006336
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发表时间：
2018-08
影响因子：
4.3
通讯作者：
Read EL
中科院分区：
文献类型：
--
作者：
Tse MJ;Chu BK;Gallivan CP;Read EL
随机模拟已经成为研究基因调控网络动态的有力工具，特别是在理解细胞表型稳定性和命运转变如何受到噪声基因表达的影响方面。然而，基因网络通常具有多个吸引子的动力学特征。随机模拟对于这样的系统通常是低效的，因为大部分模拟时间都花在等待罕见的跨越障碍的事件发生上。我们提出了一种基于稀有事件模拟的方法，用于计算亚稳态基因网络中的表观遗传景观和表型转换。我们的计算管道受到蛋白质折叠中亚稳态和障碍交叉研究的启发，并提供了一种计算和可视化基本静态和动态信息的自动化方法，这些信息通常是传统模拟无法访问的。应用于胚胎干细胞多能性的网络模型，我们的模拟揭示了罕见的表型和表型状态之间的近似马尔可夫转换，发生在广泛的时间范围内。表型的相对概率和连接多能性和分化的过渡路径对控制转录因子-DNA结合动力学的全局动力学参数敏感。我们的方法显着扩展了随机模拟研究基因调控网络动态的能力，这可能有助于指导合理的细胞重编程策略。我们的方法也可以推广到其他类型的分子网络和随机动力学框架。细胞表型是由细胞内基因、蛋白质和其他分子之间的复杂相互作用以及来自细胞环境的信号沿着控制的。基因调控网络（GRN）以数学方式描述了这些相互作用。原则上，GRN模型可以产生可能的细胞表型和表型转换的图，可能为控制细胞表型的实验策略提供信息。这样的地图可能对许多医学领域产生深远的影响，从干细胞治疗到伤口愈合。然而，GRN模型的解析解几乎是不可能的，除了最小的网络。相反，GRN动力学的时间过程轨迹可以使用专门的算法来模拟。然而，这些方法难以研究罕见事件，例如胚胎干细胞或癌细胞中可能发生的细胞表型之间的自发转变。在本文中，我们提出了一种方法，以扩大目前的随机模拟算法的抽样罕见的表型和表型转换。计算管道的输出是一个由几个稳定表型组成的简化网络，通过具有量化概率的潜在转换连接。这个简化的网络给出了细胞表型转换动力学的直观表示，这可能有助于理解分子过程如何影响细胞反应，并有助于解释实验数据。
Stochastic simulation has been a powerful tool for studying the dynamics of gene regulatory networks, particularly in terms of understanding how cell-phenotype stability and fate-transitions are impacted by noisy gene expression. However, gene networks often have dynamics characterized by multiple attractors. Stochastic simulation is often inefficient for such systems, because most of the simulation time is spent waiting for rare, barrier-crossing events to occur. We present a rare-event simulation-based method for computing epigenetic landscapes and phenotype-transitions in metastable gene networks. Our computational pipeline was inspired by studies of metastability and barrier-crossing in protein folding, and provides an automated means of computing and visualizing essential stationary and dynamic information that is generally inaccessible to conventional simulation. Applied to a network model of pluripotency in Embryonic Stem Cells, our simulations revealed rare phenotypes and approximately Markovian transitions among phenotype-states, occurring with a broad range of timescales. The relative probabilities of phenotypes and the transition paths linking pluripotency and differentiation are sensitive to global kinetic parameters governing transcription factor-DNA binding kinetics. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which may help guide rational cell reprogramming strategies. Our approach is also generalizable to other types of molecular networks and stochastic dynamics frameworks. Cell phenotypes are controlled by complex interactions between genes, proteins, and other molecules within a cell, along with signals from the cell’s environment. Gene regulatory networks (GRNs) describe these interactions mathematically. In principle, a GRN model can produce a map of possible cell phenotypes and phenotype-transitions, potentially informing experimental strategies for controlling cell phenotypes. Such a map could have a profound impact on many medical fields, ranging from stem cell therapies to wound healing. However, analytical solution of GRN models is virtually impossible, except for the smallest networks. Instead, time course trajectories of GRN dynamics can be simulated using specialized algorithms. However, these methods suffer from the difficulty of studying rare events, such as the spontaneous transitions between cell phenotypes that can occur in Embryonic Stem Cells or cancer cells. In this paper, we present a method to expand current stochastic simulation algorithms for the sampling of rare phenotypes and phenotype-transitions. The output of the computational pipeline is a simplified network of a few stable phenotypes, linked by potential transitions with quantified probabilities. This simplified network gives an intuitive representation of cell phenotype-transition dynamics, which could be useful for understanding how molecular processes impact cellular responses and aid interpretation of experimental data.
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影响因子：
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作者：
Bhattacharya S;Zhang Q;Andersen ME
通讯作者：
Andersen ME
影响因子：
4.4
作者：
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通讯作者：
Dinner, Aaron R.
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作者：
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作者：
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通讯作者：
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影响因子：
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作者：
Donovan, Rory M.;Sedgewick, Andrew J.;Zuckerman, Daniel M.
通讯作者：
Zuckerman, Daniel M.