标题： miRNA-蛋白切换开关的随机分析 显示英文标题

标题： Stochastic analysis of a miRNA-protein toggle switch

摘要： 在系统生物学中，人们对遗传和生化反应网络的随机行为越来越感兴趣。适当的随机描述由化学主方程提供，它表示一个连续时间马尔可夫链（CTMC）。在本文中，我们考虑了一个生化电路的随机特性，该电路已知控制真核细胞周期，并可能参与癌变，在文献中最近在一个确定性框架下被提出。由于生化过程的固有随机性和所涉及的分子数量较少，随机方法在描述真实系统方面应该更准确：我们通过探索系统参数空间来研究两种方法之间的一致性。我们通过提出模型的一个简化版本来解决这个问题，该版本允许解析处理，并对完整模型进行数值模拟。我们在大量参数范围内观察到随机描述和确定性描述之间的最佳一致性，但在至少两种情况下出现了一些实质性差异：1）当确定性系统接近从单稳态到双稳态的转变时，以及2）当双稳性（在确定性系统中）在随机系统中因分布尾部而被“掩盖”时。该方法提供了关于切换过程中涉及的最优分子数的有趣估计。我们讨论了化学主方程在系统生物学中的优点、潜力和弱点，以及与确定性建模的差异，以期为生物信息学实践者和理论科学家提供有用的建议。 显示英文摘要

摘要： Within systems biology there is an increasing interest in the stochastic behavior of genetic and biochemical reaction networks. An appropriate stochastic description is provided by the chemical master equation, which represents a continuous time Markov chain (CTMC). In this paper we consider the stochastic properties of a biochemical circuit, known to control eukaryotic cell cycle and possibly involved in oncogenesis, recently proposed in the literature within a deterministic framework. Due to the inherent stochasticity of biochemical processes and the small number of molecules involved, the stochastic approach should be more correct in describing the real system: we study the agreement between the two approaches by exploring the system parameter space. We address the problem by proposing a simplified version of the model that allows analytical treatment, and by performing numerical simulations for the full model. We observed optimal agreement between the stochastic and the deterministic description of the circuit in a large range of parameters, but some substantial differences arise in at least two cases: 1) when the deterministic system is in the proximity of a transition from a monostable to a bistable configuration, and 2) when bistability (in the deterministic system) is "masked" in the stochastic system by the distribution tails. The approach provides interesting estimates of the optimal number of molecules involved in the toggle. Our discussion of the points of strengths, potentiality and weakness of the chemical master equation in systems biology and the differences with respect to deterministic modeling are leveraged in order to provide useful advice for both the bioinformatician practitioner and the theoretical scientist.