论文标题
随机建模的反应网络的可识别性
Identifiability of Stochastically Modelled Reaction Networks
论文作者
论文摘要
化学反应网络描述了生化物种之间的相互作用。一旦给出了生化系统的基本反应网络，就可以使用各种数学框架（例如连续时间马尔可夫过程）对系统动力学进行建模。在此手稿中，研究了具有给定随机系统动力学的基础网络结构的可识别性。结果表明，与关联的随机动力学相关的一些数据类型可以唯一地识别基础网络结构以及系统参数。当通过随机模拟获得给定的动态数据时，研究了提出的网络推断的准确性。
Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data is obtained via stochastic simulations.
