标题： 马尔可夫模型的参数识别用于生化反应 显示英文标题

标题： Parameter Identification for Markov Models of Biochemical Reactions

摘要： 我们提出一种用于生物过程马尔可夫模型参数推断的数值技术。 基于过程的时间序列数据，我们通过最大化数据的似然来估计动力学速率常数。 似然的计算依赖于马尔可夫模型离散状态空间的动态抽象，这成功缓解了状态空间过大的问题。 我们将方法的两种变体与最新发表的现有方法进行比较，并在系统生物学的几个案例研究中展示了它们的有效性和效率。 显示英文摘要

摘要： We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a process we estimate the kinetic rate constants by maximizing the likelihood of the data. The computation of the likelihood relies on a dynamic abstraction of the discrete state space of the Markov model which successfully mitigates the problem of state space largeness. We compare two variants of our method to state-of-the-art, recently published methods and demonstrate their usefulness and efficiency on several case studies from systems biology.