生化反应Markov模型的参数辨识
Parameter Identification for Markov Models of Biochemical Reactions
摘要：
提出了一种生物过程马尔可夫模型中参数推断的数值方法。基于一个过程的时间序列数据，我们通过最大化数据的可能性来估计动力学速率常数。的可能性的计算依赖于马尔可夫模型的离散状态空间的动态抽象，成功地缓解了状态空间的大的问题。我们比较了我们的方法的两个变种，以国家的最先进的，最近发表的方法，并证明了他们的实用性和效率的几个案例研究从系统生物学。
英文摘要：
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.
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