Parameter Identification for Markov Models of Biochemical Reactions

TL;DR

This paper introduces a numerical method for estimating kinetic parameters in Markov models of biological processes, effectively handling large state spaces by dynamic abstraction and demonstrating improved efficiency over existing methods.

Contribution

The paper presents a novel likelihood-based parameter inference technique that uses dynamic state space abstraction to improve computational efficiency in Markov models of biochemical reactions.

Findings

Method outperforms existing approaches in case studies

Efficient handling of large state spaces

Applicable to various biological systems

Abstract

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.