Parameter inference of Chemical Reaction Networks based on high-frequency observations of species copy numbers

Chemical Reaction Networks provide a fundamental framework for modeling the stochastic dynamics of biochemical systems, where molecular species evolve through discrete and random noise reaction events. Parameter inference in Chemical Reaction Networks is a central prob- lem in systems biology, but traditional methods such as maximum likelihood estimation are often intractable due to computational complexity and the lack of continuous-time data. In this study, we introduce a statistically grounded and computationally eﬃcient estimator for reac- tion rate parameters using high-frequency discrete-time observations. Modeling the system as a Continuous-Time Markov Chain, our method handles general kinetics, including non-mass- action and higher-order reactions. Validation on synthetic and experimental datasets demon- strates its accuracy and robustness. This approach oﬀers a simple and reliable framework for parameter inference in complex stochastic systems.

Parameter inference of Chemical Reaction Networks based on high-frequency observations of species copy numbers

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Jinyoung Kim

"Parameter inference of Chemical Reaction Networks based on high-frequency observations of species copy numbers"

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