Parameter estimation of biological pathways using data assimilation and model checking

This paper presents a novel method to estimate kinetic parameter of biological pathways by using observed time-series data and other knowledge that cannot be formulated in the form of time-series data. Our method utilizes data assimilation (DA) framework and model checking (MC) technique, with a quantitative modeling and simulation architecture named hybrid functional Petri net with extension (HFPNe). Proposed method is applied to an HFPNe model underlying circadian rhythm in mouse. We first translate 23 rules of biological knowledge with temporal logic for the model checking, which are not described in the time-series data. Next, we employ particle filter often applied to DA for our estimation procedure. Each particle checks whether its simulation result satisfies the rules or not, and the result of the checking is used for its resampling step. Our simulation results show that proposed method is faster and more accurate than previous method.