High-speed parameter search of dynamic biological pathways from time-course transcriptomic profiles using high-level Petri net. (March 2021)

• Record Type: Journal Article
• Authors: Li, Chen; Qin, Jiale; Kuroyanagi, Keisuke; Lu, Lu; Nagasaki, Masao; Satoru, Miyano
• Is Part Of: Bio systems. Volume 201(2021)
• Publication Date: 2021-03
• Subjects: Parameter search -- Dynamic simulation -- Data assimilation -- Particle filter -- Model checking -- Kinetics; Biological systems -- Periodicals; Biology -- Periodicals; Biology -- Periodicals; Evolution -- Periodicals; Biologie -- Périodiques; Évolution -- Périodiques; 570
• DOI: 10.1016/j.biosystems.2020.104332
• Languages: English
• ISSNs: 0303-2647

Abstract

Abstract: Dynamic simulation promises a deeper understanding of complex molecular mechanisms of biological pathways. How to determine the reaction kinetic parameters which govern the simulation results is still an open question in the field of systems biology. (1) Background: To execute simulation experiments, it is an essential first step to search effective values of model parameters. The complexity of biological systems and the experimental measurement technology severely limit the acquirement of accurate kinetic parameters. Previously proposed genomic data assimilation (GDA) approach enables users to handle parameter estimation using time-course information. However, it highly depends on successive time points and costs massive computational resource; (2) Methods: To address this problem, we present a new high-speed parameter search method for estimating the kinetic parameters of quantitative biological pathways using time-course transcriptomic profiles. The key idea of our method is to interactively prune the search space by introducing Probabilistic Linear-time Temporal Logic (PLTL) based model checking into GDA. (3) Results and conclusion: We demonstrated the effectiveness of our method by comparing with GDA on Mus musculus transcription circuits modelled by hybrid functional Petri net with extension. As a result, our method works faster and more accurate than GDA for both time-course datasets with dense and sparse observed values.