High-speed parameter search of dynamic biological pathways from time-course transcriptomic profiles using high-level...

Area of Science:

• Systems Biology
• Computational Biology
• Bioinformatics

Background:

• Accurate kinetic parameters are crucial for dynamic simulations of biological pathways.
• Estimating these parameters is challenging due to biological complexity and experimental limitations.
• Existing methods like genomic data assimilation (GDA) are computationally intensive and data-dependent.

Purpose of the Study:

• To develop a high-speed parameter search method for kinetic parameters in quantitative biological pathways.
• To improve the efficiency and accuracy of parameter estimation using time-course transcriptomic data.
• To address the limitations of current genomic data assimilation approaches.

Main Methods:

• A novel, high-speed parameter search method integrating Probabilistic Linear-time Temporal Logic (PLTL) model checking with GDA.
• Interactive pruning of the parameter search space using PLTL-based model checking.
• Application to quantitative biological pathways using time-course transcriptomic profiles.

Main Results:

• The proposed method demonstrates superior speed and accuracy compared to the standard GDA approach.
• Effectiveness validated on Mus musculus transcription circuits modeled using hybrid functional Petri nets.
• Achieved faster and more accurate parameter estimation for both dense and sparse time-course datasets.

Conclusions:

• The novel PLTL-based GDA method offers a significant advancement in estimating kinetic parameters for biological pathway simulations.
• This approach enhances the feasibility of dynamic simulations by overcoming computational and data acquisition challenges.
• The method provides a more efficient and accurate tool for systems biology research.