Exploiting network topology for large-scale inference of nonlinear reaction models

Area of Science:

• Computational Chemistry
• Systems Biology
• Chemical Engineering

Background:

• Chemical reaction models are crucial for predicting phenomena across diverse scientific fields.
• Data-driven approaches, particularly Bayesian inference, are vital for constructing these models.
• Traditional Bayesian methods struggle with the combinatorial complexity of large nonlinear reaction networks.

Purpose of the Study:

• To develop computationally efficient methods for large-scale nonlinear reaction network inference.
• To enable data-driven discovery of model structure and parameters for complex chemical systems.
• To overcome the limitations of traditional Bayesian inference in complex network modeling.

Main Methods:

• Utilizing network topology to enhance reversible-jump Markov chain Monte Carlo (RJMCMC) 'between-model' proposals.
• Implementing sensitivity-based move type determination for improved sampling efficiency.
• Applying network-aware proposals in conjunction with sensitivity analysis for robust inference.

Main Results:

• Demonstrated tractability of large-scale nonlinear network inference through novel computational methods.
• Achieved significant gains in sampling performance by combining network topology and sensitivity analysis.
• Successfully applied the algorithms to inference problems in systems biology involving nonlinear differential equations.

Conclusions:

• The developed computational methods make large-scale nonlinear network inference feasible.
• The integration of network topology and sensitivity analysis offers a powerful approach for model discovery.
• These advancements facilitate a deeper understanding and prediction of complex chemical interactions in systems biology and beyond.