Data-Driven Analysis of Mass-Action Kinetics

TL;DR

This paper explores data-driven methods for analyzing complex, interconnected biochemical systems governed by mass-action kinetics, addressing challenges posed by nonlinear dynamics and large-scale interconnections.

Contribution

It introduces data-driven analysis techniques for large-scale biochemical systems, providing alternatives to intractable classical analytical methods.

Findings

Data-driven methods effectively analyze complex biochemical networks.

Approach handles nonlinear dynamics and large-scale interconnections.

Provides scalable analysis tools for biochemical system control.

Abstract

The physical interconnection of spatial distributed biochemical systems has some advantages when dealing with large-scale problems that require separated agents to be con- trolled locally but with an overall objective. The analysis and control of such systems becomes a difficult task, because of the nonlinear nature of the dynamics of the individual subsystems, and the added complexity due to the interconnection. Therefore, analysis tools from a data-driven perspective are employed in contrast with the analytical classical way that becomes intractable once the subsystems grow in size or complexity.