Identifying Biochemical Reaction Networks From Heterogeneous Datasets

TL;DR

This paper introduces a novel method for identifying biochemical reaction networks and their kinetic parameters by integrating heterogeneous datasets from various experimental conditions, enhancing system identification accuracy.

Contribution

The paper presents a new approach to combine diverse datasets for biochemical network identification, addressing non-identifiability issues in traditional single-dataset methods.

Findings

Effective integration of heterogeneous data improves network identification accuracy

Method reduces non-identifiability problems common in biochemical system modeling

Applicable to datasets from various experimental perturbations

Abstract

In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.