Reconstruction of Arbitrary Biochemical Reaction Networks: A Compressive Sensing Approach

TL;DR

This paper introduces a novel method for reconstructing biochemical reaction networks by formulating the problem as a compressive sensing task and employing Bayesian sparse learning to identify network structure and reaction constants from data.

Contribution

It presents a new algorithm that reconstructs nonlinear biochemical networks with polynomial and rational functions, simultaneously identifying structure and parameters.

Findings

Successfully reconstructs network structure from time-series data.

Handles polynomial and rational nonlinear models.

Employs Bayesian sparse learning for efficient solution.

Abstract

Reconstruction of biochemical reaction networks is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data quite difficult. In this paper, we present a network reconstruction algorithm that can deal with model descriptions under the form of polynomial and rational functions. Rather than identifying the parameters of linear or nonlinear ODEs characterised by pre-defined equation structures, our methodology allows us to determine the nonlinear ODEs structure together with their associated reaction constants. To solve the network reconstruction problem, we cast it as a Compressive Sensing (CS) problem and use Bayesian Sparse Learning (BSL) algorithms as an efficient way to obtain its solution.