A Dimension Reduction Method for Inferring Biochemical Networks

TL;DR

This paper introduces a dimension reduction technique using principal component analysis to improve the inference of biochemical reaction networks, especially for networks with lower-dimensional stoichiometric spaces, enhancing existing algebraic-statistical methods.

Contribution

It extends previous network inference methods by incorporating a preprocessing step that identifies relevant subspaces, enabling analysis of non-full-dimensional biochemical networks.

Findings

Effective dimension reduction via PCA improves network inference accuracy.

Method successfully applied to simulated biochemical network examples.

Enhanced algorithm handles networks with smaller stoichiometric spaces.

Abstract

We present herein an extension of an algebraic statistical method for inferring biochemical reaction networks from experimental data, proposed recently in [3]. This extension allows us to analyze reaction networks that are not necessarily full-dimensional, i.e., the dimension of their stoichiometric space is smaller than the number of species. Specifically, we propose to augment the original algebraic-statistical algorithm for network inference with a preprocessing step that identifies the subspace spanned by the correct reaction vectors, within the space spanned by the species. This dimension reduction step is based on principal component analysis of the input data and its relationship with various subspaces generated by sets of candidate reaction vectors. Simulated examples are provided to illustrate the main ideas involved in implementing this method, and to asses its performance.