Algebraic Methods for Inferring Biochemical Networks: a Maximum Likelihood Approach

TL;DR

This paper introduces an algebraic maximum likelihood method for inferring biochemical reaction networks from multiple experimental datasets, focusing on the geometric relationships of reaction rates.

Contribution

It presents a scalable algebraic statistical approach that uniquely identifies the most probable network structure using relative experimental data.

Findings

Successfully infers network structure from simulated data

Applicable to complex biochemical systems with many species and reactions

Demonstrated with a hypothetical mass transfer model

Abstract

We present a novel method for identifying a biochemical reaction network based on multiple sets of estimated reaction rates in the corresponding reaction rate equations arriving from various (possibly different) experiments. The current method, unlike some of the graphical approaches proposed in the literature, uses the values of the experimental measurements only relative to the geometry of the biochemical reactions under the assumption that the underlying reaction network is the same for all the experiments. The proposed approach utilizes algebraic statistical methods in order to parametrize the set of possible reactions so as to identify the most likely network structure, and is easily scalable to very complicated biochemical systems involving a large number of species and reactions. The method is illustrated with a numerical example of a hypothetical network arising form a "mass transfer"-type model.