Design of experiments and biochemical network inference

TL;DR

This paper connects design of experiments and biochemical network inference by leveraging polynomial models and computational algebra to optimize information gathering in biological research.

Contribution

It introduces a novel link between experimental design and network inference using polynomial models and algebraic techniques.

Findings

Establishes a theoretical connection between the two fields.

Proposes algebraic methods for network inference.

Highlights potential for optimized experimental strategies.

Abstract

Design of experiments is a branch of statistics that aims to identify efficient procedures for planning experiments in order to optimize knowledge discovery. Network inference is a subfield of systems biology devoted to the identification of biochemical networks from experimental data. Common to both areas of research is their focus on the maximization of information gathered from experimentation. The goal of this paper is to establish a connection between these two areas coming from the common use of polynomial models and techniques from computational algebra.