Network inference and biological dynamics

TL;DR

This paper unifies various biological network inference methods under a common statistical framework, clarifies their differences, and evaluates their performance on dynamical models to guide future research and experimental design.

Contribution

It introduces a general formulation linking multiple network inference approaches, including existing methods, and explores their relation to cellular dynamics and data averaging.

Findings

Comparison of 32 inference methods on dynamical models

Insights into the treatment of time intervals in data

Guidance for practitioners and experimental design

Abstract

Network inference approaches are now widely used in biological applications to probe regulatory relationships between molecular components such as genes or proteins. Many methods have been proposed for this setting, but the connections and differences between their statistical formulations have received less attention. In this paper, we show how a broad class of statistical network inference methods, including a number of existing approaches, can be described in terms of variable selection for the linear model. This reveals some subtle but important differences between the methods, including the treatment of time intervals in discretely observed data. In developing a general formulation, we also explore the relationship between single-cell stochastic dynamics and network inference on averages over cells. This clarifies the link between biochemical networks as they operate at the cellular level and network inference as carried out on data that are averages over populations of cells. We present empirical results, comparing thirty-two network inference methods that are instances of the general formulation we describe, using two published dynamical models. Our investigation sheds light on the applicability and limitations of network inference and provides guidance for practitioners and suggestions for experimental design.