Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics

TL;DR

This paper introduces a novel network inference method based on biochemical kinetics and steady-state data, improving accuracy over linear models in identifying network topology in gene and protein networks.

Contribution

The paper presents a kinetic-based approach for network inference from steady-state data that does not require prior network topology or kinetic parameters.

Findings

More effective at estimating network topology than linear models.

Validated on simulated and real proteomic data.

Applicable to gene expression and proteomic steady-state data.

Abstract

Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference. Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.