GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks

TL;DR

This paper introduces a novel maximum-likelihood approach for modeling signed gene regulatory networks, providing new signage models, algorithms for parameter estimation, and validation on synthetic and real data to enhance understanding of gene regulation.

Contribution

It proposes a new class of signage models for signed networks, tailored for gene regulatory networks, and develops algorithms for maximum likelihood estimation.

Findings

Algorithms successfully estimate model parameters.

Models fit well to synthetic and real gene networks.

Potential to predict unknown gene regulations.

Abstract

Signed networks, i.e., networks with positive and negative edges, commonly arise in various domains from social media to epidemiology. Modeling signed networks has many practical applications, including the creation of synthetic data sets for experiments where obtaining real data is difficult. Influential prior works proposed and studied various graph topology models, as well as the problem of selecting the most fitting model for different application domains. However, these topology models are typically unsigned. In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signed networks given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology. Based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, the generation of biological hypotheses, and realistic GRN benchmark datasets.