A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions

TL;DR

This paper introduces a new method for reconstructing protein networks across multiple conditions, enabling better understanding of dynamic biological processes from genome-wide data.

Contribution

It presents a novel multi-condition network reconstruction formulation and an efficient integer programming solution, advancing beyond single-condition analysis methods.

Findings

Effectively captures condition-specific gene interactions.

Demonstrates improved network reconstruction over existing single-condition tools.

Reveals dynamic changes in protein networks during influenza infection.

Abstract

The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.