Reverse-engineering biological networks from large data sets

TL;DR

This paper reviews the field of reverse-engineering biological networks from large datasets, highlighting its evolution, methodologies, applications, and the potential for large-scale inference to impact systems biology and medicine.

Contribution

It provides a comprehensive overview of reverse-engineering methods, applications, and discusses recent advances and challenges in large-scale biological network inference.

Findings

Network inference has become routine with high-throughput data.

Inferred networks aid in understanding system dynamics and clinical applications.

Method selection depends on the specific prediction goals.

Abstract

Much of contemporary systems biology owes its success to the abstraction of a network, the idea that diverse kinds of molecular, cellular, and organismal species and interactions can be modeled as relational nodes and edges in a graph of dependencies. Since the advent of high-throughput data acquisition technologies in fields such as genomics, metabolomics, and neuroscience, the automated inference and reconstruction of such interaction networks directly from large sets of activation data, commonly known as reverse-engineering, has become a routine procedure. Whereas early attempts at network reverse-engineering focused predominantly on producing maps of system architectures with minimal predictive modeling, reconstructions now play instrumental roles in answering questions about the statistics and dynamics of the underlying systems they represent. Many of these predictions have clinical relevance, suggesting novel paradigms for drug discovery and disease treatment. While other reviews focus predominantly on the details and effectiveness of individual network inference algorithms, here we examine the emerging field as a whole. We first summarize several key application areas in which inferred networks have made successful predictions. We then outline the two major classes of reverse-engineering methodologies, emphasizing that the type of prediction that one aims to make dictates the algorithms one should employ. We conclude by discussing whether recent breakthroughs justify the computational costs of large-scale reverse-engineering sufficiently to admit it as a mainstay in the quantitative analysis of living systems.