Functional models for large-scale gene regulation networks: realism and fiction

TL;DR

This paper reviews large-scale gene regulation network models, discussing their realism, limitations, and future directions, with implications for understanding complex diseases and developing new therapeutic strategies.

Contribution

It provides a comprehensive overview of current large-scale functional network models, highlighting challenges, limitations, and future research directions in the field.

Findings

Current models face scalability and parameter proliferation issues.

Bridging topology and function remains a key challenge.

Future models should aim for increased realism and predictive power.

Abstract

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as realistic and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.