Non-equilibrium thermodynamics of gene expression and transcriptional regulation

TL;DR

This paper advocates for developing a thermodynamic framework to better understand gene expression and regulation, aiming to integrate molecular biophysical data and move beyond probabilistic models.

Contribution

It proposes establishing a thermodynamic theory for gene regulation to enhance mechanistic understanding and integrate diverse molecular data.

Findings

Current methods are mostly probabilistic and hypothesis-generating.

A thermodynamic approach could provide mechanistic insights.

Potential to unify molecular data into a systemic view.

Abstract

In recent times whole-genome gene expression analysis has turned out to be a highly important tool to study the coordinated function of a very large number of genes within their corresponding cellular environment, especially in relation to phenotypic diversity and disease. A wide variety of methods of quantitative analysis have been developed to cope with high throughput data sets generated by gene expression profiling experiments. Due to the complexity associated with transcriptomics, specially in the case of gene regulation phenomena, most of these methods are of a probabilistic or statistical nature. Even if these methods have reached a central status in the development of an integrative, systematic understanding of the associated biological processes, they very rarely constitute a concrete guide to the actual physicochemical mechanisms behind biological function and the role of these methods is more on a hypotheses generating line. An important improvement could be done with the development of a thermodynamic theory for gene expression and transcriptional regulation that will build the foundations for a proper integration of the vast amount of molecular biophysical data and could lead, in the future, to a systemic view of genetic transcription and regulation.