Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales
Abstract
All biological systems are subject to perturbations: due to thermal fluctuations, external environments, or mutations. Yet, while biological systems are composed of thousands of interacting components, recent high-throughput experiments show that their response to perturbations is surprisingly low-dimensional: confined to only a few stereotyped changes out of the many possible. Here, we explore a unifying dynamical systems framework - soft modes - to explain and analyze low-dimensionality in biology, from molecules to eco-systems. We argue that this one framework of soft modes makes non-trivial predictions that generalize classic ideas from developmental biology to disparate systems, namely: phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.
Cite
@article{arxiv.2412.13637,
title = {Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales},
author = {Christopher Joel Russo and Kabir Husain and Arvind Murugan},
journal= {arXiv preprint arXiv:2412.13637},
year = {2024}
}