Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
Tadamune Kaneko, Macoto Kikuchi

TL;DR
This paper introduces a novel computational approach using statistical physics techniques to study evolution, revealing that evolution enhances mutational robustness and delays the emergence of new phenotypes like bistability in gene regulatory networks.
Contribution
It presents a new method combining evolutionary simulations with Monte Carlo sampling to analyze evolution in gene regulatory networks.
Findings
Mutational robustness is higher in evolved networks than in random ensembles.
Evolution delays the emergence of bistability as a new phenotype.
Bistable networks are more mutationally fragile than non-bistable ones.
Abstract
The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To understand the particularities of this process theoretically, evolutionary simulation (ES) alone is insufficient because the outcomes of ES depend on evolutionary pathways. We need a reference system for comparison. An appropriate reference system for this purpose is an ensemble of the randomly sampled genotypes. However, generating high-fitness genotypes by simple random sampling is difficult because such genotypes are rare. In this study, we used the multicanonical Monte Carlo method developed in statistical physics to construct a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
