An open problem in biology is to understand when particular adaptation strategies of microorganisms are selected during evolution. They range from random, bet-hedging strategies to deterministic, responsive strategies, relying on signalling circuits. We present an evolutionary model that integrates basic statistical physics of molecular circuits with fitness maximisation and information theory. This model provides an explanation for a puzzling observation on responsive strategies: the accuracy with which signalling networks track external signals seems remarkably low. Single cells often distinguish only between 2 to 4 concentration ranges, corresponding to 1 or 2 bits of mutual information between signal and response. Why did evolution lead to such low-fidelity signalling systems? Our theory offers an explanation by taking a novel perspective. It considers the fitness benefit of all signals, including those that are not sensed. We introduce a new concept, `latent information', which captures the mutual information between all non-sensed signals and the optimal response. The theory predicts that it is often evolutionarily optimal to transduce sensed signals noisily when latent information is present. It indicates that fitness can indeed be maximal when the mutual information extracted from sensed signals is not maximal, but rather has a low value of about 1 or 2 bits. Cells likely do not sense all signals because of the fitness cost of expressing idle signalling systems that consume limited biosynthetic resources. Our theory illustrates that as the total available information about the optimal behaviour decreases, the cell should trust the available information less, and gamble more.
- Downloaded 276 times
- Download rankings, all-time:
- Site-wide: 93,717
- In evolutionary biology: 4,969
- Year to date:
- Site-wide: 121,878
- Since beginning of last month:
- Site-wide: 106,099
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!