journal.pcbi.1002739.pdf (1.26 MB)
Evolution of associative learning in chemical networks
journal contribution
posted on 2023-06-08, 13:25 authored by Simon McGregor, Vera Vasas, Phil HusbandsPhil Husbands, Chrisantha FernandoOrganisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ’memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells.
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Publication status
- Published
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- Published version
Journal
PLoS Computational BiologyISSN
1553-734XPublisher
Public Library of ScienceExternal DOI
Issue
11Volume
8Article number
e1002739Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2012-11-14First Open Access (FOA) Date
2012-11-14First Compliant Deposit (FCD) Date
2012-11-02Usage metrics
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