Evolution of associative learning in chemical networks

McGregor, Simon, Vasas, Vera, Husbands, Phil and Fernando, Chrisantha (2012) Evolution of associative learning in chemical networks. PLoS Computational Biology, 8 (11). e1002739. ISSN 1553-734X

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Abstract

Organisms 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.

Item Type: Article
Keywords: associative learning, chemical networks
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QH Natural history > QH0301 Biology > QH0359 Evolution
Q Science > QR Microbiology > QR0075 Bacteria
Depositing User: Phil Husbands
Date Deposited: 14 Nov 2012 14:27
Last Modified: 10 Mar 2017 16:24
URI: http://sro.sussex.ac.uk/id/eprint/41930

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