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The evolution of phenotypic correlations and “developmental memory”

The evolution of phenotypic correlations and “developmental memory”
The evolution of phenotypic correlations and “developmental memory”
Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time.
0014-3820
1124-1138
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Wagner, Gunter
c35d4aac-411a-439c-bf6e-849509a95673
Pavlicev, Mihaela
77905b12-c44f-4b68-9a82-4b235c6ae405
Weinreich, Daniel M.
a5522cf4-5a31-4e13-9934-e3529fdd4d6f
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Watson, Richard A.
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Wagner, Gunter
c35d4aac-411a-439c-bf6e-849509a95673
Pavlicev, Mihaela
77905b12-c44f-4b68-9a82-4b235c6ae405
Weinreich, Daniel M.
a5522cf4-5a31-4e13-9934-e3529fdd4d6f
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7

Watson, Richard A., Wagner, Gunter, Pavlicev, Mihaela, Weinreich, Daniel M. and Mills, Rob (2014) The evolution of phenotypic correlations and “developmental memory”. Evolution, 68 (4), 1124-1138. (doi:10.1111/evo.12337).

Record type: Article

Abstract

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time.

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Accepted/In Press date: 22 November 2013
e-pub ahead of print date: 1 February 2014
Published date: April 2014
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 396725
URI: http://eprints.soton.ac.uk/id/eprint/396725
ISSN: 0014-3820
PURE UUID: ade4d801-2ef6-4b1c-afc3-e5046a5e1f21

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Date deposited: 13 Jun 2016 14:22
Last modified: 16 Dec 2019 19:52

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Contributors

Author: Richard A. Watson
Author: Gunter Wagner
Author: Mihaela Pavlicev
Author: Daniel M. Weinreich
Author: Rob Mills

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