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Embodied Computational Evolution: A Model for Investigating Randomness and the Evolution of Morphological Complexity.

E Aaron^1,2,3, J H Long^2,3,4

¹Department of Computer Science, Colby College, Waterville, ME 04901, USA.

Integrative Organismal Biology (Oxford, England)

|September 23, 2024

Summary

This summary is machine-generated.

Computational models reveal how genetic and developmental randomness influence the evolution of morphological complexity. Selection on locomotor performance adaptively drives this complexity, demonstrating the interplay between randomness and adaptation in evolution.

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Area of Science:

Evolutionary Biology
Computational Biology
Developmental Biology

Background:

Understanding evolutionary dynamics across multiple levels requires advanced computational models.
Investigating the interplay of genetic and developmental randomness in evolution is crucial.

Purpose of the Study:

To present the Embodied Computational Evolution (ECE) modeling framework.
To investigate how genetic and developmental randomness drive the evolution of morphological complexity.
To test hypotheses on randomness altering selection and selection targeting complexity.

Main Methods:

Utilized the Embodied Computational Evolution (ECE) framework.
Implemented genetic (germline mutation) and developmental (transcription error) randomness.
Conducted a factorial experimental design varying randomness rates over 100 generations with directional selection for locomotor performance.

Main Results:

Variations in transcription error rates altered the dynamics of selection, supporting the first hypothesis.
Populations evolved increased morphological complexity adaptively in response to selection on locomotor performance, supporting the second hypothesis.

Conclusions:

Randomness in gene transcription significantly impacts selection dynamics.
Selection for locomotor performance effectively targets and drives the evolution of morphological complexity.