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A generative bias towards average complexity in artificial cell lineages.

Rolf Lohaus¹, Nicholas L Geard, Janet Wiles

¹Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204-5001, USA.

Proceedings. Biological Sciences

|May 3, 2007

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Generative bias alone does not explain evolutionary increases in phenotypic complexity. A computational model suggests selective biases play a critical role in driving and maintaining complexity.

Area of Science:

Evolutionary biology
Developmental biology
Computational modeling

Background:

Life's evolution shows a trend toward increased phenotypic complexity.
Natural selection is a proposed driver, but generative bias is an alternative hypothesis.

Purpose of the Study:

To computationally model a developmental system and test if generative bias drives increased phenotypic complexity.
To analyze mutational effects on complexity in generated organisms.

Main Methods:

Developed a computational model of a developmental system.
Generated random organisms with varying phenotypic complexity.
Analyzed distributions of mutational effects on complexity.

Main Results:

Related Experiment Videos

Highly complex organisms are generatable, but complexity measures have trade-offs.
Generative bias favors higher complexity only in the simplest phenotypes.
Generative bias favors lower complexity in highly complex phenotypes.

Conclusions:

Generative biases alone are insufficient to explain long-term evolutionary increases in phenotypic complexity.
A generative bias towards average complexity suggests selective biases are crucial for driving and maintaining complexity.