BMNEMNMar 2, 2016

Evolving Boolean Regulatory Networks with Variable Gene Expression Times

arXiv:1603.01185v22 citations
AI Analysis

This work addresses the role of gene expression time variation in shaping regulatory networks, but it is incremental as it builds on abstract models without direct biological validation.

The paper investigated how varying gene expression times affect Boolean regulatory network dynamics, showing that non-uniform expression times can emerge through simulated evolution and benefit network behavior without considering protein function.

The time taken for gene expression varies not least because proteins vary in length considerably. This paper uses an abstract, tuneable Boolean regulatory network model to explore gene expression time variation. In particular, it is shown how non-uniform expression times can emerge under certain conditions through simulated evolution. That is, gene expression time variance appears beneficial in the shaping of the dynamical behaviour of the regulatory network without explicit consideration of protein function.

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