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Puzzles in modern biology. V. Why are genomes overwired?

Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simple...

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Autor principal: Frank, Steven A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580404/
https://www.ncbi.nlm.nih.gov/pubmed/28928947
http://dx.doi.org/10.12688/f1000research.11911.2
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author Frank, Steven A.
author_facet Frank, Steven A.
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description Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added. Genomes adapt to the additional complexity. The newly added factors can no longer be removed without significant loss of fitness. Alternatively, highly wired genomes may be more malleable. In large networks, most genomic variants tend to have a relatively small effect on gene expression and trait values. Many small effects lead to a smooth gradient, in which traits may change steadily with respect to underlying regulatory changes. A smooth gradient may provide a continuous path from a starting point up to the highest peak of performance. A potential path of increasing performance promotes adaptability and learning. Genomes gain by the inductive process of natural selection, a trial and error learning algorithm that discovers general solutions for adapting to environmental challenge. Similarly, deeply and densely connected computational networks gain by various inductive trial and error learning procedures, in which the networks learn to reduce the errors in sequential trials. Overwiring alters the geometry of induction by smoothing the gradient along the inductive pathways of improving performance. Those overwiring benefits for induction apply to both natural biological networks and artificial deep learning networks.
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spelling pubmed-55804042017-09-18 Puzzles in modern biology. V. Why are genomes overwired? Frank, Steven A. F1000Res Opinion Article Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added. Genomes adapt to the additional complexity. The newly added factors can no longer be removed without significant loss of fitness. Alternatively, highly wired genomes may be more malleable. In large networks, most genomic variants tend to have a relatively small effect on gene expression and trait values. Many small effects lead to a smooth gradient, in which traits may change steadily with respect to underlying regulatory changes. A smooth gradient may provide a continuous path from a starting point up to the highest peak of performance. A potential path of increasing performance promotes adaptability and learning. Genomes gain by the inductive process of natural selection, a trial and error learning algorithm that discovers general solutions for adapting to environmental challenge. Similarly, deeply and densely connected computational networks gain by various inductive trial and error learning procedures, in which the networks learn to reduce the errors in sequential trials. Overwiring alters the geometry of induction by smoothing the gradient along the inductive pathways of improving performance. Those overwiring benefits for induction apply to both natural biological networks and artificial deep learning networks. F1000Research 2017-08-14 /pmc/articles/PMC5580404/ /pubmed/28928947 http://dx.doi.org/10.12688/f1000research.11911.2 Text en Copyright: © 2017 Frank SA http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Opinion Article
Frank, Steven A.
Puzzles in modern biology. V. Why are genomes overwired?
title Puzzles in modern biology. V. Why are genomes overwired?
title_full Puzzles in modern biology. V. Why are genomes overwired?
title_fullStr Puzzles in modern biology. V. Why are genomes overwired?
title_full_unstemmed Puzzles in modern biology. V. Why are genomes overwired?
title_short Puzzles in modern biology. V. Why are genomes overwired?
title_sort puzzles in modern biology. v. why are genomes overwired?
topic Opinion Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580404/
https://www.ncbi.nlm.nih.gov/pubmed/28928947
http://dx.doi.org/10.12688/f1000research.11911.2
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