Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems

Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems
Author :
Publisher : Springer
Total Pages : 125
Release :
ISBN-10 : 9783642302961
ISBN-13 : 3642302963
Rating : 4/5 (963 Downloads)

Book Synopsis Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems by : Johannes F. Knabe

Download or read book Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems written by Johannes F. Knabe and published by Springer. This book was released on 2012-08-14 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting from a single cell interacting with its environment, eventually including a changing local neighbourhood of other cells. These methods may help us understand the genesis, organization, adaptive plasticity, and evolvability of differentiated biological systems, and may also provide a paradigm for transferring these principles of biology's success to computational and engineering challenges at a scale not previously conceivable.


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