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Computational Genetic Regulatory Networks Evolvable Selforganizing Systems 1st Edition Johannes F Knabe Auth

  • SKU: BELL-4522552
Computational Genetic Regulatory Networks Evolvable Selforganizing Systems 1st Edition Johannes F Knabe Auth
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Computational Genetic Regulatory Networks Evolvable Selforganizing Systems 1st Edition Johannes F Knabe Auth instant download after payment.

Publisher: Springer-Verlag Berlin Heidelberg
File Extension: PDF
File size: 5.22 MB
Pages: 122
Author: Johannes F. Knabe (auth.)
ISBN: 9783642302954, 9783642302961, 3642302955, 3642302963
Language: English
Year: 2013
Edition: 1

Product desciption

Computational Genetic Regulatory Networks Evolvable Selforganizing Systems 1st Edition Johannes F Knabe Auth by Johannes F. Knabe (auth.) 9783642302954, 9783642302961, 3642302955, 3642302963 instant download after payment.

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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