Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems (Record no. 55049)

000 -LEADER
fixed length control field 03206nam a22004695i 4500
001 - CONTROL NUMBER
control field 978-3-642-30296-1
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421111702.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120813s2013 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783642302961
-- 978-3-642-30296-1
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Knabe, Johannes F.
245 10 - TITLE STATEMENT
Title Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems
300 ## - PHYSICAL DESCRIPTION
Number of Pages X, 122 p.
490 1# - SERIES STATEMENT
Series statement Studies in Computational Intelligence,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Evolution -- Genetic Regulatory Networks -- Biological Clocks and Differentiation -- Topological Network Analysis -- Development and Morphogenesis.
520 ## - SUMMARY, ETC.
Summary, etc 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-30296-1
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2013.
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-- txt
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence (incl. Robotics).
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1860-949X ;
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-- ZDB-2-ENG

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