Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems (Record no. 55049)
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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 |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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-- | text file |
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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 ; |
912 ## - | |
-- | ZDB-2-ENG |
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