000 03206nam a22004695i 4500
001 978-3-642-30296-1
003 DE-He213
005 20200421111702.0
007 cr nn 008mamaa
008 120813s2013 gw | s |||| 0|eng d
020 _a9783642302961
_9978-3-642-30296-1
024 7 _a10.1007/978-3-642-30296-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKnabe, Johannes F.
_eauthor.
245 1 0 _aComputational Genetic Regulatory Networks: Evolvable, Self-organizing Systems
_h[electronic resource] /
_cby Johannes F. Knabe.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aX, 122 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v428
505 0 _aEvolution -- Genetic Regulatory Networks -- Biological Clocks and Differentiation -- Topological Network Analysis -- Development and Morphogenesis.
520 _aGenetic 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642302954
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v428
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-30296-1
912 _aZDB-2-ENG
942 _cEBK
999 _c55049
_d55049