rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems
  name:
    name part: Knabe, Johannes F.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 122 p. online resource.
  abstract: 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.
  table of contents: Evolution -- Genetic Regulatory Networks -- Biological Clocks and Differentiation -- Topological Network Analysis -- Development and Morphogenesis.
  note: by Johannes F. Knabe.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Computational Intelligence, 428
  identifier: 9783642302961
  identifier: http://dx.doi.org/10.1007/978-3-642-30296-1
  record info:
    record creation date: 120813
    record change date: 20151125201055.0
    record identifier: 978-3-642-30296-1