Computational Genetic Regulatory Networks: Evolvable, Self-organizing Systems

  • Johannes F.┬áKnabe

Part of the Studies in Computational Intelligence book series (SCI, volume 428)

Table of contents

  1. Front Matter
    Pages 1-8
  2. Johannes F. Knabe
    Pages 1-5
  3. Johannes F. Knabe
    Pages 7-18
  4. Johannes F. Knabe
    Pages 19-43
  5. Johannes F. Knabe
    Pages 45-70
  6. Johannes F. Knabe
    Pages 71-81
  7. Johannes F. Knabe
    Pages 83-100
  8. Johannes F. Knabe
    Pages 101-106
  9. Back Matter
    Pages 0--1

About this book

Introduction

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.

Keywords

Computational Genetic Regulatory Networks Computational Intelligence Evolvable Systems Self-organizing Systems

Authors and affiliations

  • Johannes F.┬áKnabe
    • 1
  1. 1., Science and Technology Research InstitutUniversity of HertfordshireHatfieldUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-30296-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-30295-4
  • Online ISBN 978-3-642-30296-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
Industry Sectors
Pharma
Automotive
Chemical Manufacturing
Biotechnology
Electronics
Telecommunications
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences