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Evolving artificial cell signaling networks: perspectives and methods

Decraene, James and Mitchell, George G. and McMullin, Barry (2007) Evolving artificial cell signaling networks: perspectives and methods. In: Dressler, Falko and Carreras, Iacopo, (eds.) Advances in Biologically Inspired Information Systems. Studies in Computational Intelligence, 69 . Springer Berlin / Heidelberg, pp. 165-184. ISBN 978-3-540-72692-0

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Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. In this paper we introduce an abstraction of Cell Signaling Networks focusing on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. Following this we describe a novel class of Artificial Chemistry named Molecular Classifier Systems (MCS) to simulate ACSNs. The MCS can be regarded as a special purpose derivation of Hollands Learning Classifier System (LCS). We propose an instance of the MCS called the MCS.b that extends the precursor of the LCS: the broadcast language. We believe the MCS.b can offer a general purpose tool that can assist in the study of real CSNs in Silico The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.

Item Type:Book Section
Additional Information:The original publication is available at
Subjects:Computer Science > Artificial intelligence
Engineering > Artificial life
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Research Institute for Networks and Communications Engineering (RINCE)
Publisher:Springer Berlin / Heidelberg
Official URL:
Copyright Information:Copyright Springer-Verlag Berlin Heidelberg 2007
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:4588
Deposited On:08 Jun 2009 12:09 by James Decraene. Last Modified 08 Jun 2009 12:09

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