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Evolving artificial cell signaling networks using molecular classifier systems

Decraene, James and Mitchell, George G. and McMullin, Barry (2006) Evolving artificial cell signaling networks using molecular classifier systems. In: BIONETICS '06: The 1st international conference on Bio inspired models of network, information and computing systems, 11-13 December 2006, Cavalese, Italy. ISBN 1-4244-0539-4

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Abstract

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. Our abstraction of Cell Signaling Networks focuses 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. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. 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:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Algorithms
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)
Published in:BIONETICS '06: Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems. . Institute of Electrical and Electronics Engineers. ISBN 1-4244-0539-4
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/BIMNICS.2006.361815
Copyright Information:©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ID Code:4591
Deposited On:08 Jun 2009 12:20 by James Decraene. Last Modified 08 Jun 2009 12:20

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