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An approach to evolving cell signaling networks in silico

Decraene, James and Mitchell, George G. and Kelly, Ciarán and McMullin, Barry (2006) An approach to evolving cell signaling networks in silico. In: 2006 International Workshop on Systems Biology , 17-19 July 2006, Maynooth, Ireland.

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Cell Signaling Networks(CSN) are complex bio-chemical networks which, through evolution, have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. From a computational point of view, modeling Artificial Cell Signaling Networks (ACSNs) in silico may provide new ways to design computer systems which may have specialized application areas. To investigate these new opportunities, we review the key issues of modeling ACSNs identified as follows. We first present an analogy between analog and molecular computation. We discuss the application of evolutionary techniques to evolve biochemical networks for computational purposes. The potential roles of crosstalk in CSNs are then examined. Finally we present how artificial CSNs can be used to build robust real-time control systems. The research we are currently involved in is part of the multi disciplinary EU 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. This also complements the present requirements of Computational Systems Biology by providing new insights in micro-biology research.

Item Type:Conference or Workshop Item (Poster)
Event Type:Workshop
Subjects: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:Proceedings of the first International Workshop on Systems Biology. .
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:4601
Deposited On:18 Jun 2009 09:20 by James Decraene. Last Modified 04 Nov 2016 14:05

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