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A multidisciplinary survey of modeling techniques for biochemical networks

Decraene, James and Dittrich, Peter and Hinze, Thomas and Lenser, Thorsten and McMullin, Barry and Mitchell, George G. (2006) A multidisciplinary survey of modeling techniques for biochemical networks. In: IPG 2006: Integrative Post-Genomics conference 2006, 29 Nov-1Dec 2006, Lyon, France.

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All processes of life are dominated by networks of interacting biochemical components. The purpose of modeling these networks is manifold. From a theoretical point of view it allows the exploration of network structures and dynamics, to find emergent properties or to explain the organization and evolution of networks. From a practical point of view, in silico experiments can be performed that would be very expensive or impossible to achieve in the laboratory, such as hypothesis-testing with regard to knockout experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modeling biochemical networks is growing rapidly and the motivations behind different modeling techniques are sometimes quite distant from each other. To clarify the current context, we present a systematic overview of the different philosophies to model biochemical networks. We put particular emphasis on three main domains which have been playing a major role in the past, namely: mathematics with ordinary and partial differential equations, statistics with stochastic simulation algorithms, Bayesian networks and Markov chains, and the field of computer science with process calculi, term rewriting systems and state based systems. For each school, we evaluate advantages and disadvantages such as the granularity of representation, scalability, accessibility or availability of analysis tools. Following this, we describe how one can combine some of those techniques and thus take advantages of several techniques through the use of bridging tools. Finally, we propose a next step for modeling biochemical networks by using artificial chemistries and evolutionary computation. This work was funded by ESIGNET (Evolving Cell Signaling Networks in Silico), an European Integrated Project in the EU FP6 NEST Initiative (contract no. 12789).

Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Subjects:Biological Sciences > Bioinformatics
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 third Integrative Post-Genomics2006. .
Official URL:
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:4602
Deposited On:09 Jun 2009 09:33 by James Decraene. Last Modified 13 Jan 2017 13:19

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