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|Title:||A multidisciplinary survey of computational techniques for the modelling, simulation and analysis of biochemical networks||Authors:||Decraene, James
|Keywords:||DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences||Issue Date:||2010||Source:||Decraene, J., & Hinze, T. (2010). A Multidisciplinary Survey of Computational Techniques for the Modelling, Simulation and Analysis of Biochemical. Journal of Universal Computer Science, 16(9), 1152-1175.||Series/Report no.:||Journal of universal computer science||Abstract:||All processes of life are controlled by networks of interacting biochemical components. The purpose of modelling 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 organisation 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 regards to knock-out experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modelling biochemical networks is growing rapidly and the motivations behind different modelling techniques are sometimes quite distant from each other. To clarify the current context, we review several of the most popular methods and outline the strengths and weaknesses of deterministic, stochastic, probabilistic, algebraic and agent-based approaches. We then present a comparison table which allows one to identify easily key attributes for each approach such as: the granularity of representation or formulation of temporal and spatial behaviour. We describe how through the use of heterogeneous and bridging tools, it is possible to unify and exploit desirable features found in differing modelling techniques. This paper provides a comprehensive survey of the multidisciplinary area of biochemical networks modelling. By increasing the awareness of multiple complementary modelling approaches, we aim at offering a more comprehensive understanding of biochemical networks.||URI:||https://hdl.handle.net/10356/92485
|ISSN:||0948-695X||DOI:||10.3217/jucs-016-09-1152||Rights:||© 2010 Graz University of Technology, Institut für Informationssysteme und Computer Medien (IICM). This paper was published in Journal of Universal Computer Science and is made available as an electronic reprint (preprint) with permission of Graz University of Technology, Institut für Informationssysteme und Computer Medien (IICM). The paper can be found at the following official URL: http://dx.doi.org/10.1523/JNEUROSCI.0182-08.2008. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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