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The LNCS journal Transactions on Computational Systems Biology is devoted to inter- and multidisciplinary research in the fields of computer science and life sciences and supports a paradigmatic shift in the techniques from computer and information science to cope with the new challenges arising from the systems oriented point of view of biological phenomena.
This, the 14th Transactions on Computational Systems Biology volume, guest edited by Ion Petre and Erik de Vink, focuses on Computational Models for Cell Processes and features a number of carefully selected and enhanced contributions, initially presented at the CompMod workshop, which took place in Aachen, Germany, in September 2011. The papers, written from different points of view and following various approaches, cover a wide range of topics within the field of modeling and analysis of biological systems. In addition, two regular submissions deal with models of self-assembling systems and metabolic constraints on the evolution of genetic codes.
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Subjects
Computational Biology/Bioinformatics, Probability and Statistics in Computer Science, Simulation and Modeling, Algorithm Analysis and Problem Complexity, Computer simulation, Computation by Abstract Devices, Artificial Intelligence (incl. Robotics), Bioinformatics, Computer software, Artificial intelligence, Computer scienceShowing 3 featured editions. View all 3 editions?
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1
Transactions on Computational Systems Biology XIV: Special Issue on Computational Models for Cell Processes
Nov 16, 2012, Springer
paperback
3642355234 9783642355233
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Transactions on Computational Systems Biology XIV: Special Issue on Computational Models for Cell Processes
2012, Springer Berlin Heidelberg, Imprint: Springer
electronic resource :
in English
3642355242 9783642355240
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3
Transactions on Computational Systems Biology XIV: Special Issue on Computational Models for Cell Processes
Nov 21, 2012, Springer
paperback
3642355250 9783642355257
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Source title: Transactions on Computational Systems Biology XIV: Special Issue on Computational Models for Cell Processes
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