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008 100726s2010 xxk| s |||| 0|eng d
020 _a9781849963268
_9978-1-84996-326-8
040 _cMX-MeUAM
050 4 _aQA76.9.C65
082 0 4 _a003.3
_223
100 1 _aBarnes, David J.
_eauthor.
245 1 0 _aIntroduction to Modeling for Biosciences
_h[recurso electrónico] /
_cby David J. Barnes, Dominique Chu.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2010.
300 _aXII, 322 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aFoundations of Modeling -- Agent-Based Modeling -- ABMs Using Repast and Java -- Differential Equations -- Mathematical Tools -- Other Stochastic Methods and Prism -- Simulating Biochemical Systems.
520 _aComputational modeling has become an essential tool for researchers in the biological sciences. Yet in biological modeling, there is no one technique that is suitable for all problems. Instead, different problems call for different approaches. Furthermore, it can be helpful to analyze the same system using a variety of approaches, to be able to exploit the advantages and drawbacks of each. In practice, it is often unclear which modeling approaches will be most suitable for a particular biological question - a problem that requires researchers to know a reasonable amount about a number of techniques, rather than become experts on a single one. Introduction to Modeling for Biosciences addresses this issue by presenting a broad overview of the most important techniques used to model biological systems. In addition to providing an introduction into the use of a wide range of software tools and modeling environments, this helpful text/reference describes the constraints and difficulties that each modeling technique presents in practice. This enables the researcher to quickly determine which software package would be most useful for their particular problem. Topics and features: Introduces a basic array of techniques to formulate models of biological systems, and to solve them Discusses agent-based models, stochastic modeling techniques, differential equations and Gillespie’s stochastic simulation algorithm Intersperses the text with exercises Includes practical introductions to the Maxima computer algebra system, the PRISM model checker, and the Repast Simphony agent modeling environment Contains appendices on Repast batch running, rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts Supplies source code for many of the example models discussed, at the associated website http://www.cs.kent.ac.uk/imb/ This unique and practical work guides the novice modeler through realistic and concrete modeling projects, highlighting and commenting on the process of abstracting the real system into a model. Students and active researchers in the biosciences will also benefit from the discussions of the high-quality, tried-and-tested modeling tools described in the book, as well as thorough descriptions and examples. David J. Barnes is a lecturer in computer science at the University of Kent, UK, with a strong background in the teaching of programming. Dominique Chu is a lecturer in computer science at the University of Kent, UK. He is an expert in mathematical and computational modeling of biological systems, with years of experience in these subject fields.
650 0 _aComputer science.
650 0 _aComputer simulation.
650 0 _aBioinformatics.
650 0 _aBiology
_xData processing.
650 1 4 _aComputer Science.
650 2 4 _aSimulation and Modeling.
650 2 4 _aMathematical Modeling and Industrial Mathematics.
650 2 4 _aComputational Biology/Bioinformatics.
650 2 4 _aComputer Appl. in Life Sciences.
700 1 _aChu, Dominique.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781849963251
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-84996-326-8
596 _a19
942 _cLIBRO_ELEC
999 _c200774
_d200774