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020 _a9781447167624
_z978-1-4471-6762-4
024 7 _a10.1007/978-1-4471-6762-4
_2doi
050 4 _aQA76.9.C65
072 7 _aUYM
_2bicssc
072 7 _aCOM072000
_2bisacsh
072 7 _aUYM
_2thema003.3
_223
100 1 _aBarnes, David J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGuide to Simulation and Modeling for Biosciences /
_cby David J. Barnes, Dominique Chu.
250 _a2nd ed. 2015.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aSimulation Foundations, Methods and Applications,
_x2195-2817
505 0 _aFoundations of Modeling -- Agent-based Modeling -- ABMs using Repast Simphony -- Differential Equations -- Mathematical Tools -- Other Stochastic Methods and Prism -- Simulating Biochemical Systems -- Biochemical Models Beyond the Perfect Mixing Assumption -- Reference Material.
520 _aThis accessible text/reference presents a detailed introduction to the use of a wide range of software tools and modeling environments for use in the biosciences, as well as some of the fundamental mathematical background. The practical constraints and difficulties presented by each modeling technique are described in detail, enabling the researcher to determine quickly which software package would be most useful for their particular problem. This Guide to Simulation and Modeling for Biosciences is a fully updated and enhanced revision of the authors’ earlier Introduction to Modeling for Biosciences. Written with the particular needs of the novice modeler in mind, this unique and helpful work guides the reader through realistic and concrete modeling projects, highlighting and commenting on the process of abstracting the real system into a model. 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, spatial simulations, and Gillespie’s stochastic simulation algorithm Provides exercises to help the reader sharpen their understanding of the topics Describes such useful tools as the Maxima algebra system, the PRISM model checker, and the modeling environments Repast Simphony and Smoldyn Contains appendices on rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts Offers supplementary material at an associated website, including source code for many of the example models discussed in the book Students and active researchers in the biosciences will benefit from the discussions of the high-quality, tried-and-tested modeling tools described in the book, as well as the thorough descriptions and examples.
650 0 _aComputer simulation.
650 0 _aBioinformatics.
650 0 _aBiology
_xData processing.
650 1 4 _aSimulation and Modeling.
_0http://scigraph.springernature.com/things/product-market-codes/I19000
650 2 4 _aMathematical Modeling and Industrial Mathematics.
_0http://scigraph.springernature.com/things/product-market-codes/M14068
650 2 4 _aComputational Biology/Bioinformatics.
_0http://scigraph.springernature.com/things/product-market-codes/I23050
650 2 4 _aComputer Appl. in Life Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/L17004
700 1 _aChu, Dominique.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-1-4471-6762-4
_3Springer eBooks
_zOnline access link to the resource
912 _aZDB-2-SCS
999 _c200434488
_d52700
942 _2lcc
_cEBK
041 _aeng