000 04238nam a22005055i 4500
001 978-3-319-59731-7
003 DE-He213
005 20181204134416.0
007 cr nn 008mamaa
008 170906s2017 gw | s |||| 0|eng d
020 _a9783319597317
_9978-3-319-59731-7
024 7 _a10.1007/978-3-319-59731-7
_2doi
040 _aISI Library, Kolkata
050 4 _aQH323.5
050 4 _aQH324.2-324.25
072 7 _aPDE
_2bicssc
072 7 _aMAT003000
_2bisacsh
072 7 _aPDE
_2thema
082 0 4 _a570.285
_223
100 1 _aGarfinkel, Alan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aModeling Life
_h[electronic resource] :
_bThe Mathematics of Biological Systems /
_cby Alan Garfinkel, Jane Shevtsov, Yina Guo.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 445 p. 353 illus., 299 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1. Modeling, Change, and Simulation -- 2. Derivatives and Integrals -- 3. Equilibrium Behavior -- 4. Non-Equilibrium Dynamics: Oscillation -- 5. Chaos -- 6. Linear Algebra -- 7. Multivariable Systems -- Bibliography -- Index.
520 _aFrom predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. This book develops the mathematical tools essential for students in the life sciences to describe these interacting systems and to understand and predict their behavior. Complex feedback relations and counter-intuitive responses are common in dynamical systems in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science?
650 0 _aDifferential Equations.
650 1 4 _aMathematical and Computational Biology.
_0http://scigraph.springernature.com/things/product-market-codes/M31000
650 2 4 _aMathematical Modeling and Industrial Mathematics.
_0http://scigraph.springernature.com/things/product-market-codes/M14068
650 2 4 _aOrdinary Differential Equations.
_0http://scigraph.springernature.com/things/product-market-codes/M12147
700 1 _aShevtsov, Jane.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aGuo, Yina.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319597300
776 0 8 _iPrinted edition:
_z9783319597324
776 0 8 _iPrinted edition:
_z9783319866895
856 4 0 _uhttps://doi.org/10.1007/978-3-319-59731-7
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c426981
_d426981