Modeling Differential Equations in Biology

Clifford Henry Taubes

doi:10.1017/CBO9780511811364

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Bonchev, D. Thomas, S. Apte, A. and Kier, L.B. 2010. Cellular automata modelling of biomolecular networks dynamics. SAR and QSAR in Environmental Research, Vol. 21, Issue. 1-2, p. 77.

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Raja, Muhammad Asif Zahoor Umar, Muhammad Sabir, Zulqurnain Khan, Junaid Ali and Baleanu, Dumitru 2018. A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. The European Physical Journal Plus, Vol. 133, Issue. 9,

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Ivanova, Tsvetelina I. 2021. Dynamics of flows of a substance in a channel with a branch. Vol. 2321, Issue. , p. 030014.

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Grossmann, Tamara G Komorowska, Urszula Julia Latz, Jonas and Schönlieb, Carola-Bibiane 2024. Can physics-informed neural networks beat the finite element method?. IMA Journal of Applied Mathematics, Vol. 89, Issue. 1, p. 143.

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2nd edition
Clifford Henry Taubes, Harvard University, Massachusetts

Publisher:: Cambridge University Press
Online publication date:: June 2012
Print publication year:: 2008
Online ISBN:: 9780511811364
DOI:: https://doi.org/10.1017/CBO9780511811364

Subjects:: Mathematics, Mathematical Modeling and Methods

56.99 (GBP)

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Based on a very successful one-semester course taught at Harvard, this text teaches students in the life sciences how to use differential equations to help their research. It needs only a semester's background in calculus. Ideas from linear algebra and partial differential equations that are most useful to the life sciences are introduced as needed, and in the context of life science applications, are drawn from real, published papers. It also teaches students how to recognize when differential equations can help focus research. A course taught with this book can replace the standard course in multivariable calculus that is more usually suited to engineers and physicists.

'Graduates of this course will be prepared to discuss the setup and structure of the underlying model, and to engage with the type of predictions that it can make. That's an admirable accomplishment for a book at this level! Moreover, equipping undergraduates with such a toolkit can open truly exciting doors for thinking about biology, a theme that runs throughout this text.'

Source: SIAM Review

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Modeling Differential Equations in Biology

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