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Quantifying Life : A Symbiosis of Computation, Mathematics, and Biology /
Since the time of Isaac Newton, physicists have used mathematics to describe the behavior of matter of all sizes, from subatomic particles to galaxies. In the past three decades, as advances in molecular biology have produced an avalanche of data, computational and mathematical techniques have also...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chicago :
University of Chicago Press,
[2016]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 1 | 0 | |a Quantifying Life : |b A Symbiosis of Computation, Mathematics, and Biology / |c Dmitry A. Kondrashov. |
| 264 | 1 | |a Chicago : |b University of Chicago Press, |c [2016] | |
| 264 | 4 | |c ©2016 | |
| 300 | |a 1 online resource (304 p.) : |b 90 halftones | ||
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| 505 | 0 | 0 | |t Frontmatter -- |t Contents -- |t Preface -- |t Chapter 0. Introduction -- |t Part I. Describing single variables -- |t Chapter 1. Arithmetic and variables: the lifeblood of modeling -- |t Chapter 2. Functions and their graphs -- |t Chapter 3. Describing data sets -- |t Chapter 4. Random variables and distributions -- |t Chapter 5. Estimation from a random sample -- |t Part II. Relationship between two variables -- |t Chapter 6. Independence of random variables -- |t Chapter 7. Bayes' amazing formula -- |t Chapter 8. Linear regression and correlation -- |t Chapter 9. Nonlinear data fitting -- |t Part III. Chains of random variables -- |t Chapter 10. Markov models with discrete states -- |t Chapter 11. Probability distributions of Markov chains -- |t Chapter 12. Stationary distributions of Markov chains -- |t Chapter 13. Dynamics of Markov models -- |t Part IV. Variables that change with time -- |t Chapter 14. Linear difference equations -- |t Chapter 15. Linear ordinary differential equations -- |t Chapter 16. Graphical analysis of ordinary differential equations -- |t Chapter 17. Chaos and bifurcations in difference equations -- |t Bibliography -- |t Index |
| 506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
| 520 | |a Since the time of Isaac Newton, physicists have used mathematics to describe the behavior of matter of all sizes, from subatomic particles to galaxies. In the past three decades, as advances in molecular biology have produced an avalanche of data, computational and mathematical techniques have also become necessary tools in the arsenal of biologists. But while quantitative approaches are now providing fundamental insights into biological systems, the college curriculum for biologists has not caught up, and most biology majors are never exposed to the computational and probabilistic mathematical approaches that dominate in biological research. With Quantifying Life, Dmitry A. Kondrashov offers an accessible introduction to the breadth of mathematical modeling used in biology today. Assuming only a foundation in high school mathematics, Quantifying Life takes an innovative computational approach to developing mathematical skills and intuition. Through lessons illustrated with copious examples, mathematical and programming exercises, literature discussion questions, and computational projects of various degrees of difficulty, students build and analyze models based on current research papers and learn to implement them in the R programming language. This interplay of mathematical ideas, systematically developed programming skills, and a broad selection of biological research topics makes Quantifying Life an invaluable guide for seasoned life scientists and the next generation of biologists alike. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Jun 2022) | |
| 650 | 0 | |a Biology |x Mathematical models. | |
| 650 | 0 | |a Biology-Mathematical models. | |
| 650 | 0 | |a Computational biology. | |
| 650 | 0 | |a Mathematical models. | |
| 650 | 7 | |a SCIENCE / General. |2 bisacsh | |
| 653 | |a mathematical modeling, physics, biology, science, molecules, quantitative, coding, math, undergraduate, career, computational projects, programming, r language, variables, genetics, graphs, plotting, scripts, functions, biochemical reactions, probability, distribution, random numbers, loops, central limit theorem, independence, hypothesis testing, bayes formula, linear regression, fitting, log transforms, nonfiction, markov, molecular evolution, chaos, fixed points. | ||
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t University of Chicago Press Complete eBook-Package 2016 |z 9783110710984 |
| 856 | 4 | 0 | |u https://degruyter.uam.elogim.com/isbn/9780226371931 |z Texto completo |
| 912 | |a 978-3-11-071098-4 University of Chicago Press Complete eBook-Package 2016 |b 2016 | ||
| 912 | |a GBV-deGruyter-alles | ||