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Mathematical Models in Biology: Bringing Mathematics to Life
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
| Lieferstatus: |
 Auf Bestellung (Lieferzeit unbekannt) |
| Genre: |
Schulbücher |
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Algebra /
Analysis /
Analysis (Mathematics) /
B /
Biomathematics /
Calculus & mathematical analysis /
Life Sciences /
Life sciences# general issues /
Life Sciences, general /
Linear Algebra /
Linear and Multilinear Algebras, Matrix Theory /
Mathematical analysis /
Mathematical and Computational Biology /
Mathematics and Statistics /
Matrix theory |
| ISBN: |
9783319794921 |
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EAN-Code:
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9783319794921 |
| Verlag: |
Springer Nature EN |
| Einband: |
Kartoniert |
| Sprache: |
English
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| Dimensionen: |
H 235 mm / B 155 mm / D |
| Gewicht: |
332 gr |
| Seiten: |
199 |
| Illustration: |
XII, 199 p. 28 illus., 18 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen, Tabellen, schwarz-weiss, Tabellen, farbig |
| Zus. Info: |
Previously published in hardcover |
| Bewertung: |
Titel bewerten / Meinung schreiben
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| Inhalt: |
| This book presents an exciting collection of contributions based on the workshop "Bringing Maths to Life" held October 27-29, 2014 in Naples, Italy. The state-of-the art research in biology and the statistical and analytical challenges facing huge masses of data collection are treated in this Work. Specific topics explored in depth surround the sessions and special invited sessions of the workshop and include genetic variability via differential expression, molecular dynamics and modeling, complex biological systems viewed from quantitative models, and microscopy images processing, to name several. In depth discussions of the mathematical analysis required to extract insights from complex bodies of biological datasets, to aid development in the field novel algorithms, methods and software tools for genetic variability, molecular dynamics, and complex biological systems are presented in this book. Researchers and graduate students in biology, life science, and mathematics/statistics will find the content useful as it addresses existing challenges in identifying the gaps between mathematical modeling and biological research. The shared solutions will aid and promote further collaboration between life sciences and mathematics. |
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