
Introduction to Mathematics for Computational Biology
Springer (Publisher)
Published on 13. September 2023
Book
Hardback
X, 264 pages
978-3-031-36565-2 (ISBN)
Description
This introductory guide provides a thorough explanation of the mathematics and algorithms used in standard data analysis techniques within systems biology, biochemistry, and biophysics. Each part of the book covers the mathematical background and practical applications of a given technique. Readers will gain an understanding of the mathematical and algorithmic steps needed to use these software tools appropriately and effectively, as well how to assess their specific circumstance and choose the optimal method and technology. Ideal for students planning for a career in research, early-career researchers, and established scientists undertaking interdisciplinary research.
More details
Series
Edition
2023 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
10 s/w Abbildungen, 30 farbige Abbildungen
X, 264 p. 40 illus., 30 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
582 gr
ISBN-13
978-3-031-36565-2 (9783031365652)
DOI
10.1007/978-3-031-36566-9
Schweitzer Classification
Other editions
Additional editions

Paola Lecca | Bruno Carpentieri
Introduction to Mathematics for Computational Biology
Book
09/2024
Springer
€106.99
Shipment within 15-20 days

Paola Lecca | Bruno Carpentieri
Introduction to Mathematics for Computational Biology
E-Book
09/2023
1st Edition
Springer
€96.29
Available for download
Persons
Paola Lecca, Assistant Professor,
Faculty of Engineering
, Free University of Bozen-Bolzano, ItalyBruno Carpentieri, Associate Professor,
Faculty of Engineering
, Free University of Bozen-Balzano, Italy
Content
1. Introduction to graph theory.- 2. Biological networks.- 3. Network inference for drug discovery- 4. Introduction to differential and integral calculus.- 5. Modelling chemical reactions.- 6. Reaction-diffusion systems.- 7. Linear algebra background.- 8. Regression.- 9. Cardiac electrophysiology.-