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Quantitative Biology : A Practical Introduction - 1st ed. 2022

By: (Author) Akatsuki Kimura

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Ksh 11,700.00

Format: Paperback / Softback

ISBN-10: 9811650179

ISBN-13: 9789811650178

Edition: 1st ed. 2022

Series: Learning Materials in Biosciences

Publisher: Springer Verlag, Singapore

Imprint: Springer Verlag, Singapore

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Jan 5th, 2022

Print length: 130 Pages

Weight: 262 grams

Dimensions (height x width x thickness): 16.80 x 24.50 x 1.20 cms

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This textbook is for biologists, to conduct quantitative analysis and modeling of biological processes at molecular and cellular levels. Focusing on practical concepts and techniques for everyday research, this text will enable beginners, both students and established biologists, to take the first step in quantitative biology.

This textbook is for biologists, to conduct quantitative analysis and modeling of biological processes at molecular and cellular levels.

Focusing on practical concepts and techniques for everyday research, this text will enable beginners, both students and established biologists, to take the first step in quantitative biology. It also provides step-by-step tutorials to run various sample programs in one''s personal computer using Excel and Python.

This volume traces topics, starting with an introductory chapter, such as modeling, construction and execution of numerical models, and key concepts in quantitative biology: feedback regulations, fluctuations and randomness, and statistical analyses. It also provide sample codes with guidance to procedure programming for actual biological processes such as movement of the nucleus within a cell, cell-cycle regulation, stripe pattern formation of skins, and distribution of energy.

Written by a leading research scientist who has a background in biology, studied quantitative approaches by himself, and teaches quantitative biology at several universities, this textbook broadens quantitative approaches for biologists who do not have a strong background in mathematics, physics, or computer programming, and helps them progress further in their research.



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