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E-grāmata: Quantitative Biology: A Practical Introduction

  • Formāts: EPUB+DRM
  • Sērija : Learning Materials in Biosciences
  • Izdošanas datums: 04-Jan-2022
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811650185
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Quantitative Biology: A Practical IntroductionPalielināt
  • Formāts: EPUB+DRM
  • Sērija : Learning Materials in Biosciences
  • Izdošanas datums: 04-Jan-2022
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811650185
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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. It also provides step-by-step tutorials to run various sample programs in ones 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 scientistwho 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.
1 Introduction to Quantitative Biology
1(10)
1.1 What Is (Modern) Quantitative Biology?
2(2)
1.2 Why Study Quantitative Biology?
4(1)
1.3 The Aim and Target of This Book
5(1)
1.4 Construction of Quantitative Models as a Goal of Quantitative Biology
6(5)
1.4.1 What Kind of Model Is a Good Model?
6(1)
1.4.2 The Need for Quantitative Models
7(1)
1.4.3 How Can We Make a Good Quantitative Model?
8(2)
References
10(1)
2 Cell Architectonics
11(6)
2.1 Why We Deal with the Architectonics of the Cell (In This Book)?
12(1)
2.2 What Is Cell Architectonics?
12(1)
2.3 Objective #1: Mechanics of the Cell (Chap. 3)
13(1)
2.4 Objective #2: Diversity of the Cell (Chap. 7)
13(1)
2.5 Objective #3: Self-Organization of the Cell (Chap. 9)
13(1)
2.6 Objective #4: Development of the Cell over Time (Chap. 11)
14(3)
Reference
15(2)
3 Mechanics of the Cell
17(12)
3.1 Mechanical Forces and Cellular Dynamics 4
18(1)
3.2 Methods for Applying Force to Cellular Materials
18(2)
3.3 Mechanical Properties of Structures Inside the Cell
20(1)
3.4 Relationship Between Intracellular Deformation and Force: Elasticity, Viscosity, and Viscoelasticity
21(1)
3.5 Stress-Strain Relationship of Elastic Materials
21(2)
3.6 Rheology
23(1)
3.7 Reynolds Number
23(1)
3.8 Equations for Describing Viscous Fluids
24(1)
3.9 Modeling Cell Behaviors Based on Cell Mechanics
24(5)
References
25(4)
4 Implementing Toy Models in Microsoft Excel
29(22)
4.1 Custom Makes All Things Easy
30(1)
4.2 The Toy Model: Centration of the Nucleus Inside a Cell
31(7)
4.2.1 Biological Background
31(2)
4.2.2 Constructing One-Dimensional Model for Nuclear Centration
33(5)
4.3 Calculating the Movement of the Nucleus
38(1)
4.4 Model Implementation in Microsoft Excel
38(13)
4.4.1 Implementation of Cytoplasmic Pulling Model
40(3)
4.4.2 Implementation of Pushing Model
43(2)
4.4.3 Implementation of Cortex Pulling Model
45(4)
References
49(2)
5 Implementing Toy Models in Python
51(10)
5.1 Why Do We Need to Learn Programming?
52(1)
5.2 Why Python?
52(1)
5.3 Getting Started with Python
53(1)
5.4 A Code to Simulate Nuclear Centration
53(8)
Reference
60(1)
6 Differential Equations to Describe Temporal Changes
61(14)
6.1 Why the Use of a Differential Equation?
62(2)
6.1.1 What Is a Differential Equation?
62(1)
6.1.2 Modeling a Biological Phenomenon Using Differential Equation
63(1)
6.2 What Differential Equations Convey
64(2)
6.2.1 Equilibrium Points
64(1)
6.2.2 Stability of the Equilibrium Points: Linear Stability Analysis
64(2)
6.3 Solving Differential Equations
66(9)
6.3.1 Modeling Nuclear Centration Using Differential Equation
66(1)
6.3.2 Analytical Solutions
66(1)
6.3.3 Calculating the Consequences of Differential Equations Computationally: Euler and the Runge-Kutta Methods
67(2)
6.3.4 A Coding Example of the Runge-Kutta Method with Python
69(4)
References
73(2)
7 Diversity of the Cell
75(10)
7.1 Diversity of the Cell
75(1)
7.2 Diversity in Cell Size: Scaling Problems
76(1)
7.3 Diversity in Cellular Response Due to Fluctuations
77(2)
7.4 Diversity in Cell Arrangement Due to Spatial Restrictions
79(1)
7.5 Diversity in the Pattern of Cytoplasmic Streaming Due to Molecular Activities
79(3)
7.6 The Role of a Gene as a Switch
82(3)
References
82(3)
8 Randomness, Diffusion, and Probability
85(16)
8.1 Randomness
86(3)
8.1.1 Why Should We Consider Randomness for Biological Processes?
86(1)
8.1.2 Modeling Random Motion with Python
86(3)
8.2 Diffusion
89(4)
8.2.1 Random Motion and Diffusion
89(3)
8.2.2 Diffusion Equation
92(1)
8.3 Energy Landscape and Existing Probability
93(8)
8.3.1 Potential Energy and Energy Landscape
93(2)
8.3.2 Boltzmann Distribution
95(3)
8.3.3 Existing Probability
98(1)
References
99(2)
9 Self-Organization of the Cell
101(8)
9.1 Why Self-Organization?
102(1)
9.2 Mechanisms to Create Order
102(1)
9.3 Negative Feedback Regulation
103(1)
9.4 Positive Feedback Regulation
103(3)
9.4.1 Positive Feedback Plus Fluctuations
104(1)
9.4.2 Positive Feedback Plus Negative Feedback
104(2)
9.5 Symmetry Breaking
106(1)
9.6 Phase Separation in Cell Biology
106(3)
References
107(2)
10 Modeling Feedback Regulations
109(14)
10.1 Basic Knowledge to Model Feedback Regulations Using Differential Equation
110(6)
10.1.1 Modeling of Activation and Repression Using Hill Function
110(1)
10.1.2 Modeling Degradation
111(1)
10.1.3 Negative Feedback Regulations
111(4)
10.1.4 Linear Stability Analyses for Negative Feedback Models
115(1)
10.2 Reaction-Diffusion Mechanism Creating Biological Patterns
116(7)
10.2.1 An Example of a Reaction-Diffusion System
116(3)
10.2.2 Linear Stability Analysis for the Reaction-Diffusion System
119(3)
References
122(1)
11 Development of the Cell over Time (Perspectives)
123(6)
11.1 Development over Time: Temporal Changes from One Order to Another
124(1)
11.2 An Example: Development of Cell Arrangement over Time
124(1)
11.3 Models for Individual but Sequential Cell Orders
124(1)
11.4 Transition of Different Orders: Diversity in Time Scales
125(1)
11.5 Perspective
125(4)
References
126(3)
Index 129
Akatsuki Kimura is a Professor at National Institute of Genetics and The Graduate University for Advanced Studies (SOKENDAI), Japan. He earned his Bachelor under the supervision of Dr. Masayuki Yamamoto, and his PhD under the supervision of Dr. Masami Horikoshi, in Biophysics and Biochemistry from the University of Tokyo. After completing his PhD, Kimura joined the field of quantitative biology as an assistant professor at Keio University and a JSPS (Japan Society for the Promotion of Science) fellow under the supervision of Dr. Shuichi Onami. In 2006, Kimura launched his research group, Cell Architecture Laboratory, at National Institute of Genetics as an Associate Professor. He was also appointed at The Graduate University for Advanced Studies (SOKENDAI). His research interest is to construct mechanical models of the cell, to understand the mechanics, diversity, and self-organization of the cell.
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