Quantitative biology : a practical introduction / Akatsuki Kimura.

Format:

Book

Author/Creator:

Kimura, Akatsuki, 1974- author.

Series:

Learning materials in biosciences.

Learning materials in biosciences

Language:

English

Subjects (All):

Biology--Research.

Biology.

Quantitative research.

Bioinformatics.

Physical Description:

1 online resource

Edition:

1st ed.

Place of Publication:

Gateway East, Singapore : Springer, [2022]

Summary:

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.

Contents:

Intro

Preface

Acknowledgments

Contents

1: Introduction to Quantitative Biology

What You Will Learn in This Chapter

1.1 What Is (Modern) Quantitative Biology?

Questions

1.2 Why Study Quantitative Biology?

1.3 The Aim and Target of This Book

1.4 Construction of Quantitative Models as a Goal of Quantitative Biology

1.4.1 What Kind of Model Is a Good Model?

1.4.2 The Need for Quantitative Models

1.4.3 How Can We Make a Good Quantitative Model?

Answers

Take-Home Message

References

Further Reading

2: Cell Architectonics

2.1 Why We Deal with the Architectonics of the Cell (In This Book)?

2.2 What Is Cell Architectonics?

2.3 Objective #1: Mechanics of the Cell (Chap. 3)

2.4 Objective #2: Diversity of the Cell (Chap. 7)

2.5 Objective #3: Self-Organization of the Cell (Chap. 9)

2.6 Objective #4: Development of the Cell over Time (Chap. 11)

Reference

3: Mechanics of the Cell

3.1 Mechanical Forces and Cellular Dynamics

3.2 Methods for Applying Force to Cellular Materials

3.3 Mechanical Properties of Structures Inside the Cell

3.4 Relationship Between Intracellular Deformation and Force: Elasticity, Viscosity, and Viscoelasticity

3.5 Stress-Strain Relationship of Elastic Materials

3.6 Rheology

3.7 Reynolds Number

3.8 Equations for Describing Viscous Fluids

3.9 Modeling Cell Behaviors Based on Cell Mechanics

4: Implementing Toy Models in Microsoft Excel

4.1 Custom Makes All Things Easy

4.2 The Toy Model: Centration of the Nucleus Inside a Cell

4.2.1 Biological Background.

4.2.2 Constructing One-Dimensional Model for Nuclear Centration

4.2.2.1 Modeling Forces to Move the Nucleus Using Stokes´ Law

4.2.2.2 Force Generation in the Cytoplasmic Pulling Model

4.2.2.3 Force Generation in the Pushing Model

4.2.2.4 Force Generation in the Cortex Pulling Model (an Educational Version)

4.3 Calculating the Movement of the Nucleus

4.4 Model Implementation in Microsoft Excel

4.4.1 Implementation of Cytoplasmic Pulling Model

4.4.2 Implementation of Pushing Model

4.4.3 Implementation of Cortex Pulling Model

5: Implementing Toy Models in Python

5.1 Why Do We Need to Learn Programming?

5.2 Why Python?

5.3 Getting Started with Python

5.4 A Code to Simulate Nuclear Centration

6: Differential Equations to Describe Temporal Changes

6.1 Why the Use of a Differential Equation?

6.1.1 What Is a Differential Equation?

6.1.2 Modeling a Biological Phenomenon Using Differential Equation

6.2 What Differential Equations Convey

6.2.1 Equilibrium Points

6.2.2 Stability of the Equilibrium Points: Linear Stability Analysis

6.3 Solving Differential Equations

6.3.1 Modeling Nuclear Centration Using Differential Equation

6.3.2 Analytical Solutions

6.3.3 Calculating the Consequences of Differential Equations Computationally: Euler and the Runge-Kutta Methods

6.3.4 A Coding Example of the Runge-Kutta Method with Python

7: Diversity of the Cell

7.1 Diversity of the Cell

7.2 Diversity in Cell Size: Scaling Problems

7.3 Diversity in Cellular Response Due to Fluctuations.

7.4 Diversity in Cell Arrangement Due to Spatial Restrictions

7.5 Diversity in the Pattern of Cytoplasmic Streaming Due to Molecular Activities

7.6 The Role of a Gene as a Switch

8: Randomness, Diffusion, and Probability

8.1 Randomness

8.1.1 Why Should We Consider Randomness for Biological Processes?

8.1.2 Modeling Random Motion with Python

8.2 Diffusion

8.2.1 Random Motion and Diffusion

8.2.2 Diffusion Equation

8.3 Energy Landscape and Existing Probability

8.3.1 Potential Energy and Energy Landscape

8.3.2 Boltzmann Distribution

8.3.3 Existing Probability

9: Self-Organization of the Cell

9.1 Why Self-Organization?

9.2 Mechanisms to Create Order

9.3 Negative Feedback Regulation

9.4 Positive Feedback Regulation

9.4.1 Positive Feedback Plus Fluctuations

9.4.2 Positive Feedback Plus Negative Feedback

9.5 Symmetry Breaking

9.6 Phase Separation in Cell Biology

10: Modeling Feedback Regulations

10.1 Basic Knowledge to Model Feedback Regulations Using Differential Equation

10.1.1 Modeling of Activation and Repression Using Hill Function

10.1.2 Modeling Degradation

10.1.3 Negative Feedback Regulations

10.1.4 Linear Stability Analyses for Negative Feedback Models

10.2 Reaction-Diffusion Mechanism Creating Biological Patterns

10.2.1 An Example of a Reaction-Diffusion System

10.2.2 Linear Stability Analysis for the Reaction-Diffusion System

11: Development of the Cell over Time (Perspectives)

What You Will Learn in This Chapter.

11.1 Development over Time: Temporal Changes from One Order to Another

11.2 An Example: Development of Cell Arrangement over Time

11.3 Models for Individual but Sequential Cell Orders

11.4 Transition of Different Orders: Diversity in Time Scales

11.5 Perspective

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

981-16-5018-7

981-16-5017-9

OCLC:

1291314355