Details

Autor(en) / Beteiligte

• Klipp, E

Titel

Systems biology in practice : concepts, implementation and application [electronic resource]

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Description based upon print version of record.
• Includes bibliographical references and index.
• Systems Biology in Practice; Preface; Foreword; Contents; Part I General Introduction; 1 Basic Principles; 1.1 Systems Biology is Biology!; 1.2 Systems Biology is Modeling; 1.2.1 Properties of Models; 1.2.1.1 Model Assignment is not Unique; 1.2.1.2 System State; 1.2.1.3 Steady States; 1.2.1.4 Variables, Parameters, and Constants; 1.2.1.5 Model Behavior; 1.2.1.6 Process Classification; 1.2.1.7 Purpose and Adequateness of Models; 1.2.1.8 Advantages of Computational Modeling; 1.2.1.9 Model Development; 1.2.2 Typical Aspects of Biological Systems and Corresponding Models
• 1.2.2.1 Network Versus Elements1.2.2.2 Modularity; 1.2.2.3 Robustness and Sensitivity are Two Sides of the Same Coin; 1.3 Systems Biology is Data Integration; 1.4 Systems Biology is a Living Science; References; 2 Biology in a Nutshell; Introduction; 2.1 The Origin of Life; 2.2 Molecular Biology of the Cell; 2.2.1 Chemical Bonds and Forces Important in Biological Molecules; 2.2.2 Functional Groups in Biological Molecules; 2.2.3 Major Classes of Biological Molecules; 2.2.3.1 Carbohydrates; 2.2.3.2 Lipids; 2.2.3.3 Proteins; 2.2.3.4 Nucleic Acids; 2.3 Structural Cell Biology
• 2.3.1 Structure and Function of Biological Membranes2.3.2 Nucleus; 2.3.3 Cytosol; 2.3.4 Mitochondria; 2.3.5 Endoplasmatic Reticulum and Golgi Complex; 2.3.6 Other Organelles; 2.4 Expression of Genes; 2.4.1 Transcription; 2.4.2 Processing of the mRNA; 2.4.3 Translation; 2.4.4 Protein Sorting and Posttranslational Modifications; 2.4.5 Regulation of Gene Expression; 2.5 Cell Cycle; 2.5.1 Mitosis; 2.5.2 Meiosis and Genetic Recombination; References; 3 Mathematics in a Nutshell; Introduction; 3.1 Linear Algebra; 3.1.1 Linear Equations; 3.1.1.1 The Gaussian Elimination Algorithm
• 3.1.1.2 Systematic Solution of Linear Systems3.1.2 Matrices; 3.1.2.1 Basic Notions; 3.1.2.2 Linear Dependency; 3.1.2.3 Basic Matrix Operations; 3.1.2.4 Dimension and Rank; 3.1.2.5 Eigenvalues and Eigenvectors of a Square Matrix; 3.2 Ordinary Differential Equations; 3.2.1 Notions; 3.2.2 Linearization of Autonomous Systems; 3.2.3 Solution of Linear ODE Systems; 3.2.4 Stability of Steady States; 3.2.4.1 Global Stability of Steady States; 3.2.4.2 Limit Cycles; 3.3 Difference Equations; 3.4 Statistics; 3.4.1 Basic Concepts of Probability Theory
• 3.4.1.1 Random Variables, Densities, and Distribution Functions3.4.1.2 Transforming Probability Densities; 3.4.1.3 Product Experiments and Independence; 3.4.1.4 Limit Theorems; 3.4.2 Descriptive Statistics; 3.4.2.1 Statistics for Sample Location; 3.4.2.2 Statistics for Sample Variability; 3.4.2.3 Density Estimation; 3.4.2.4 Correlation of Samples; 3.4.3 Testing Statistical Hypotheses; 3.4.3.1 Statistical Framework; 3.4.3.2 Two-sample Location Tests; 3.4.4 Linear Models; 3.4.4.1 ANOVA; 3.4.4.2 Multiple Linear Regression; 3.5 Graph and Network Theory; 3.5.1 Introduction
• 3.5.2 Regulatory Networks
• Presenting the main concepts, this book leads students as well as advanced researchers from different disciplines to an understanding of current ideas in the complex field of comprehensive experimental investigation of biological objects, analysis of data, development of models, simulation, and hypothesis generation. It provides readers with guidance on how a specific complex biological question may be tackled:How to formulate questions that can be answeredWhich experiments to performWhere to find information in databases and on the InternetWhat kinds of models are approp
• English