Systems Biology E-Book

Edda Klipp

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1061

Veröffentlichungsjahr: 2011

Beschreibung

This advanced textbook is tailored to the needs of introductory course in Systems Biology. It has a compagnion website (WWW.WILEY-VCH.DE/HOME/SYSTEMSBIOLOGY) with solutions to questions in the book and several additional extensive working models. The book is related to the very successful previous title 'Systems Biology in Practice' and has incorporated the feedback and suggestions from many lecturers worldwide. The book addresses biologists as well as engineers and computer scientists. The interdisciplinary team of acclaimed authors worked closely together to ensure a comprehensive coverage with no overlaps in a homogenous and compelling style.

Leseprobe

Contents

Preface

Part One Introduction to Systems Biology

1 Introduction

1.1 Biology in Time and Space

1.2 Models and Modeling

1.3 Basic Notions for Computational Models

1.4 Data Integration

1.5 Standards

References

2 Modeling of Biochemical Systems

2.1 Kinetic Modeling of Enzymatic Reactions

2.2 Structural Analysis of Biochemical Systems

2.3 Kinetic Models of Biochemical Systems

2.4 Tools and Data Formats for Modeling

References

3 Specific Biochemical Systems

3.1 Metabolic Systems

3.2 Signaling Pathways

3.3 The Cell Cycle

3.4 Spatial Models

3.5 Apoptosis

References

4 Model Fitting

4.1 Data for Small Metabolic and Signaling Systems

4.2 Parameter Estimation

4.3 Reduction and Coupling of Models

4.4 Model Selection

References

5 Analysis of High-Throughput Data

5.1 High-Throughput Experiments

5.2 Analysis of Gene Expression Data

References

6 Gene Expression Models

6.1 Mechanisms of Gene Expression Regulation

6.2 Gene Regulation Functions

6.3 Dynamic Models of Gene Regulation

References

7 Stochastic Systems and Variability

7.1 Stochastic Modeling of Biochemical Reactions

7.2 Fluctuations in Gene Expression

7.3 Variability and Uncertainty

7.4 Robustness

References

8 Network Structures, Dynamics, and Function

8.1 Structure of Biochemical Networks

8.2 Network Motifs

8.3 Modularity

References

9 Optimality and Evolution

9.1 Optimality and Constraint-Based Models

9.2 Optimal Enzyme Concentrations

9.3 Evolutionary Game Theory

References

10 Cell Biology

10.1 Introduction

10.2 The Origin of Life

10.3 Molecular Biology of the Cell

10.4 Structural Cell Biology

10.5 Expression of Genes

References

11 Experimental Techniques in Molecular Biology

11.1 Introduction

11.2 Restriction Enzymes and Gel Electrophoresis

11.3 Cloning Vectors and DNA Libraries

11.4 1D and 2D Protein Gels

11.5 Hybridization and Blotting Techniques

11.6 Further Protein Separation Techniques

11.7 DNA and Protein Chips

11.8 Yeast Two-Hybrid System

11.9 Mass Spectrometry

11.10 Transgenic Animals

11.11 RNA Interference

11.12 ChIP on Chip and ChIP-PET

11.13 Surface Plasmon Resonance

11.14 Population Heterogeneity and Single Entity Experiments

References

12 Mathematics

12.1 Linear Modeling

12.2 Ordinary Differential Equations

12.3 Difference Equations

12.4 Graph and Network Theory

References

13 Statistics

13.1 Basic Concepts of Probability Theory

13.2 Descriptive Statistics

13.3 Testing Statistical Hypotheses

13.4 Linear Models

13.5 Principal Component Analysis

References

14 Stochastic Processes

14.1 Basic Notions for Random Processes

14.2 Markov Processes

14.3 Jump Processes in Continuous Time: The Master Equation

14.4 Continuous Random Processes

References

15 Control of Linear Systems

15.1 Linear Dynamical Systems

15.2 System Response

15.3 The Gramian Matrices

16 Databases

16.1 Databases of the National Center for Biotechnology

16.2 Databases of the European Bioinformatics Institute

16.3 Swiss-Prot, TrEMBL, and UniProt

16.4 Protein Databank

16.5 BioNumbers

16.6 Gene Ontology

16.7 Pathway Databases

References

17 Modeling Tools

17.1 Introduction

17.2 Mathematica and Matlab

17.3 Dizzy

17.4 Systems Biology Workbench

17.5 Tools Compendium

References

Index

The Authors

Prof. Edda KlippHumboldt-Universität BerlinInstitut für BiologieTheoretische BiophysikInvalidenstr. 4210115 Berlin

Dr. Wolfram LiebermeisterHumboldt-Universität BerlinInstitut für BiologieTheoretische Biophysik Invalidenstr. 4210115 Berlin

Dr. Christoph WierlingMPI für Molekulare GenetikIhnestr. 7314195 BerlinGermany

Dr. Axel KowaldProtagen AGOtto-Hahn-Str. 1544227 Dortmund

Prof. Hans LehrachMPI für Molekulare GenetikIhnestr. 7314195 Berlin Germany

Prof. Ralf HerwigMPI für Molekulare GenetikIhnestr. 7314195 BerlinGermany

CoverThe cover pictures were provided with kind permission by Santiago Ortiz and Dr. Michael Erlowitz

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Preface

Life is probably the most complex phenomenon in the universe. We see kids growing, people aging, plants blooming, and microbes degrading their remains. We use yeast for brewery and bakery, and doctors prescribe drugs to cure diseases. But can we understand how life works? Since the 19th century, the processes of life have no longer been explained by special “living forces, ” but by the laws of physics and chemistry. By studying the structure and physiology of living systems more and more in detail, researchers from different disciplines have revealed how the mystery of life arises from the structural and functional organization of cells and from the continuous refinement by mutation and selection.

In recent years, new imaging techniques have opened a completely new perception of the cellular microcosm. If we zoom into the cell, we can observe how structures are built, maintained, and reproduced while various sensing and regulation systems help the cell to respond appropriately to environmental changes. But along with all these fascinating observations, many open questions remain. Why do we age? How does a cell know when to divide? How can severe diseases such as cancer or genetic disorders be cured? How can we convince – i.e., manipulate – microbes to produce a desirable substance? How can the life sciences contribute to environmental safety and sustainable technologies?

This book provides you with a number of tools and approaches that can help you to think in more detail about such questions from a theoretical point of view. A key to tackle such questions is to combine biological experiments with computational modeling in an approach called systems biology: it is the combined study of biological systems through (i) investigating the components of cellular networks and their interactions, (ii) applying experimental high-throughput and whole-genome techniques, and (iii) integrating computational methods with experimental efforts.

The systemic approach in biology is not new, but it recently gained new thrust due to the emergence of powerful experimental and computational methods. It is based on the accumulation of an increasingly detailed biological knowledge, on the emergence of new experimental techniques in genomics and proteomics, on a tradition of mathematical modeling of biological processes, on the exponentially growing computer power (as prerequisite for databases and the calculation of large systems), and on the Internet as the central medium for a quick and comprehensive exchange of information.

Systems Biology has influenced modern biology in two major ways: on the one hand, it offers computational tools for analyzing, integrating and interpreting biological data and hypotheses. On the other hand, it has induced the formulation of new theoretical concepts and the application of existing ones to new questions. Such concepts are, for example, the theory of dynamical systems, control theory, the analysis of molecular noise, robustness and fragility of dynamic systems, and statistical network analysis. As systems biology is still evolving as a scientific field, a central issue is the standardization of experiments, of data exchange, and of mathematical models.

In this book, we attempt to give a survey of this rapidly developing field. We will show you how to formulate your own model of biological processes, how to analyze such models, how to use data and other available information for making your model more precise – and how to interpret the results. This book is designed as an introductory course for students of biology, biophysics and bioinformatics, and for senior scientists approaching Systems Biology from a different discipline. Its nine chapters contain material for about 30 lectures and are organized as follows.

Chapter 1 – Introduction (E. Klipp, W. Liebermeister, A. Kowald, 1 lecture)

Introduction to the subject. Elementary concepts and definitions are presented. Read this if you want to start right from the beginning.

Chapter 2 – Modeling of Biochemical Systems (E. Klipp, C. Wierling, 4 lectures)

This chapter describes kinetic models for biochemical reaction networks, the most common computational technique in Systems Biology. It includes kinetic laws, stoichiometric analysis, elementary flux modes, and metabolic control analysis. Introduces tools and data formats necessary for modeling.

Chapter 3 – Specific Biochemical Systems (E. Klipp, C. Wierling, W. Liebermeister, 5 lectures)

Using specific examples from metabolism, signaling, and cell cycle, a number of popular modeling techniques are discussed. The aim of this chapter is to make the reader familiar with both modeling techniques and biological phenomena.

Chapter 4 – Model Fitting (W. Liebermeister, A. Kowald, 4 lectures)

Models in systems biology usually contain a large number of parameters. Assigning appropriate numerical values to these parameters is an important step in the creation of a quantitative model. This chapter shows how numerical values can be obtained from the literature or by fitting a model to experimental data. It also discusses how model structures can be simplified and how they can be chosen if several different models can potentially describe the experimental observations.

Chapter 5 – Analysis of High-Throughput Data (R. Herwig, 2 lectures)

Several techniques that have been developed in recent years produce large quantities of data (e.g., DNA and protein chips, yeast two-hybrid, mass spectrometry). But such large quantities often go together with a reduced quality of the individual measurement. This chapter describes techniques that can be used to handle this type of data appropriately.

Chapter 6 – Gene Expression Models (R. Herwig, W. Liebermeister, E. Klipp, 3 lectures)

Thousands of gene products are necessary to create a living cell, and the regulation of gene expression is a very complex and important task to keep a cell alive. This chapter discusses how the regulation of gene expression can be modeled, how different input signals can be integrated, and how the structure of gene networks can be inferred from experimental data.

Chapter 7 – Stochastic Systems and Variability (W. Liebermeister, 4 lectures)

Random fluctuations in transcription, translation and metabolic reactions make mathematics complicated, computation costly and interpretation of results not straight forward. But since experimentalists find intriguing examples for macroscopic consequences of random fluctuation at the molecular level, the incorporation of these effects into the simulations becomes more and more important. This chapter gives an overview where and how stochasticity enters cellular life.

Chapter 8 – Network Structures, Dynamics and Function (W. Liebermeister, 3 lectures)

Many complex systems in biology can be represented as networks (reaction networks, interaction networks, regulatory networks). Studying the structure, dynamics, and function of such networks helps to understand design principles of living cells. In this chapter, important network structures such as motifs and modules as well as the dynamics resulting from them are discussed.

Chapter 9 – Optimality and Evolution (W. Liebermeister, E. Klipp, 3 lectures)

Theoretical research suggests that constraints of the evolutionary process should have left their marks in the construction and regulation of genes and metabolic pathways. In some cases, the function of biological systems can be well understood by models based on an optimality hypothesis. This chapter discusses the merits and limitations of such optimality approaches.

Various aspects of systems biology – the biological systems themselves, types of mathematical models to describe them, and practical techniques – reappear in different contexts in various parts of the book. The following diagram, which shows the contents of the book sorted by a number of different aspects, may serve as an orientation.

At the end of the regular course material, you will find a number of additional chapters that summarize important biological and mathematical methods. The first chapters deal with to cell biology (chapter 10, C. Wierling) and molecular biological methods (chapter 11, A. Kowald). For looking up mathematical and statistical definitions and methods, turn to chapters 12 and 13 (R. Herwig, A. Kowald). Chapters 14 and 15 (W. Liebermeister) concentrate on random processes and control theory. The final chapters provide an overview over useful databases (chapter 16, C. Wierling) as well as a huge list of available software tools including a short description of their purposes (chapter 17, A. Kowald).

Further material is available on an accompanying website (www.wiley-vch.de/home/systemsbiology)

Beside additional and more specialized topics, the website also contains solutions to the exercises and problems presented in the book.

We give our thanks to a number of people who helped us in finishing this book. We are especially grateful to Dr. Ulrich Liebermeister, Prof. Dr. Hans Meinhardt, Dr. Timo Reis, Dr. Ulrike Baur, Clemens Kreutz, Dr. Jose Egea, Dr. Maria Rodriguez-Fernandez, Dr. Wilhelm Huisinga, Sabine Hummert, Guy Shinar, Nadav Kashtan, Dr. Ron Milo, Adrian Jinich, Elad Noor, Niv Antonovsky, Bente Kofahl, Dr. Simon Borger, Martina Fröhlich, Christian Waltermann, Susanne Gerber, Thomas Spießer, Szymon Stoma, Christian Diener, Axel Rasche, Hendrik Hache, Dr. Michal Ruth Schweiger, and Elisabeth Maschke-Dutz for reading and commenting on the manuscript.

We thank the Max Planck Society for support and encouragement. We are grateful to the European Commission for funding via different European projects (MEST-CT2004-514169, LSHG-CT-2005-518254, LSHG-CT-2005-018942, LSHG-CT-2006-037469, LSHG-CT-2006-035995-2 NEST-2005-Path2-043310, HEALTH-F4-2007-200767, and LSHB-CT-2006-037712). Further funding was obtained from the Sysmo project “Translucent” and from the German Research Foundation (IRTG 1360) E.K. thanks with love her sons Moritz and Richard for patience and incentive and the Systems Biology community for motivation. W.L. wishes to thank his daughters Hannah and Marlene for various insights and inspiration. A.K. likes to thank Prof. Dr. H.E. Meyer for support and hospitality. This book is dedicated to our teacher Prof. Dr. Reinhart Heinrich (1946–2006), whose works on metabolic control theory in the 1970s paved the way to systems biology and who greatly inspired our minds.

Part One

Introduction to Systems Biology

2

Modeling of Biochemical Systems

2.1 Kinetic Modeling of Enzymatic Reactions

Summary

The rate of an enzymatic reaction, i.e., the velocity by which the execution of the reaction changes the concentrations of its substrates, is determined by concentrations of its substrates, concentration of the catalyzing enzyme, concentrations of possible modifiers, and by certain parameters. We introduce different kinetic laws for reversible and irreversible reactions, for reactions with varying numbers of substrates, and for reactions that are subject to inhibition or activation. The derivations of the rate laws are shown and the resulting restrictions for their validity and applicability. Saturation and sigmoidal kinetics are explained. The connection to thermodynamics is shown.

Deterministic kinetic modeling of individual biochemical reactions has a long history. The Michaelis–Menten model for the rate of an irreversible one-substrate reaction is an integral part of biochemistry, and the Km value is a major characteristic of the interaction between enzyme and substrate. Biochemical reactions are catalyzed by enzymes, i.e., specific proteins which often function in complex with cofactors. They have a catalytic center, are usually highly specific, and remain unchanged by the reaction. One enzyme molecule can catalyze thousands of reactions per second (this so-called turnover number ranges from 102 s−1 to 107 s−1). Enzyme catalysis leads to a rate acceleration of about 106- up to 1012-fold compared to the noncatalyzed, spontaneous reaction.

In this chapter, we make you familiar with the basic concepts of the mass action rate law. We will show how you can derive and apply more advanced kinetic expressions. The effect of enzyme inhibitors and activators will be discussed. The thermodynamic foundations and constraints are introduced.

The basic quantities are the concentration S of a substance S, i.e., the number n of molecules (or, alternatively, moles) of this substance per volume V, and the rate v of a reaction, i.e., the change of concentration S per time t. This type of modeling is macroscopic and phenomenological, compared to the microscopic approach, where single molecules and their interactions are considered. Chemical and biochemical kinetics rely on the assumption that the reaction rate v at a certain point in time and space can be expressed as a unique function of the concentrations of all substances at this point in time and space. Classical enzyme kinetics assumes for sake of simplicity a spatial homogeneity (the “well-stirred” test tube) and no direct dependency of the rate on time

(2.1)

In more advanced modeling approaches, longing toward whole-cell modeling, spatial inhomogeneities are taken into account, paying tribute to the fact that many components are membrane-bound or that cellular structures hinder the free movement of molecules. But, in the most cases, one can assume that diffusion is rapid enough to allow for an even distribution of all substances in space.