Systems Biology Constraint Based Reconstruction and Analysis 2nd edition by BERNHARD PALSSON ISBN 1107038855 978-1107038851

1 Introduction

1.1 The Genotype–Phenotype Relationship

1.2 Some Concepts of Genome-scale Science

1.3 The Emergence of Systems Biology

1.4 Building Foundations

1.5 About This Book

1.6 Summary

Part I Network Reconstruction

2 Network Reconstruction: The Concept

2.1 Many Reactions and Their Stoichiometry

2.2 Reconstructing a Pathway

2.3 Module-by-module Reconstruction

2.4 Proteins and Their Many States

2.5 Central E. coli Energy Metabolism

2.6 Genome-scale Networks

2.7 Summary

3 Network Reconstruction: The Process

3.1 Building Knowledge Bases

3.2 Reconstruction is a Four-step Process

3.3 Reconstruction is Iterative and Labor-intensive

3.4 The Many Uses of Reconstructions

3.5 Summary

4 Metabolism in Escherichia coli

4.1 Some Basic Facts about E. coli

4.2 History

4.2.1 Pre-genome era reconstructions

4.2.2 Genome era reconstructions

4.3 Content of the iJO1366 Reconstruction

4.4 From a Reconstruction to a Computational Model

4.5 Validation of iJO1366

4.6 Uses of the E. coli GEM

4.7 Summary

5 Prokaryotes

5.1 State of The Field

5.2 Metabolism in Pathogens

5.3 Metabolism in Blue-Green Algae

5.4 Metabolism in Microbial Communities

5.4.1 Systems biology of communities

5.4.2 Model-based analysis of microbial communities

5.5 An Environmentally Important Organism

5.5.1 Geobacter sulfurreducens

5.5.2 Genome-scale science for Geobacter

5.6 Summary

6 Eukaryotes

6.1 Metabolism in Saccharomyces cerevisiae

6.1.1 Reconstruction and its uses

6.1.2 Community-based reconstruction

6.2 Metabolism in Chlamydomonas reinhardtii

6.2.1 Metabolic network reconstruction

6.2.2 Description of photon usage

6.3 Metabolism in Homo sapiens

6.3.1 Recon 1

6.3.2 Uses of Recon 1

6.3.3 Building multi-cell and multi-tissue reconstructions

6.3.4 Mapping Recon 1 onto other mammals

6.3.5 Recon 2

6.4 Summary

7 Biochemical Reaction Networks

7.1 Protein Properties

7.2 Structural Biology

7.3 Transcription and Translation

7.4 Integrating Network Reconstructions

7.5 Signaling Networks

7.6 Summary

8 Metastructures of Genomes

8.1 The Concept of a Metastructure

8.2 Transcriptional Regulatory Networks

8.3 Refactoring DNA for Synthetic Biology

8.4 The Challenge of Polyomic Data Integration

8.5 Building Mathematical Descriptions

8.6 Summary

Part II Mathematical Properties of Reconstructed Networks

9 The Stoichiometric Matrix

9.1 The Many Attributes of S

9.2 Chemistry: S as a Data Matrix

9.2.1 Elementary biochemical reactions

9.2.2 Basic chemistry

9.2.3 Example: glycolysis

9.3 Network Structure: S as a Connectivity Matrix

9.3.1 The maps of S

9.3.2 Biological quantities displayed on maps

9.3.3 Linearity of maps

9.4 Mathematics: S as a Linear Transformation

9.4.1 Mapping fluxes onto concentration time derivatives

9.4.2 The four fundamental subspaces

9.4.3 Looking into the four fundamental subspaces

9.5 Systems Science: S and Network Models

9.6 Summary

10 Simple Topological Network Properties

10.1 The Binary Form of S

10.2 Participation and Connectivity

10.2.1 Rearranging the stoichiometric matrix

10.2.2 Connectivities in genome-scale matrices

10.3 Linked Participation and Connectivities

10.3.1 The adjacency matrices of S

10.3.2 Computation of the adjacency matrices

10.4 Summary

11 Fundamental Network Properties

11.1 Singular Value Decomposition

11.1.1 Decomposition into three matrices

11.1.2 The content of U, Σ, and V

11.1.3 Key properties of the SVD

11.2 SVD and Properties of Reaction Networks

11.3 Studying Elementary Reactions using SVD

11.3.1 The linear reversible reaction

11.3.2 The bi-linear association reaction

11.4 Studying Network Structure Using SVD

11.5 Drivers and Directions

11.5.1 Directions: the column space

11.5.2 Drivers: the row space

11.5.3 The fundamental subspaces are of a finite size

11.6 Summary

12 Pathways

12.1 Network-based Pathway Definitions

12.2 Choice of a Basis

12.3 Confining the Steady-state Flux Vector

12.3.1 Finite or closed spaces

12.3.2 Importance of constraints

12.4 Pathways as Basis Vectors

12.4.1 Some perspective

12.4.2 Extreme pathways

12.4.3 Classifying extreme pathways

12.4.4 The simplest set of linearly independent basis vectors

12.4.5 Examples of pathway computation

12.5 Summary

13 Use of Pathway Vectors

13.1 The Matrix of Pathway Vectors

13.2 Pathway Length and Flux Maps

13.3 Reaction Participation and Correlated Subsets

13.4 Input–output Relationships and Crosstalk

13.5 Regulation Eliminates Active Pathways

13.6 Summary

14 Randomized Sampling

14.1 The Basics

14.2 Sampling Low-dimensional Spaces

14.3 Sampling High-dimensional Spaces

14.4 Sampling Network States in Human Metabolism

14.5 Summary

Part III Determining the Phenotypic Potential of Reconstructed Networks

15 Dual Causality

15.1 Causation in Physics and Biology

15.2 Building Quantitative Models

15.2.1 The physical sciences

15.2.2 The life sciences

15.2.3 Genome-scale models

15.3 Constraints in Biology

15.4 Summary

16 Functional States

16.1 Components vs. Systems

16.2 Properties of Links

16.3 Links to Networks to Biological Functions

16.4 Constraining Allowable Functional States

16.5 Biological Consequences of Constraints

16.6 Summary

17 Constraints

17.1 Genome-scale Viewpoints

17.2 Stating and Imposing Constraints

17.3 Capacity Constraints

17.4 Constraints from Chemistry

17.4.1 Mass conservation

17.4.2 Thermodynamics

17.4.3 Fluxomics

17.5 Regulatory Constraints

17.6 Coupling Constraints

17.7 Simultaneous Satisfaction of All Constraints

17.8 Summary

18 Optimization

18.1 Overview of Constraint-based Methods

18.2 Finding Functional States

18.3 Linear Programming: the basics

18.4 Genome-scale Models

18.5 Summary

19 Determining Capabilities

19.1 Optimal Network Performance

19.1.1 Co-factors

19.1.2 Biosynthetic Precursors

19.2 Production of ATP

19.2.1 Producing ATP aerobically from glucose

19.2.2 Producing ATP anaerobically from glucose

19.2.3 Optimal ATP production from other substrates

19.3 Production of Redox Potential

19.3.1 Aerobic production of NADH from glucose

19.3.2 Anaerobic production of NADH

19.4 Capabilities of Genome-scale Models

19.5 Summary

20 Equivalent States

20.1 Equivalent Ways to Reach a Network Objective

20.2 Flux Variability Analysis

20.2.1 The concept

20.2.2 Flux variability in the core E. coli model

20.2.3 Genome-scale results

20.3 Extreme Pathways and Optimal States

20.3.1 The concept

20.3.2 Extreme pathways in the core E. coli metabolic network

20.3.3 Genome-scale results

20.4 Enumerating Alternative Optima

20.5 Summary

21 Distal Causation

21.1 The Objective Function

21.2 Types of Objective Functions

21.3 Producing Biomass

21.4 Formulating The Biomass Objective Function

21.5 Studying the Objective Function

21.6 Objective Functions in Practice

21.7 Summary

Part IV Basic and Applied Uses

22 Environmental Parameters

22.1 Varying a Single Parameter

22.1.1 Robustness analysis

22.1.2 The effects of oxygen on ATP production

22.1.3 The effects of oxygen uptake rate on growth rate

22.1.4 Sensitivity with respect to key processes

22.1.5 Uses of robustness analysis

22.2 Varying Two Parameters

22.2.1 Phenotypic phase planes

22.2.2 Using the PhPP at a small scale

22.2.3 Using the PhPP at the genome-scale

22.3 Summary

23 Genetic Parameters

23.1 Single Gene Knock-outs

23.1.1 Concept

23.1.2 Core E. coli metabolic network

23.1.3 Genome-scale studies of essential genes

23.1.4 Studying non-lethal gene KOs

23.2 Double Gene Knock-outs

23.2.1 Core E. coli metabolic network

23.2.2 Genome-scale studies

23.3 Gene Dosage and Sequence Variation

23.4 Summary

24 Analysis of Omic Data

24.1 Context for Content

24.2 Omics Data-mapping and Network Topology

24.3 Omics Data as Constraints

24.4 Omics Data and Validation of GEM Predictions

24.5 Summary

25 Model-Driven Discovery

25.1 Models Can Drive Discovery

25.2 Predicting Gap-filling Reactions

25.3 Predicting Metabolic Gene Functions

25.4 Summary

26 Adaptive Laboratory Evolution

26.1 A New Line of Biological Inquiry

26.2 Determining the Genetic Basis

26.3 Interpretation of Outcomes

26.4 A Specific Example of Nutrient Adaptation

26.5 General Uses of ALE

26.6 Complex Examples of Adaptive Evolution

26.7 Summary

27 Model-driven Design

27.1 Historical Background

27.2 GEMs and Design Algorithms

27.3 GEMs and Cell Factory Design

27.4 Summary

Part V Conceptual Foundations

28 Teaching Systems Biology

28.1 The Core Paradigm

28.2 High-throughput Technologies

28.3 Network Reconstruction

28.4 Computing Functional States of Networks

28.4.1 Conversion to a computational model

28.4.2 Topological properties

28.4.3 Determining the capabilities of networks

28.4.4 Dynamic states

28.5 Prospective Experimentation

28.6 Building a Curriculum

28.7 Summary

29 Epilogue

29.1 The Brief History of COBRA

29.2 Common Misunderstandings

29.3 Questions in Biology and in Systems Biology

29.4 Why Build Mathematical Models?

29.5 What Lies Ahead?

References

Index