Understanding the Dynamics of Biological Systems (eBook)

Understanding the Dynamics of Biological Systems 3
Preface 
5 
Contents 
11 
Contributors 
13 
Chapter 1 Effects of Protein Quality Control Machinery on Protein Homeostasis 
15 
1.1 Protein Folding is Catalyzed by a Complex Network of Reactions 15
1.1.1 Disruptions to the Protein Folding Network are Associated with Disease 16
1.1.2 The ER Functions as a Protein Folding Factory 17
1.1.3 Mathematical Models of Protein Quality Control Provide Novel Insights into the Regulation of Protein Assembly 18
1.2 Case Studies 18
1.2.1 Case Study I: Protein Folding Without Quality Control 18
1.2.1.1 Assumptions 18
1.2.1.2 Analytical Solution 19
1.2.1.3 Timescale Analysis 20
1.2.1.4 Conclusions for Case Study I 21
1.2.2 Case Study II: Protein Folding with Quality Control 21
1.2.2.1 Assumptions 22
1.2.2.2 Qualitative Dynamical Behavior and Equilibrium Points 23
1.2.2.3 Timescale Analysis 24
1.2.2.4 Parametric Sensitivity Analysis 26
1.2.2.5 Conclusions for Case Study II 28
1.3 Lessons Learned 29
Appendix 30
References 31
Chapter 2 Metabolic Network Dynamics: Properties and Principles 
32 
2.1 Introduction 32
2.2 Dynamic Mass Balances and Fundamental Subspaces 33
2.2.1 Key Considerations in Networks 35
2.2.2 Properties of Dynamic Systems 36
2.2.2.1 Underlying Structure of the Jacobian 37
2.2.2.2 Structural Similarity 37
2.2.2.3 Flux-Concentration Duality 37
2.2.2.4 Hierarchical Dynamics 38
2.3 Dual Jacobian Matrices 38
2.4 Stoichiometry Versus Gradients 39
2.5 Example: Folate Metabolism 40
2.5.1 Constituent Matrices and Subspaces 40
2.5.2 Hierarchical Pooling of Metabolites 42
2.5.3 Environmental Perturbations 44
2.6 Conclusions 46
2.6.1 Future Directions: Constructing Genome-Scale Models 47
References 49
Chapter 3 A Deterministic, Mathematical Modelfor Hormonal Control of the Menstrual Cycle 
51 
3.1 Introduction and Biological Background 51
3.2 The Pituitary and Ovarian Models 54
3.3 Fitting Parameters 59
3.4 Parameter Sensitivity and Bifurcations 64
3.5 Exogenous Hormone Effects 66
3.6 Conclusion 68
References 69
Chapter 4 Modeling Transport Processes and Their Implications for Chemical Disposition and Action 
71 
4.1 Introduction 71
4.1.1 The Fate of Chemicals in the Body 71
4.1.1.1 Absorption 71
4.1.1.2 Distribution 73
4.1.1.3 Metabolism 73
4.1.1.4 Excretion 74
4.1.2 Chemical and Pathophysiological-Mediated Alterations in Drug Disposition 74
4.1.3 Extrapolation of Data Between Biological Scenarios 75
4.2 Traditional Pharmacokinetic Approaches to Modeling Drug Disposition 76
4.3 More Complex Models of Drug Movement Across Biological Membranes 77
4.3.1 The Measurement of Chemical Movement Across Biological Membranes 77
4.3.2 General Considerations for Measuring Movement of Drugs Across Biological Membranes 78
4.3.2.1 Simple Versus Complex Measurement Systems 79
4.3.2.2 Ionization Status of the Drug 79
4.3.2.3 Heterogeneity in Drug Dispersion 80
4.3.2.4 Chemical Sequestration 80
4.3.2.5 Physico-Chemical Characteristics of the Chemical 81
4.3.2.6 ATP Usage Within the Test System 82
4.3.3 Measurement of Passive Diffusion 82
4.3.4 Measurement of Active Transport 84
4.4 The Integration of Drug Disposition and Drug Fate into a Predictive Model of the Life Cycle of a Drug in the Body 86
4.4.1 Multiple Drug Resistance Phenotype in Cancer Treatment 87
4.5 Summary 91
References 92
Chapter 5 Systems Biology of Tuberculosis: Insights for Drug Discovery 
95 
5.1 Introduction 95
5.2 Understanding Mtb: A Parts Catalogue 97
5.3 Assembling the Parts: Network Reconstruction 97
5.3.1 Annotation of Genomes 97
5.3.2 Impact of High-Throughput Experiments 100
5.4 Network Modeling and Simulation 100
5.4.1 Reconstruction of Mtb Metabolism 100
5.4.1.1 Flux Balance Analysis 101
5.4.1.2 Mycolic Acid Pathway 102
5.4.1.3 Genome-Scale Metabolic Models 105
5.4.2 Transcriptional Analysis 106
5.4.2.1 Transcriptional Regulatory Networks in Mtb 107
5.4.3 Analysis of the Mtb Interactome 108
5.5 Target Identification 109
5.5.1 Multi-Level Target Identification Pipeline: TargetTB 109
5.5.1.1 Importance of Systems-Based Approaches 112
5.5.2 Disruption of Metabolism 113
5.5.3 Tackling Resistance in Mtb 113
5.6 Interface with the Host: Modeling Host--Pathogen Interactions 114
5.6.1 Response Networks 114
5.6.2 Mechanistic Models of Immune System Dynamics 115
5.6.3 Boolean Modeling of Mtb-Human Interactions 116
5.7 Future Perspectives 117
References 118
Chapter 6 Qualitative Analysis of Genetic Regulatory Networks in Bacteria 
123 
6.1 Introduction 123
6.2 Carbon Starvation in E. coli 124
6.3 Modeling and Model Reduction 126
6.4 Qualitative Analysis of Dynamics 129
6.5 Formal Verification of Network Properties 133
6.6 Model Completion 134
6.7 Conclusions 136
References 138
Chapter 7 Modeling Antibiotic Resistance in Bacterial Colonies Using Agent-Based Approach 
143 
7.1 Introduction 143
7.1.1 MRSA Antibiotic Resistance Mechanisms 144
7.1.2 Overview of Modeling Approaches 146
7.1.3 Agent-Based Modeling Approach 147
7.2 Micro-Gen Bacterial Simulator 149
7.2.1 Environment 150
7.2.2 Bacterial Agents 151
7.2.2.1 Growth Parameters 152
7.2.2.2 Antibiotic Resistance Mechanisms 153
7.2.2.3 Overcrowding Algorithm 154
7.2.3 Antibiotics 154
7.2.4 -Lactamase Enzymes 156
7.2.5 Program Flow Structure 157
7.2.6 Parallelisation 158
7.3 Simulations of Bacteria--Antibiotic Interactions 159
7.4 Conclusions and Future Work 163
References 164
Chapter 8 Modeling the Spatial Pattern Forming Modules in Mitotic Spindle Assembly 
167 
8.1 Introduction 167
8.2 Microtubule Dynamics 170
8.2.1 Nucleation 170
8.2.2 Polymerization 171
8.3 Microtubule-Motor Interactions 172
8.3.1 Microtubule Gliding Assays 173
8.3.2 Motor Mechanics 173
8.3.3 Microtubule-Motor Patterns 173
8.4 Chromosome Dynamics 175
8.4.1 Search and Capture 175
8.4.2 Metaphase Plate Formation 176
8.5 Reaction-Diffusion Gradients of Microtubule Dynamics Regulation 177
8.5.1 Stathmin 177
8.5.2 RanGTP Nucleation and Stabilization Gradients 178
8.5.3 Long-Range Stabilization Gradients 178
8.6 Outlook 181
References 182
Chapter 9 Cell-Centred Modeling of Tissue Behaviour 
186 
9.1 Introduction: Towards a Virtual Cell Biology 186
9.2 Can Computation Cope with Cellular Complexity? 187
9.2.1 Being Generic: Function Versus Detail 188
9.3 Cells and Computation 188
9.4 Developing a Multi-Scale Model 189
9.5 The Agent Basis: The Communicating-Stream X-Machine 190
9.6 Biology, Physics, Chemistry and Computation 192
9.6.1 Forces on Cells 193
9.7 Hierarchy in Computational Models 194
9.8 Examples at Molecular and Cell Level 197
9.8.1 NF-B Signalling 198
9.8.2 Urothelium Monolayer Growth 199
9.8.3 Epidermis Multilayer Growth 200
9.9 A Framework for Multi-Scale Modeling 201
9.10 Describing Individual-Based Models 202
9.11 Visualisation and Graphical Output 203
9.12 Repeatability, Sensitivity Analysis and Validation 203
9.13 Lessons Learned 204
References 204
Chapter 10 Interaction-Based Simulations for Integrative Spatial Systems Biology 
206 
10.1 Introduction 206
10.2 Computer Modeling and Simulation in Integrative and Spatial Systems Biology 207
10.2.1 Dynamical Systems in Systems Biology 208
10.2.1.1 The Need of a Unifying Simulation Language 208
10.2.1.2 State and Evolution Function in Systems Biology 209
10.2.1.3 Dynamical Systems with a Dynamical Structure 210
10.2.1.4 Local Interactions 210
10.2.2 Individual-Based Models and Their Simulations 211
10.2.2.1 Multi-Agent Implementation 211
10.2.2.2 The Spatial Structure of Interactions 212
10.3 The MGS Domain-Specific Programming Language 213
10.3.1 Topological Collection 213
10.3.2 Transformation 214
10.3.3 Two Models of Diffusion 215
10.3.3.1 The Numerical Resolution of the Continuous Model 216
10.3.3.2 The Discrete Stochastic Evolution of a Diffusing Particle 216
10.4 A Synthetic Multicellular Bacterium 217
10.4.1 Synthetic Biology 218
10.4.2 The International Genetically Engineered Machine Competition 218
10.4.3 Objectives of the SMB Project 219
10.4.4 The Paris Team Proposal 220
10.5 Modeling in MGS 220
10.5.1 Solving Differential Equations 221
10.5.1.1 The SMB Proof of Concept 221
10.5.1.2 Analysis of the ODE Model 222
10.5.1.3 A Numerical Solution of Differential Equations 222
10.5.1.4 Interpretation of the Simulations' Results 224
10.5.2 Cellular Automata 226
10.5.2.1 The Spatial Organization of the SMB 226
10.5.2.2 A Discrete Spatial Framework 227
10.5.2.3 MGS Expression of a Cellular Automaton 229
10.5.2.4 Interpretation of the Simulations' Results 230
10.5.3 Stochastic Simulations 230
10.5.3.1 Robustness Analysis of the SMB Design 230
10.5.3.2 Stochastic Modeling for Sensitivity to Noise Analysis 232
10.5.3.3 Gillespie-Based Simulations in MGS 232
10.5.3.4 Interpretation of the Simulations' Results 233
10.5.4 Integrative Modeling 235
10.5.4.1 Description of the Model 235
10.5.4.2 Integration of the Two Models 236
10.5.4.3 Interpretation of the Simulations' Results 237
10.6 Related Work, Conclusions, and Perspectives 238
10.6.1 Related Work 238
10.6.2 Conclusions and Perspectives 238
Appendix 239
References 241
Glossary 243
Index 247