This book constitutes the refereed proceedings of the International Conference on Computational Methods in Systems Biology, CMSB 2007, held in Edinburgh, Scotland, September 20-21, 2007.
The 16 revised full papers presented were carefully reviewed and selected. The papers present a variety of techniques from computer science, such as language design, concurrency theory, software engineering, and formal methods, for biologists, physicists, and mathematicians interested in the systems-level understanding of cellular processes.
Author(s): Muffy Calder, Stephen Gilmore
Series: Lecture Notes in Bioinformatics
Edition: 1
Publisher: Springer
Year: 2007
Language: English
Pages: 257
Frontmatter......Page 1
Background......Page 10
Illustrative Example: Unimolecular Decay......Page 11
Chemical Langevin Equation......Page 13
Gene Regulation Model......Page 14
Moments for Chemical Master Equation......Page 15
Moments for Chemical Langevin Equation......Page 17
Numerical Experiment for a Bimolecular Case......Page 18
References......Page 22
Introduction......Page 24
Stochastic Chemical Kinetics......Page 26
Relation to Continuous-Time Markov Chains......Page 27
Direct Stochastic Simulation of Single Models......Page 28
Simultaneous Stochastic Simulation of Multiple Models......Page 30
Mathematical Foundations of Importance Sampling......Page 31
Importance Sampling for Biological Networks......Page 32
Reversing Roles in Importance Sampling......Page 35
Runtime Comparison......Page 36
Conclusion......Page 38
References......Page 39
Introduction......Page 41
Biological Model......Page 42
The Dizzy model......Page 43
Dizzy......Page 44
Usual transcription and CoTC......Page 45
Labelling......Page 46
Description of the model......Page 47
Validation of the model......Page 50
Some analysis results......Page 53
Conclusions......Page 54
References......Page 55
Introduction......Page 57
Constraint-LTL over the Reals......Page 58
The Existential Fragment $\exists$ -Constraint-LTL......Page 59
Formula Instantiation Algorithm......Page 60
Cell Cycle Data......Page 63
MAPK Signal Transduction Data......Page 67
Experimental Data......Page 68
Conclusion......Page 70
References......Page 71
Introduction......Page 73
Context Sensitive Thomas Formalism......Page 74
Timed Automata......Page 77
Modeling the Components......Page 79
Capturing the Network Dynamics......Page 82
Context Sensitivity of Time Delays......Page 84
Perspectives......Page 87
References......Page 88
Introduction......Page 89
Multi-Compartment Gillespie Stochastic Model......Page 91
The Stochastic $\pi$@ Language......Page 92
Syntax and Semantics......Page 93
Chemical Reactions......Page 97
Biological Modellings......Page 98
Related Work......Page 101
Conclusion......Page 102
References......Page 103
Introduction......Page 105
Experimental Results......Page 106
Overview......Page 108
Details of the Simulation......Page 109
Summary......Page 113
References......Page 114
Introduction......Page 115
The Beta Workbench......Page 116
A Compositional Model For Signalling Pathways......Page 119
Evolutionary Framework......Page 121
Mutations......Page 122
An Example: MAPK Cascade......Page 125
References......Page 128
Introduction......Page 130
Related Work......Page 131
Stochastic Logical Networks......Page 132
Fast SLN Reconstruction Algorithm......Page 133
Inferring the Type of Regulation......Page 134
Measuring Quality of Reconstruction......Page 135
Experiments......Page 136
Discrete Boolean Network Model of Mammalian Cell Cycle......Page 137
Continuous Kinetic Model of Mammalian Cell Cycle......Page 138
Reconstruction of Mammalian Cell Cycle Network from Microarray Data......Page 139
Discussion......Page 140
References......Page 142
Introduction and Motivations......Page 145
The Narrative Language......Page 146
Case Study: A Narrative Model of the Gp130 Signalling Pathway......Page 150
Beta-binders......Page 153
The Translation Algorithm......Page 154
Case Study: The Translation of the Gp130 Signalling Pathway Model......Page 157
References......Page 159
Motivations......Page 161
A Process Calculi Based Stochastic Model......Page 165
Neuro-processes......Page 167
Paired Pulse Facilitation......Page 169
Short-Term Synaptic Depression......Page 171
Extending the Stochastic Model and Further Considerations......Page 173
References......Page 174
Introduction......Page 177
The nanoκ Calculus: Syntax and Semantics......Page 179
Markov Chains and the nanoκ Calculus......Page 184
nanoκ Calculus at Work: The Rotaxane Case Study......Page 186
Modeling the Rotaxane RaH in nanoκ Calculus......Page 187
Simulation Results......Page 189
References......Page 191
Introduction......Page 193
Biological Example......Page 194
Simulation Algorithm......Page 197
Correctness......Page 201
Implementation......Page 205
Conclusions......Page 206
References......Page 208
Motivation......Page 209
Biochemical Context......Page 210
Overview of the Framework......Page 211
Qualitative Modelling......Page 213
Qualitative Analysis......Page 214
Stochastic Modelling......Page 216
Stochastic Analysis......Page 218
Continuous Analysis......Page 221
Summary and Outlook......Page 223
References......Page 224
Introduction......Page 226
Modeling Biochemical Reactions as Chemical Graph Transformations......Page 227
Reconstructing Metabolic Pathways by Bidirectional Chemical Search......Page 229
Results and Discussion......Page 236
References......Page 240
Introduction......Page 242
Regulatory Graphs......Page 243
Regulatory Graphs and Multi-valued Decision Diagrams......Page 244
Analysis of Regulatory Circuits......Page 245
Functionality Context and Sign of an Interaction......Page 247
Functionality Context and Sign of a Regulatory Circuit......Page 248
Application to Regulatory Networks Controlling T Cell Activation and Differentiation......Page 251
T Cell Differentiation......Page 252
T Cell Activation......Page 254
Conclusion......Page 255
References......Page 256
Backmatter......Page 257
