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Logical modeling of biological systems / edited by Luis Fariñas del Cerro, Katsumi Inoue. -- Hoboken, NJ : Wiley, c2014. – (58.1057/L832) |
Contents
Contents
FOREWORD
CHAPTER 1. SYMBOLIC REPRESENTATION AND INFERENCE OF REGULATORY NETWORK STRUCTURES
1.1. Introduction: logical modeling and abductive inference in systems biology 1
1.2. Logical modeling of regulatory networks 4
1.3. Evaluation of the ARNI approach 21
1.4. ARNI assisted scientific methodology 33
1.5. Related work and comparison with non-symbolic approaches 40
1.6. Conclusions 43
1.7. Bibliography
CHAPTER 2. REASONING ON THE RESPONSE OF LOGICAL SIGNALING NETWORKS WITH ASP
2.1. Introduction
2.2. Answer set programming at a glance
2.3. Learn and control logical networks with ASP
2.4. Conclusion
2.5. Acknowledgments . .
2.6. Bibliography
CHAPTER 3. A LOGICAL MODEL FOR MOLECULAR INTERACTION MAPS 93
3.1. Introduction
3.2. Biological background
3.3. Logical model
3.4. Quantifier elimination for restricted formulas
3.5. Reasoning about interactions in metabolic interaction maps . .
3.6. Conclusion and future work
3.7. Acknowledgments
3.8. Bibliography
CHAPTER 4. ANALYZING LARGE NETWORK DYNAMICS WITH PROCESS HITTING 125
4.1. Introduction/state of the art
4.2. Discrete modeling with the process hitting
4.3. Static analysis of discrete dynamics
4.4. Toward a stochastic semantic
4.5. Biological Applications
4.6. Conclusion
4.7. Bibliography
CHAPTER 5. ASP FOR CONSTRUCTION AND VALIDATION OF REGULATORY BIOLOGICAL NETWORKS
5.1. Introduction
5.2. Preliminaries: ASP and biological logical networks
5.3. Temporal logics
5.3.2. ASP implementation of CTL and LTL
5.4. ASP-based analysis of a GRN
5.5. Conclusions
5.6. Acknowledgments
5.7. Appendix on an advanced modeling for taking into additive constraints
5.8. Bibliography 204
CHAPTER 6. SIMULATION-BASED REASONING ABOUT BIOLOGICAL PATHWAYS USING PETRI NETS AND ASP 207
6.1. Introduction 207
6.2. Background 209
6.3. Translating basic Petri net into ASP 215
6.4. Changing firing semantics 218
6.5. Extension - reset arcs 219
6.6. Extension - inhibitor arcs 222
6.7. Extension - read arcs 223
6.8. Extension - colored tokens 225
6.9. Translating Petri nets with colored tokens to ASP 227
6.10. Extension - priority transitions 230
6.11. Extension - timed transitions 232
6.12. Other extensions 233
6.13. Answering simulation-based reasoning questions 234
6.14. Retated work 240
6.15. Conclusion 241
6.16. Bibliography 241
CHAPTER 7. FORMAL METHODS APPLIED TO GENE NETWORK MODELING 245
7.1. Introduction 245
7.2. From gene interactions to gene network modeling 247
7.3. Logic: a tool for multidisciplinarity with experimental sciences 25 l
7.4. Thomas and Sifakis should have met 259
7.5. Consistency of biological hypotheses
7.6. Validation of biological hypotheses
7.7. Conclusion
7.8. Acknowledgments
7.9. Bibliography . .
CHAPTER 8. TEMPORAL LOGIC MODELING OF DYNAMICAL BEHAVIORS: FIRST-ORDER PATTERNS AND SOLVERS 291
8.1. Temporal logic FO-LTL(~Iin)
8.2. Formula patterns and dedicated solvers
8.3. Study case: coupled model of the cell cycle and the circadian clock . .
8.4. Related work
8.5. Conclusion
8.6. Bibliography
CHAPTER 9. ANALYZING SBGN-AF NETWORKS USING NORMAL LOGIC PROGRAMS 325
9.1. Introduction 325
9.2. The systems biology graphical notation 326
9.3. Normal logic programs 328
9.4. Translation of SBGN-AF into logic programming 332
9.5. Boolean modeling of SBGN-AF signaling networks dynamics 340
9.6. Discussion 356
9.7. Conclusion 358
9.8. Bibliography 359
CHAPTER 10. MACHINE LEARNING OF BIOLOGICAL NETWORKS USING ABDUCTIVE ILP 363
10.1. Introduction 363
10.2. Machine learning of metabolic networks applied to predictive toxicology
10.3. Multi-clause learning of metabolic control points 375
10.4. Learning a causal network from temporal gene expression data
10.5. Automatic construction of probabilistic trophic networks
10.6. Related work and discussion
10.7. Conclusions
10.8. Acknowledgments
10.9. Bibliography
LIST OF AUTHORS 403
INDEX 407
