Logical modeling of biological systems
Systems Biology is the systematic study of the interactions between the components of a biological system and studies how these interactions give rise to the function and behavior of the living system. Through this, a life process is to be understood as a whole system rather than the collection of t...
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| Hlavní autoři: | , |
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| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
London
Wiley
2014
Hoboken, N.J ISTE ISTE Press Wiley-Blackwell |
| Vydání: | 1st ed. |
| Edice: | Bioengineering and health science series |
| Témata: | |
| ISBN: | 9781119015338, 9781848216808, 1848216807, 1119015332 |
| On-line přístup: | Získat plný text |
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- Intro -- Contents -- Title Page -- Copyright -- Foreword -- 1 Symbolic Representation and Inference of Regulatory Network Structures -- 1.1. Introduction: logical modeling and abductive inference in systems biology -- 1.2. Logical modeling of regulatory networks -- 1.3. Evaluation of the ARNI approach -- 1.4. ARNI assisted scientific methodology -- 1.5. Related work and comparison with non-symbolic approaches -- 1.6. Conclusions -- 1.7. Bibliography -- 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 -- 3 A Logical Model for Molecular Interaction Maps -- 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 -- 4 Analyzing Large Network Dynamics with Process Hitting -- 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 -- 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 -- 5.4. 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 -- 6 Simulation-based Reasoning about Biological Pathways Using Petri Nets and ASP -- 6.1. Introduction -- 6.2. Background -- 6.3. Translating basic Petri net into ASP
- 10.6. Related work and discussion -- 10.7. Conclusions -- 10.8. Acknowledgments -- 10.9. Bibliography -- List of Authors -- Index
- 6.4. Changing firing semantics -- 6.5. Extension - reset arcs -- 6.6. Extension - inhibitor arcs -- 6.7. Extension - read arcs -- 6.8. Extension - colored tokens -- 6.9. Translating Petri nets with colored tokens to ASP -- 6.10. Extension - priority transitions -- 6.11. Extension - timed transitions -- 6.12. Other extensions -- 6.13. Answering simulation-based reasoning questions -- 6.14. Related work -- 6.15. Conclusion -- 6.16. Bibliography -- 7 Formal Methods Applied to Gene Network Modeling -- 7.1. Introduction -- 7.2. From gene interactions to gene network modeling -- 7.3. Logic: a tool for multidisciplinarity with experimental sciences -- 7.4. Thomas and Sifakis should have met -- 7.5. Consistency of biological hypotheses -- 7.6. Validation of biological hypotheses -- 7.7. Conclusion -- 7.8. Acknowledgments -- 7.9. Bibliography -- 8 Temporal Logic Modeling of Dynamical Behaviors: First-Order Patterns and Solvers -- 8.1. Temporal logic FO-LTL(Rlin) -- 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 -- 9 Analyzing SBGN-AF Networks Using Normal Logic Programs -- 9.1. Introduction -- 9.2. The systems biology graphical notation -- 9.3. Normal logic programs -- 9.4. Translation of SBGN-AF into logic programming -- 9.5. Boolean modeling of SBGN-AF signaling networks dynamics -- 9.6. Discussion -- 9.7. Conclusion -- 9.8. Bibliography -- 10 Machine Learning of Biological Networks using Abductive ILP -- 10.1. Introduction -- 10.2. Machine learning of metabolic networks applied to predictive toxicology -- 10.3. Multi-clause learning of metabolic control points -- 10.4. Learning a causal network from temporal gene expression data -- 10.5. Automatic construction of probabilistic trophic networks