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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken :
ISTE, Ltd. ; Wiley,
2014.
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| Colección: | ISTE.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Logical modeling of biological systems / |c edited by Luis Fariñas del Cerro, Katsumi Inoue. |
| 264 | 1 | |a London : |b ISTE, Ltd. ; |a Hoboken : |b Wiley, |c 2014. | |
| 300 | |a 1 online resource (429 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 546 | |a Text in English. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Chapter 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; 1.2.1. Background; 1.2.2. Logical model of signed-directed networks; 1.2.2.1. Prior knowledge; 1.2.2.2. Rule-based underlying model; 1.2.2.3. Integrity constraints; 1.2.2.4. Inferring signed-directed networks and explanatory reasoning; 1.3. Evaluation of the ARNI approach; 1.3.1. ARNI predictive power. | |
| 505 | 0 | |a 1.3.1.1. Prediction under biological and experimental noise1.3.1.2. Prediction under incomplete data; 1.3.2. ARNI expressive power; 1.3.2.1. Network motif representations; 1.3.2.2. Representing complex interactions; 1.4. ARNI assisted scientific methodology; 1.4.1. Testing biological hypotheses; 1.4.1.1. Testing cross-talk between signaling pathways; 1.4.2. Informative experiments for networks discrimination; 1.5. Related work and comparison with non-symbolic approaches; 1.5.1. Limitations and future work; 1.6. Conclusions; 1.7. Bibliography. | |
| 505 | 0 | |a Chapter 2. Reasoning on the Response of Logical Signaling Networks with ASP2.1. Introduction; 2.2. Answer set programming at a glance; 2.3. Learn and control logical networks with ASP; 2.3.1. Preliminaries; 2.3.2. Reasoning on the response of logical networks; 2.3.3. Learning models of immediate-early response; 2.3.4. Minimal intervention strategies; 2.3.5. Software toolbox: caspo; 2.4. Conclusion; 2.5. Acknowledgments; 2.6. Bibliography; Chapter 3. A Logical Model for Molecular Interaction Maps; 3.1. Introduction; 3.2. Biological background; 3.3. Logical model. | |
| 505 | 0 | |a 3.3.1. Activation and inhibition3.3.1.1. Activation and inhibition capacities; 3.3.1.2. Relations between the activation and inhibition causes and effects; 3.3.1.3. Relations between causal relations; 3.3.2. Model extension; 3.3.2.1. Phosphorylation; 3.3.2.2. Autophosphorylation; 3.3.2.3. Binding; 3.3.3. Causality relations redefinition; 3.3.3.1. Activation axioms; 3.3.3.2. Phosphorylation axioms; 3.3.3.3. Autophosphorylation axioms; 3.3.3.4. Binding axioms; 3.3.3.5. Inhibition axioms; 3.4. Quantifier elimination for restricted formulas; 3.4.1. Domain formulas; 3.4.2. Restricted formulas. | |
| 505 | 0 | |a 3.4.3. Completion formulas3.4.4. Domain of domain formulas; 3.4.5. Quantifier elimination procedure; 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; 4.1. Introduction/state of the art; 4.1.1. The modeling challenge; 4.1.2. Historical context: Boolean and discrete models; 4.1.3. Analysis issues; 4.1.4. The process hitting framework; 4.1.5. Outline; 4.2. Discrete modeling with the process hitting; 4.2.1. Motivation. | |
| 500 | |a 4.2.2. The process hitting framework. | ||
| 520 | |a 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 the parts considered separately. Systems Biology is therefore more than just an emerging field: it represents a new way of thinking about biology with a dramatic impact on the way that research is performed. The logical approach provides an intuitive method to provide explanations based on an expr. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Biological systems |x Computer simulation. | |
| 650 | 6 | |a Systèmes biologiques |x Simulation par ordinateur. | |
| 650 | 7 | |a NATURE |x Animals |x Wildlife. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Microscopes & Microscopy. |2 bisacsh | |
| 650 | 7 | |a Biological systems |x Computer simulation |2 fast | |
| 700 | 1 | |a Inoue, Katsumi, |e editor. | |
| 700 | 1 | |a Fariñas del Cerro, Luis, |e editor. | |
| 758 | |i has work: |a Logical modeling of biological systems (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGJTvD7pdMjFCXbvx9Gk8P |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Inoue, Katsumi. |t Logical Modeling of Biological Systems. |d Hoboken : Wiley, ©2014 |z 9781848216808 |
| 830 | 0 | |a ISTE. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1765112 |z Texto completo |
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