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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Άλλοι συγγραφείς: Inoue, Katsumi (Επιμελητής έκδοσης), Fariñas del Cerro, Luis (Επιμελητής έκδοσης)
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : ISTE, Ltd. ; 2014.
Hoboken : Wiley, 2014.
Σειρά:ISTE.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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049 |a MAIN 
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. ;  |c 2014. 
264 1 |a Hoboken :  |b Wiley,  |c 2014. 
300 |a 1 online resource (429 pages). 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a ISTE 
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. 
650 0 |a Biological systems  |x Computer simulation. 
650 4 |a Biology  |x Methodology. 
650 4 |a Biology  |x Philosophy. 
650 4 |a Evolution (Biology) 
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  |0 (OCoLC)fst00832337 
655 4 |a Electronic books. 
655 0 |a Electronic books. 
700 1 |a Inoue, Katsumi,  |e editor. 
700 1 |a Fariñas del Cerro, Luis,  |e editor. 
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://doi.org/10.1002/9781119005223  |z Full Text via HEAL-Link 
994 |a 92  |b DG1