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Record Nr. |
UNINA9910502664103321 |
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Autore |
Kulasiri Don |
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Titolo |
Chemical Master Equation for Large Biological Networks : State-space Expansion Methods Using AI / / by Don Kulasiri, Rahul Kosarwal |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (231 pages) |
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Collana |
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Physics and Astronomy Series |
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Disciplina |
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Soggetti |
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Mathematical physics |
Computer simulation |
Bioinformatics |
Biomathematics |
Computational intelligence |
Artificial intelligence |
Computational Physics and Simulations |
Computational and Systems Biology |
Mathematical and Computational Biology |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1. Introduction -- 2. A Review and Challenges in Chemical Master Equation -- 3. Visualizing Markov Process through Graphs and Trees -- 4. Intelligent State Projection -- 5. Comparative Study And Analysis of Methods and Models -- 6. A Large Model Case Study: Solving CME for G1/S Checkpoint Involving the DNA-damage Signal Transduction Pathway -- 7. An Integrated Large Model Case Study: Solving CME for Oxidative Stress Adaptation in the Fungal Pathogen Candida Albicans. |
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Sommario/riassunto |
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This book highlights the theory and practical applications of the chemical master equation (CME) approach for very large biochemical networks, which provides a powerful general framework for model building in a variety of biological networks. The aim of the book is to not only highlight advanced numerical solution methods for the CME, |
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