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Chemical master equation for large biological networks : state-space expansion methods using AI / / Don Kulasiri, Rahul Kosarwal
| Chemical master equation for large biological networks : state-space expansion methods using AI / / Don Kulasiri, Rahul Kosarwal |
| Autore | Kulasiri Don |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (231 pages) |
| Disciplina | 574.192 |
| Soggetto topico |
Biochemistry
Systems biology |
| ISBN | 981-16-5351-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Abbreviations -- Terminology -- Notations -- Key Factors and Outputs -- 1 Introduction -- 1.1 Chemical Kinetics and Stochastic Processes -- 1.2 Why Stochastic Processes? -- 1.2.1 Introduction -- 1.2.2 Fluctuations in Biological Systems -- 1.2.3 Experimental Observations -- 1.2.4 Advantages of Stochasticity -- 1.2.5 Thermodynamics of Biological Networks Far from Equilibrium -- 1.2.6 Why Stochastic Fluctuation Modelling? -- 1.2.7 Stochastic Modelling Methods -- 1.2.8 Theoretical Thermodynamic Modelling Approaches -- 1.2.9 Oscillatory Systems -- 1.3 The Purpose of This Book -- 1.4 Specific Objectives of the Monograph -- 1.5 The Organization of the Book -- References -- 2 A Review and Challenges in Chemical Master Equation -- 2.1 Markov Processes -- 2.2 Derivation of Chemical Master Equation -- 2.3 Adaptation of CME to Biological Networks -- 2.4 Generation-Recombination Markov Processes -- 2.5 Existing State-Space Expansion Methods -- 2.5.1 R-step Reachability Method -- 2.5.2 Stochastic Simulation Methods -- 2.6 Existing Numerical Methods for Approximation -- 2.6.1 Uniformisation Method -- 2.6.2 Krylov Subspace -- 2.7 Toy Biochemical Models -- 2.8 Conclusions -- Appendix A: Basic Probability -- References -- 3 Visualizing Markov Process Through Graphs and Trees -- 3.1 Introduction -- 3.1.1 Definitions and Preliminaries -- 3.2 Finite State Markov Chains as Sample Space -- 3.2.1 Sample Space for Biochemical Systems -- 3.2.2 States Classification of Markov Chain for Biochemical System -- 3.3 Markov Chain as a Markov Chain Tree -- 3.4 Problem State-Space Model of Biochemical Networks -- 3.5 Intelligent Search and Tracking -- 3.5.1 Artificial Intelligence for CME -- 3.5.2 Bayesian Likelihood Node Projection Function -- 3.6 Complexity of Optimal Solutions -- 3.7 Discussion and Conclusions -- References.
4 Intelligent State Projection -- 4.1 Introduction -- 4.2 Derivation of the Method Conditions -- 4.2.1 Expansion Criterion for States Space -- 4.2.2 Cease of Criterion After Updating -- 4.3 Latitudinal Search Strategy -- 4.3.1 Expansion and Update -- 4.3.2 Biological Example -- 4.4 Longitudinal Latitudinal Search -- 4.4.1 Expansion and Update -- 4.4.2 Biological Example -- 4.5 Data Structure Complexity of Operations -- 4.6 Discussion and Conclusion -- Appendix A.1 Complexity Based on Operations -- References -- 5 Comparative Study and Analysis of Methods and Models -- 5.1 Study Overview -- 5.2 Comparison Based on Catalytic Reaction System -- 5.3 Comparison Based on the Dual Enzymatic Reaction Network -- 5.4 Discussion and Conclusion -- References -- 6 A Large Model Case Study: Solving CME for G1/S Checkpoint Involving the DNA-Damage Signal Transduction Pathway -- 6.1 Introduction -- 6.1.1 What Happens in Normal Conditions? -- 6.1.2 What Happens in the Presence of a DNA-Damage Signal? -- 6.2 Model Integration -- 6.3 Computational Experiments -- 6.4 Discussion and Summary -- References -- 7 An Integrated Large Model Case Study: Solving CME for Oxidative Stress Adaptation in the Fungal Pathogen Candida Albicans -- 7.1 Introduction -- 7.1.1 Integrated Model Overview -- 7.2 Model Integration -- 7.2.1 Transporter Module -- 7.2.2 Antioxidant Module -- 7.2.3 Protein-Thiol Module -- 7.2.4 Signaling and Gene Expression Module -- 7.3 Computational Experiments -- 7.4 Discussion and Summary -- References. |
| Record Nr. | UNISA-996466729303316 |
Kulasiri Don
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| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Chemical master equation for large biological networks : state-space expansion methods using AI / / Don Kulasiri, Rahul Kosarwal
| Chemical master equation for large biological networks : state-space expansion methods using AI / / Don Kulasiri, Rahul Kosarwal |
| Autore | Kulasiri Don |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (231 pages) |
| Disciplina | 574.192 |
| Soggetto topico |
Biochemistry
Systems biology |
| ISBN | 981-16-5351-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Abbreviations -- Terminology -- Notations -- Key Factors and Outputs -- 1 Introduction -- 1.1 Chemical Kinetics and Stochastic Processes -- 1.2 Why Stochastic Processes? -- 1.2.1 Introduction -- 1.2.2 Fluctuations in Biological Systems -- 1.2.3 Experimental Observations -- 1.2.4 Advantages of Stochasticity -- 1.2.5 Thermodynamics of Biological Networks Far from Equilibrium -- 1.2.6 Why Stochastic Fluctuation Modelling? -- 1.2.7 Stochastic Modelling Methods -- 1.2.8 Theoretical Thermodynamic Modelling Approaches -- 1.2.9 Oscillatory Systems -- 1.3 The Purpose of This Book -- 1.4 Specific Objectives of the Monograph -- 1.5 The Organization of the Book -- References -- 2 A Review and Challenges in Chemical Master Equation -- 2.1 Markov Processes -- 2.2 Derivation of Chemical Master Equation -- 2.3 Adaptation of CME to Biological Networks -- 2.4 Generation-Recombination Markov Processes -- 2.5 Existing State-Space Expansion Methods -- 2.5.1 R-step Reachability Method -- 2.5.2 Stochastic Simulation Methods -- 2.6 Existing Numerical Methods for Approximation -- 2.6.1 Uniformisation Method -- 2.6.2 Krylov Subspace -- 2.7 Toy Biochemical Models -- 2.8 Conclusions -- Appendix A: Basic Probability -- References -- 3 Visualizing Markov Process Through Graphs and Trees -- 3.1 Introduction -- 3.1.1 Definitions and Preliminaries -- 3.2 Finite State Markov Chains as Sample Space -- 3.2.1 Sample Space for Biochemical Systems -- 3.2.2 States Classification of Markov Chain for Biochemical System -- 3.3 Markov Chain as a Markov Chain Tree -- 3.4 Problem State-Space Model of Biochemical Networks -- 3.5 Intelligent Search and Tracking -- 3.5.1 Artificial Intelligence for CME -- 3.5.2 Bayesian Likelihood Node Projection Function -- 3.6 Complexity of Optimal Solutions -- 3.7 Discussion and Conclusions -- References.
4 Intelligent State Projection -- 4.1 Introduction -- 4.2 Derivation of the Method Conditions -- 4.2.1 Expansion Criterion for States Space -- 4.2.2 Cease of Criterion After Updating -- 4.3 Latitudinal Search Strategy -- 4.3.1 Expansion and Update -- 4.3.2 Biological Example -- 4.4 Longitudinal Latitudinal Search -- 4.4.1 Expansion and Update -- 4.4.2 Biological Example -- 4.5 Data Structure Complexity of Operations -- 4.6 Discussion and Conclusion -- Appendix A.1 Complexity Based on Operations -- References -- 5 Comparative Study and Analysis of Methods and Models -- 5.1 Study Overview -- 5.2 Comparison Based on Catalytic Reaction System -- 5.3 Comparison Based on the Dual Enzymatic Reaction Network -- 5.4 Discussion and Conclusion -- References -- 6 A Large Model Case Study: Solving CME for G1/S Checkpoint Involving the DNA-Damage Signal Transduction Pathway -- 6.1 Introduction -- 6.1.1 What Happens in Normal Conditions? -- 6.1.2 What Happens in the Presence of a DNA-Damage Signal? -- 6.2 Model Integration -- 6.3 Computational Experiments -- 6.4 Discussion and Summary -- References -- 7 An Integrated Large Model Case Study: Solving CME for Oxidative Stress Adaptation in the Fungal Pathogen Candida Albicans -- 7.1 Introduction -- 7.1.1 Integrated Model Overview -- 7.2 Model Integration -- 7.2.1 Transporter Module -- 7.2.2 Antioxidant Module -- 7.2.3 Protein-Thiol Module -- 7.2.4 Signaling and Gene Expression Module -- 7.3 Computational Experiments -- 7.4 Discussion and Summary -- References. |
| Record Nr. | UNINA-9910502664103321 |
|
Kulasiri Don
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| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational modelling of multi-scale non-fickian dispersion in porous media : an approach based on stochastic calculus / / Don Kulasiri
| Computational modelling of multi-scale non-fickian dispersion in porous media : an approach based on stochastic calculus / / Don Kulasiri |
| Autore | Kulasiri Don |
| Pubbl/distr/stampa | Rijeka, Crotia : , : IntechOpen, , [2011] |
| Descrizione fisica | 1 online resource (244 pages) : illustrations |
| Disciplina | 620.116 |
| Soggetto topico |
Porous materials - Fluid dynamics
Hydrodynamics - Mathematical models |
| ISBN | 953-51-5702-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Computational modelling of multi-scale non-fickian dispersion in porous media |
| Record Nr. | UNINA-9910138251103321 |
|
Kulasiri Don
|
||
| Rijeka, Crotia : , : IntechOpen, , [2011] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Non-fickian Solute Transport in Porous Media : A Mechanistic and Stochastic Theory / / by Don Kulasiri
| Non-fickian Solute Transport in Porous Media : A Mechanistic and Stochastic Theory / / by Don Kulasiri |
| Autore | Kulasiri Don |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (227 p.) |
| Disciplina | 620.11696 |
| Collana | Advances in Geophysical and Environmental Mechanics and Mathematics |
| Soggetto topico |
Geophysics
Fluids Mathematical models Geophysics/Geodesy Fluid- and Aerodynamics Mathematical Modeling and Industrial Mathematics |
| ISBN | 3-642-34985-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | NonFickian Solute Transport -- Stochastic Differential Equations and Related Inverse Problems -- A Stochastic Model for Hydrodynamic Dispersion -- A Generalized Mathematical Model in One-dimension -- Theories of Fluctuations and Dissipation -- Multiscale, Generalised Stochastic Solute Transport Model in One Dimension -- The Stochastic Solute Transport Model in 2-Dimensions -- Multiscale Dispersion in 2 dimensions. |
| Record Nr. | UNINA-9910799232303321 |
|
Kulasiri Don
|
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||