Chemical master equation for large biological networks : state-space expansion methods using AI / / Don Kulasiri, Rahul Kosarwal

• Autore: Kulasiri Don
• Pubblicazione: Singapore : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (231 pages)
• Disciplina: 574.192
• Soggetto topico: Biochemistry; Systems biology
• Persona (resp. second.): KosarwalRahul
• 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.
• Titolo autorizzato: Chemical Master Equation for Large Biological Networks
• ISBN: 981-16-5351-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910502664103321
• Lo trovi qui: Univ. Federico II