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Title
Chemical master equation for large biological networks : state-space expansion methods using AI / Don Kulasiri, Rahul Kosarwal.
ISBN
9789811653513 (electronic bk.)
9811653518 (electronic bk.)
981165350X
9789811653506
Imprint
Singapore : Springer, 2021.
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-981-16-5351-3 doi
Call Number
QP514.2
Dewey Decimal Classification
572
Summary
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, but also reveal their potential by means of practical examples. The case studies presented are mainly from biology; however, the applications from novel methods are discussed comprehensively, underlining the interdisciplinary approach in simulation and the potential of the chemical master equation approach for modelling bionetworks. The book is a valuable guide for researchers, graduate students, and professionals alike.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 22, 2021).
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.
