Solving chemical master equation for large biological networks: Novel state-space expansion algorithm based on artificial intelligence and Bayesian standards: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
dc.contributor.author | Kosarwal, Rahul | |
dc.date.accessioned | 2020-10-06T00:52:08Z | |
dc.date.available | 2020-10-06T00:52:08Z | |
dc.date.issued | 2019 | |
dc.description.abstract | Numerical solutions of the chemical master equation (CME) are gaining increasing attention with the need to understand the stochasticity of biochemical systems. In this thesis, we aim to develop the intelligent state projection (πΌππ) method to use in the stochastic analysis of these systems. For any biochemical reaction network, it is important to capture more than one moment to describe the dynamic behaviour of the system. πΌππ is based on a state-space search and the data structure standards of artificial intelligence (π΄πΌ) to explore and update the states of a biochemical system. To support the expansion in πΌππ, we also develop a Bayesian likelihood node projection (π΅πΏππ) function to predict the likelihood of the states. The proposed algorithm systematically expands the projection space based on predefined inputs, which are useful in providing accuracy in the approximation (Σ) and an exact analytical solution at the time of interest. We also discuss the applications of the πΌππ algorithm for realistic models and compare this with other existing, and latest, domain expansion methods based on the r-step reachability of the finite state projection (πΉππ) and the stochastic simulation algorithm (πππ΄). In all examples, our proposed methods perform better in terms of speed and accuracy of the expansion, the accuracy of the solution, and provide a better understanding of the state-space of the system. | en |
dc.identifier.uri | https://hdl.handle.net/10182/12932 | |
dc.identifier.wikidata | Q112949092 | |
dc.language.iso | en | |
dc.publisher | Lincoln University | |
dc.rights.uri | https://researcharchive.lincoln.ac.nz/pages/rights | |
dc.subject | biochemical reaction | en |
dc.subject | Markov chains | en |
dc.subject | stochastic simulation | en |
dc.subject | chemical master equation | en |
dc.subject | intelligent state projection | en |
dc.subject | Bayesian | en |
dc.subject | mathematical modelling | en |
dc.subject | ordinary differential equations | en |
dc.subject | state-space | en |
dc.subject | oxidative stress | en |
dc.subject | Candida albicans | en |
dc.subject | fungal pathogens | en |
dc.subject | artificial intelligence | en |
dc.subject | bioinformatics | en |
dc.subject | computational biology | en |
dc.subject | Biophysics | en |
dc.subject | simulation | en |
dc.subject | modelling | en |
dc.subject.anzsrc | ANZSRC::0103 Numerical and Computational Mathematics | en |
dc.subject.anzsrc | ANZSRC::060102 Bioinformatics | en |
dc.subject.anzsrc | ANZSRC::010406 Stochastic Analysis and Modelling | en |
dc.subject.anzsrc | ANZSRC::080110 Simulation and Modelling | en |
dc.subject.anzsrc | ANZSRC::08 Information and Computing Sciences | en |
dc.subject.anzsrc | ANZSRC::029901 Biological Physics | en |
dc.title | Solving chemical master equation for large biological networks: Novel state-space expansion algorithm based on artificial intelligence and Bayesian standards: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University | en |
dc.type | Thesis | en |
lu.contributor.unit | Department of Wine, Food and Molecular Biosciences | |
lu.contributor.unit | Centre for Advanced Computational Solutions | |
lu.thesis.supervisor | Kulasiri, Don | |
lu.thesis.supervisor | Samarasinghe, Sandhya | |
thesis.degree.grantor | Lincoln University | en |
thesis.degree.level | Doctoral | en |
thesis.degree.name | Doctor of Philosophy | en |
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