Data-driven selection and parameter estimation for DNA methylation mathematical models
| Autor: | Loukas Zagkos, Anastasios Matzavinos, Jason A. Roberts, Nikos I. Kavallaris, Mark T. Mc Auley, Karen Larson |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Statistics and Probability Aging Computational biology Biology General Biochemistry Genetics and Molecular Biology Epigenesis Genetic 03 medical and health sciences 0302 clinical medicine Humans Epigenetics Selection (genetic algorithm) Biological data General Immunology and Microbiology Mathematical model Applied Mathematics Model selection Bayes Theorem Promoter General Medicine Methylation DNA Methylation Models Theoretical 030104 developmental biology Nonlinear Dynamics Health Modeling and Simulation DNA methylation General Agricultural and Biological Sciences Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Journal of Theoretical Biology. 467:87-99 |
| ISSN: | 0022-5193 |
| DOI: | 10.1016/j.jtbi.2019.01.012 |
| Popis: | Epigenetics is coming to the fore as a key process which underpins health. In particular emerging experimental evidence has associated alterations to DNA methylation status with healthspan and aging. Mammalian DNA methylation status is maintained by an intricate array of biochemical and molecular processes. It can be argued changes to these fundamental cellular processes ultimately drive the formation of aberrant DNA methylation patterns, which are a hallmark of diseases, such as cancer, Alzheimer’s disease and cardiovascular disease. In recent years mathematical models have been used as effective tools to help advance our understanding of the dynamics which underpin DNA methylation. In this paper we present linear and nonlinear models which encapsulate the dynamics of the molecular mechanisms which define DNA methylation. Applying a recently developed Bayesian algorithm for parameter estimation and model selection, we are able to estimate distributions of parameters which include nominal parameter values. Using limited noisy observations, the method also identified which methylation model the observations originated from, signaling that our method has practical applications in identifying what models best match the biological data for DNA methylation. |
| Databáze: | OpenAIRE |
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