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Data-driven selection and parameter estimation for DNA methylation mathematical models
Larson, Karen Zagkos, Loukas Mc Auley, Mark T. Roberts, Jason A. Kavallaris, Nikos I. Matzavinos, Anastasios
Larson, Karen
Zagkos, Loukas
Mc Auley, Mark T.
Roberts, Jason A.
Kavallaris, Nikos I.
Matzavinos, Anastasios
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2019-01-10
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Abstract
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
e ective 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
de ne 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 identifed 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.
Citation
Larson, K., Zagkos, L., Mc Auley, M., Roberts, J., Kavallaris, N. I., & Matzavinos, A. (2019). Data-driven selection and parameter estimation for DNA methylation mathematical models. Journal of Theoretical Biology, 467, 87-99.
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Elsevier
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Journal of Theoretical Biology
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Article
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en
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0022-5193
EISSN
1095-8541