Bayesian inversion of a diffusion model with application to biology
Research output: Contribution to journal › Article › peer-review
Abstract
A common task in experimental sciences is to fit mathematical models to real-world measurements to improve understanding of natural phenomenon (reverse-engineering or inverse modelling). When complex dynamical systems are considered, such as partial differential equations, this task may become challenging or ill-posed. In this work, a linear parabolic equation is considered as a model for protein transcription from MRNA. The objective is to estimate jointly the differential operator coefficients, namely the rates of diffusion and self-regulation, as well as a functional source. The recent Bayesian methodology for infinite dimensional inverse problems is applied, providing a unique posterior distribution on the parameter space continuous in the data. This posterior is then summarized using a Maximum a Posteriori estimator. Finally, the theoretical solution is illustrated using a state-of-the-art MCMC algorithm adapted to this non-Gaussian setting.
Bibliographical metadata
Original language | English |
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Article number | 13 |
Journal | Journal of Mathematical Biology |
Volume | 83 |
Issue number | 2 |
Early online date | 6 Jul 2021 |
DOIs | |
Publication status | Published - 1 Aug 2021 |