Use this URL to cite or link to this record in EThOS: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713606 |
![]() |
|||||||
Title: | Inference in stochastic systems with temporally aggregated data | ||||||
Author: | Folia, Maria Myrto |
ISNI:
0000 0004 6351 6215
|
|||||
Awarding Body: | University of Manchester | ||||||
Current Institution: | University of Manchester | ||||||
Date of Award: | 2017 | ||||||
Availability of Full Text: |
|
||||||
Abstract: | |||||||
The stochasticity of cellular processes and the small number of molecules in a cell make deterministic models inappropriate for modelling chemical reactions at the single cell level. The Chemical Master Equation (CME) is widely used to describe the evolution of biochemical reactions inside cells stochastically but is computationally expensive. The Linear Noise Approximation (LNA) is a popular method for approximating the CME in order to carry out inference and parameter estimation in stochastic models. Data from stochastic systems is often aggregated over time. One such example is in luminescence bioimaging, where a luciferase reporter gene allows us to quantify the activity of proteins inside a cell. The luminescence intensity emitted from the luciferase experiments is collected from single cells and is integrated over a time period (usually 15 to 30 minutes), which is then collected as a single data point. In this work we consider stochastic systems that we approximate using the Linear Noise Approximation (LNA). We demonstrate our method by learning the parameters of three different models from which aggregated data was simulated, an Ornstein-Uhlenbeck model, a Lotka-Voltera model and a gene transcription model. We have additionally compared our approach to the existing approach and find that our method is outperforming the existing one. Finally, we apply our method in microscopy data from a translation inhibition experiment.
|
|||||||
Supervisor: | Rattray, Magnus | Sponsor: | Not available | ||||
Qualification Name: | Thesis (Ph.D.) | Qualification Level: | Doctoral | ||||
EThOS ID: | uk.bl.ethos.713606 | DOI: | Not available | ||||
Keywords: | Stochastic Systems ; linear noise approximation ; kalman filter ; single cell data | ||||||
Share: |