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Dr N Popovic

Top university

1 year ago

EastBio: Fitting mechanistic models of gene expression to single-cell data. University of Edinburgh in United Kingdom

Degree Level

PhD

Field of study

Cell Biology

Funding

Fully Funded

Deadline

Expired

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Country

United Kingdom

University

University of Edinburgh

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Keywords

Cell Biology
Physiology
Biochemistry
Computer Science
Machine Learning
Mathematics
Gene Expression
Statistical Analysis
Stochastic Processes
Developmental Biology
Content Analysis
Quantitative Genetics
Single Cell
Neural Network
Mrna Technology
Bioinformatic

About this position

It is nowadays possible to measure the mRNA numbers at the single cell level genome wide. While a large amount of this data is now readily available, its analysis has lagged behind. In particular it would be useful to use this data to obtain insight on how the parameters of gene expression, such as the transcription rate, the mRNA degradation rate and the rate at which genes turn on and off, vary from gene-to-gene, from cell-to-cell and with time and the type of environmental conditions. Inference of these parameters is possible by fitting stochastic models of gene expression to single-cell data.

In this project the student will use publicly available single-cell data (single molecule FISH, single-cell RNA sequencing) for yeast and mammalian cells to build a comprehensive set of mechanistic models of gene expression that can fit and explain the data. Unlike current models in the literature, the stochastic models will be mechanistic, i.e. incorporate a significant degree of realistic biological mechanisms including those underpinning gene-gene interactions and the coupling of transcription to cellular state. Inference of parameters will be achieved using a variety of existing methods include those based on maximum likelihood, Bayesian inference and neural-networks. It is also likely that during the PhD other methods will be developed to overcome challenges with the aforementioned methods.

The project will provide the student with a solid foundation in the analysis of single-cell data, neural-networks, inference methods and stochastic modelling. No previous background on these topics is assumed though some experience in coding is ideal. The project is ideal for a student with a mathematics, physics or computer science bachelors degree who is interested in quantitative biology. The student will be based in the C. H. Waddington building which houses the Centre for Engineering Biology at the University of Edinburgh.

Funding details

Fully Funded

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