Dr Michael Tellier

Supervisor Details

Contact Details

Dr Michael Tellier

Scientific Background

Research Interests

The Tellier lab is interested in understanding how protein phosphorylation contribute to the regulation of transcription and co-transcriptional processes, including pre-mRNA splicing and mRNA cleavage and polyadenylation.

To achieve this we use cutting-edge molecular, genomic, biochemical, and cellular approaches in human cell lines.

Ultimately, our motivation is to understand how protein phosphorylation regulate the production of mature mRNAs in normal cell biology to develop therapeutic approaches to counteract its perturbation in cancer and other human diseases.

Research Groups

Supervision Style

In three words or phrases: Student-centred, outcome-oriented, collaborative.

Provision of Training

I prefer to take responsibility for your technical training at first, leading to more independence later. However, I will always be available for troubleshooting, data analysis, and discussion about the project.

Progression Monitoring and Management

I will expect you to take full ownership of your progression but come to me when you need help troubleshooting.

I will set out my expectations of your progression on a monthly basis. If these progression criteria are not met we will always discuss a plan of action together.

I am here for advice and guidance to help you reach the goals you set for yourself.

Communication

I am known to email my team/PhD students at all hours of the day and night, however I will not expect you to do the same.

I am happy to discuss any issues that are impacting your ability to fulfil your potential or my/our expectations.

PhD Students can expect scheduled meetings with me:

In a group meeting

At least once per week

In year 1 of PhD study

At least once per week

In year 2 of PhD study

At least once per fortnight

In year 3 of PhD study

At least once per fortnight

The meetings will be face to face and I am usually contactable for an instant response (if required) on every working day.

Working Pattern

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings), I expect students to manage their own time.

Notice Period for Feedback

I need at least 1 week鈥檚 notice to provide feedback on written work of up to 5000 words.

MIBTP Project Details

Primary supervisor for:

Deep learning of nascent transcription dynamics to predict mature mRNA production

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Deep learning of nascent transcription dynamics to predict mature mRNA production

Secondary Supervisor(s): Dr Massimo Cavallaro

University of Registration: University of Leicester

BBSRC Research Themes:

Understanding the Rules of Life (Systems Biology)

Project Outline

The production of mature messenger RNA (mRNA) from a human protein-coding gene is a highly regulated process involving transcription by RNA polymerase II (RNAPII) and co-transcriptional RNA processing, including splicing and cleavage/polyadenylation. Transcription proceeds through multiple stages, such as initiation, pausing, elongation and termination, all of which influence the final amount of mature mRNA produced.

Recent advances in artificial intelligence and the availability of large-scale genomic and transcriptomic datasets have enabled the development of deep-learning approaches to predict gene-expression outputs. However, most existing models rely on DNA sequence features or mature RNA measurements and therefore do not explicitly capture the dynamic transcriptional and RNA-processing events that determine gene-expression outcomes.

This PhD project will use nascent-transcription datasets, which provide direct information on RNAPII activity and RNA-processing dynamics on chromatin, to develop deep-learning models of gene expression. The aim is to determine whether incorporating transcriptional dynamics improves the prediction of mature mRNA abundance and reveals mechanisms that limit productive gene expression.

The student will combine next-generation sequencing analysis, deep learning and mathematical modelling to address questions such as how RNAPII dynamics influence mRNA production, how transcription and RNA processing interact, and whether predictive models generalise across biological contexts.

The project is suitable for students from biology, bioinformatics, computer science, mathematics, physics or related quantitative disciplines. Joint supervision by Dr Michael Tellier (gene regulation, genomics and transcriptomics) and Dr Cavallaro (artificial intelligence and mathematical modelling) will provide interdisciplinary training at the interface of molecular biology and AI.

Co-supervisor on projects with Dr Yolanda Markaki and Professor Thomas Schalch.

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Midlands Integrative Biosciences Training Partnership