Mathematical Modelling of Alternative Splicing and Transcriptomic Complexity

Mathematical Modelling of Alternative Splicing and Transcriptomic Complexity

Advisor/s

A/Prof Ulf Schmitz, Dr Siyuan Wu

College or Research Centre

College of Public Health, Medical & Veterinary Science; College of Science & Engineering

Summary of Project

We are seeking motivated and innovative PhD candidates to join our research team in investigating the regulatory mechanisms of alternative splicing through mathematical modelling. Alternative splicing is a vital regulatory mechanism responsible for the vast diversity of eukaryotic transcriptomes and proteomes. This process enables a single gene to produce multiple mRNA isoforms, leading to distinct protein variants with diverse functions. Despite its importance in cellular processes and disease development, the regulatory mechanisms governing alternative splicing remain poorly understood. This project aims to develop a comprehensive modelling framework that integrates various factors influencing alternative splicing, such as cis-regulatory elements, trans-acting factors, chromatin structure, and cellular context, to simulate alternative splicing events and unravel the intricate regulatory mechanisms behind transcriptomic complexity.

The successful candidate should have a background in mathematics, computer science, engineering, bioinformatics, RNA biology, or a related field. The project will involve developing and implementing mathematical models that describe the splicing process and analysing the accuracy and predictive power of these models using experimental data from various sequencing technologies, such as single-cell RNA-seq and long-read sequencing. The candidate will also be responsible for analysing and interpreting the model results and writing up their findings for publication in high-impact academic journals.

Key Words

Numerical methods; Cell-Fate Determination; Computational methods; Mathematical modelling; Computational Biology; Systems Biology; Mathematical Biology; Bioinformatics; Alternative splicing; Transcriptomic Complexity

Would suit an applicant who

has a background in mathematics, computer science, engineering, bioinformatics, computational biology, or a related field. This project is suitable for doctoral or master students. Some computational skills (MATLAB, Python, R programming) would be very helpful.

Updated: 17 Mar 2023