Join us for this special seminar to showcase PhD student research at the interface of maths and biology as part of the North West Seminar Series in Mathematical Biology and Data Sciences. Our speakers are Charlotte Taylor Barca and Muhammad Ahtazaz Ahsan (PhD candidates in Department of Mathematics and Division of Informatics, Imaging & Data Sciences respectively). More details about the joint series can be found here https://northwestseminars.great-site.net/.
The talk will be hosted in person in room 2.60 of the Simon Building. For those who cannot attend in person the talk will also be streamed via zoom, please contact carl.whitfield@manchester.ac.uk or igor.chernyavsky@manchester.ac.uk for the zoom link, or sign up to the mailing list.
Talk 1: 2:00 - 2:20
Title: Modelling cell state dynamics in melanoma
Speaker: Charlotte Taylor Barca (Department of Mathematics. Supervisors: Oliver Jensen and Gareth Wyn Jones)
Abstract: Melanoma cells can transition between cell states, contributing to therapy resistance and immune evasion. These state changes involve dynamic and reversible shifts in gene expression, making it essential to understand the underlying regulatory mechanisms for developing effective therapies. We present a mathematical model of a minimal gene regulatory network comprising key transcription factors associated with melanoma cell states. Using deterministic temporal and spatio-temporal differential equation models, we analyse gene expression dynamics and classify stable states in a biologically meaningful way. We exploit an approximation, based on cooperative binding of transcription factors, in which the models are piecewise smooth. At the population level, we use a naïve model of intercellular communication to explore how cells within a tumour can exhibit coordinated behaviour through travelling waves of gene expression. Additionally, we propose a method for deriving a condition that determines the final state of a population of communicating cells. This model provides a framework for better understanding some of the mechanisms driving gene expression dynamics and to inform and validate experimental hypotheses.
Talk 2:25 - 2:45
Title: Development of deep learning methods to predict pathway expression from histology images
Speaker: Muhammad Ahtazaz Ahsan (Division of Informatics, Imaging & Data Sciences. Supervisors: Syed Murtuza Baker, Mudassar Iqbal, Karen Piper Hanley , Martin Fergie)
Abstract: My research uses deep learning to predict the pathway expression from H&E images. A biological pathway is denoted by a group of functionally related genes and provides more compact and meaningful information for analysing certain diseases, e.g., identifying hypoxic regions in tumor tissues. Lately, we have been looking at refining the pathway definitions and integrating them with multi-modal data to train a rich and diverse model for capturing the pathway activities. Currently, we are benchmarking the gene vs. pathway prediction using a bimodal contrastive learning framework to showcase the effectiveness of pathway prediction.
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