AIBN Virtual Seminar Series: Developing image-based predictive models for the characterisation of cellular senescence in mesenchymal stem cells
Event Details
- Date:
- Thursday, 01 October 2020 - Thursday, 01 October 2020
- Time:
- 12:00 pm - 12:45 pm
- Room:
- Online via Zoom
- URL:
- https://uqz.zoom.us/webinar/register/WN_nodGXIjNQgeR26NLlmv4fA
- Event category(s):
Event Contact
- Name:
- AIBN Events
- Phone:
- 64215
- Email:
- aibn.events@uq.edu.au
- Org. Unit:
- Australian Institute for Bioengineering and Nanotechnology
Event Description
- Full Description:
- Abstract: Senescence is a dynamic cellular stress response that is hypothesised to act as the causal nexus of ageing through its extensive pro-inflammatory secretory phenotype. Cells entering senescence undergo extensive morphological, transcriptional, and metabolic changes, which vary significantly by cell-type, tissue, the inducing stressor, and time since the induction of senescence. This extreme heterogeneity has made identifying and characterising the senescence response a considerable challenge, presenting a major obstacle in the research and development of therapeutics for cellular senescence.
Through utilising computer vision and machine learning techniques, we are developing predictive models of cellular senescence in mesenchymal stem cells from extensive high-content imaging data taken across multiple time points and lineage differentiations. By applying interpretability methods to extract actionable information from the final models, we hope to establish a robust and specific phenotype of cellular senescence that can be utilised for the classification of senescent cells, as well as identifying novel subtypes and transitional states.
Presenter: Ebony Watson, AIBN PhD Student (Mar Group)
Ebony Watson received her Bachelor of Science (Honours) degree in Genetics from the University of Queensland in 2018. In January 2019, she commenced her PhD with Associate Professor Jessica Mar at the Institute for Bioengineering and Nanotechnology, University of Queensland. Ebony’s research is focused on investigating the mechanisms behind ageing and age-associated diseases through the application of machine learning and image analysis techniques.
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