Mathematical and statistical modelling to drive translational impact in cancer biology

Speaker:  Aaron Kilgallon, Dharmesh Bhuva
Affiliation: Wesley Research Institute, Frazer Institute

Abstract

Cancer results from the dysregulation of an otherwise finely controlled complex biological system. Such dysregulation empowers cancers with the programs required to evade the immune system, resist therapies, and acquire nutrients through recruitment of blood vessels. Molecular biology offers the tools to disentangle this complexity through high throughput cellular measurements of thousands of molecules; however, data of such scale presents an extraordinary analytical and modelling challenge. We will introduce the complexity of biological systems profiled in spatial biology, the data measured, and some of the mathematical and statistical approaches from the fields of dynamical systems modelling, linear modelling, deep learning, point process modelling, and topological analysis that our team employs to generate a translational pathway that improves the lives of patients.

Bio

Dr Aaron Kilgallon is a data scientist at the Queensland Spatial Biology Centre (QSBC) at the Wesley Research Institute. His doctoral research at the University of Oregon was on the application of machine learning techniques to search for undiscovered and novel signatures of dark matter produced at the Large Hadron Collider.  Currently, he is working on modelling clinical patient response to treatments by incorporating statistical and machine learning methods in analyses of spatial proteomics and transcriptomics data obtained from multiplexed imaging of patient biopsies. Aaron is passionate about research in data-driven techniques to revolutionise medicine and improve treatment outcomes for patients, particularly those with degenerative diseases, and believes that next-generation medicine will rely on these methods to address the complexity of modern healthcare.

Dr Dharmesh Bhuva is an NHMRC Emerging Leadership Fellow (EL1) at the Frazer Institute, The University of Queensland. He is passionate about understanding how complex systems of gene regulation and signalling drive the organisation of healthy and diseased tissues. He completed his PhD in 2020 at the University of Melbourne and the Walter and Eliza Hall Institute (WEHI), where he developed novel systems biology approaches to study molecular function and gene regulation in cancer. He then undertook postdoctoral research in the world-renowned WEHI Bioinformatics Division, where he developed new computational approaches for studying cancer tissues using spatial molecular data. In 2023, Dr Bhuva joined the Computational Systems Oncology Division at the South Australian Immunogenomics Cancer Institute (SAiGENCI), where he continued to develop cutting-edge computational methods to study tissue architecture. Computational tools developed by Dr Bhuva have been downloaded over 240,000 times globally. His contributions to computational biology have been recognised by the Australian Bioinformatics and Computational Biology Society through the Outstanding Early Career Researcher Award. Most recently, Dr Bhuva has been awarded an MRFF grant and an NHMRC Investigator Grant (EL1) to identify spatial biomarkers in cancer, with a particular focus on biomarkers predictive of response to immunotherapies in non-small cell lung cancer (NSCLC).