Dr Tyler Cassidy

Profile

I received my PhD in Applied Mathematics from McGill University in 2019. During my graduate training, I was an intern in the Internal Medicine Research Unit of Pfizer and a Junior Fellow at the Institut Mittag-Leffler in 2018. After my PhD,  I was a postdoctoral research associate in the Theoretical Biology and Biophysics Group at the Los Alamos National Laboratory where I worked with Alan Perelson on modelling viral dynamics in HIV and hepatitis B. I was also awarded a NSERC postdoctoral fellowship in 2020 that I deferred to work as a Senior Scientist in the Oncology Research Department of Pfizer Inc. I joined the School of Mathematics at Leeds in 2022.

I enjoy running, cycling, and have been recently learning to rock climb.  Fulfilling the Canadian stereotype, I am also a (ice) hockey fan. I use the pronouns he/him/his.

Responsibilities

• Mathematical biology seminar organiser

Research interests

Broadly speaking, I am an applied mathematician working at the intersection of mathematics and medicine. My research is centered on using mathematical models to understand the role of heterogeneity in disease progression and treatment resistance. I'm interested in the evolution of resistance to anti-cancer therapies, particularly the emergence of adaptive resistance through phenotypic adaptation to treatment. I also develop within-host models of viral dynamics in HIV and other viral infections. These viral-dynamics models can be used to characterise the efficacy of new anti-viral therapies, explore potential synergies of combination therapies, and quantify the development of resistance to existing therapies. Finally, oncolytic viruses, which are genetically modified viruses designed to selectively target tumour cells, are a further area of interest. I am particularly interested in quantifying the inter-play between oncolytic viruses, the induced anti-tumour immune response and an anti-viral immune response that may limit treatment efficacy. 

Mathematically, I am interested in structured population models and delay differential equations. However, it can be difficult to apply these types to model biological problems. Accordingly, I enjoy developing new numerical and analytical techniques to facilitate the use of structured population models in mathematical biology. I find the language of dynamical systems to be extremely useful in understanding disease progression. I have developed techniques to bridge individual level mathematical models with population level heterogeneity through virtual clinical trials. 

Research groups and institutes

• Applied Mathematics

Projects

<li><a href="//phd.leeds.ac.uk/project/1711-mechanistic-modelling-of-treatment-resistance-in-cancer">Mechanistic modelling of treatment resistance in cancer</a></li>