Imperial College London

DrAdaYan

Faculty of MedicineDepartment of Infectious Disease

Imperial College Research Fellow
 
 
 
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Contact

 

a.yan Website

 
 
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Location

 

421Praed StreetSt Mary's Campus

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Summary

 

Summary

I am an Imperial College Research Fellow in the Section of Virology, Department of Infectious Disease. My background is in developing mathematical models to understand how biological mechanisms drive the time course of infection within the host, and interactions between infection and host immunity. During my fellowship, I will develop skills in experimental virology and bioinformatics. This will enable a cycle of data collection, followed by hypothesis generation by modelling these data, and testing of these new hypotheses experimentally. In the future, I plan to lead projects spanning the theoretical-experimental spectrum to improve health outcomes.

The 1918 influenza pandemic caused 50 million deaths, and such pandemics remain a threat to global health. These pandemics occur when viruses in animals, most commonly birds, adapt to infect humans efficiently. Being able to assess which bird viruses pose the greatest risk to humans is essential for surveillance programs and effective interventions.

One risk factor is the receptor binding preference of the virus. Switching receptor binding preference is thought to give influenza viruses a fitness advantage in humans because it enables the virus to infect more abundant cell types. However, cell types preferred by human viruses may also have different susceptibilities to infection, produce different amounts of virus, become resistant to infection at different rates, and/or have different abilities to warn other cells of infection. The impact of these additional factors, rather than cell type abundance alone, is not well understood.

During my fellowship, I will infect primary human airway epithelial cells and measure the amount of virus and immune response produced by each cell using single-cell RNAseq. I will then apply my mathematical modelling expertise to understand how measured differences between cells affect virus growth, and predict which viruses are likely to benefit from preferring human-like receptors. This work will help us assess which viruses are the most dangerous and design effective interventions. The data analysis methods developed will generalise to other diseases, and the generated data will inform future studies on how viruses adapt to humans.

In 2020, I was a Research Associate at the Section of Immunology of Infection. My work focused on understanding the relationship between host genetics, CD8 T cell dynamics and immune control of chronic virus infections including human immunodeficiency virus (HIV-1), hepatitis C virus (HCV) and human T cell leukemia virus (HTLV-1). From 2017 to 2019, I was a Research Associate at the MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health. I determined the replicative fitness of influenza strains through fitting viral dynamics models to laboratory data.

My PhD studies at the School of Mathematics and Statistics at The University of Melbourne analysed the effects of different components of the immune response on within-host influenza infection. I was awarded a Victoria Fellowship in 2015.

Publications

Journals

Challenger J, Foo C, Wu Y, et al., 2022, Modelling upper respiratory viral load dynamics of SARS-CoV-2, Bmc Medicine, Vol:20, ISSN:1741-7015

Peacock TP, Brown JC, Zhou J, et al., 2022, The altered entry pathway and antigenic distance of the SARS-CoV-2 Omicron variant map to separate domains of spike protein

Deol AK, Scarponi D, Beckwith P, et al., 2021, Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens, Plos One, Vol:16, ISSN:1932-6203

Goldhill DH, Yan A, Frise R, et al., 2021, Favipiravir-resistant influenza A virus shows potential for transmission, Plos Pathogens, Vol:17, ISSN:1553-7366, Pages:1-17

Menkir TF, Chin T, Hay JA, et al., 2021, Estimating internationally imported cases during the early COVID-19 pandemic, Nature Communications, Vol:12, ISSN:2041-1723

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