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| Funder | The Academy of Medical Sciences |
|---|---|
| Recipient Organization | University College London |
| Country | United Kingdom |
| Start Date | Jan 01, 2021 |
| End Date | Dec 31, 2024 |
| Duration | 1,460 days |
| Data Source | Europe PMC |
| Grant ID | APR3\1018 |
I plan to move my lab from the Fred Hutchinson Cancer Center (FHCC, USA) to University College London (UCL) in January 2021. An AMS professorship will be critical to start my new lab, as my US grants cannot be transferred to the UK. It will also give us immediate opportunity to collaborate with the medical community, as described below.
First, an AMS professorship will allow us to develop medical applications for our work on community selection.
We have simulated artificial selection of different types of communities where two species work together to generate a final product, and identified general themes on best practices.
To experimentally test these predictions, we will use artificial selection to improve two-species communities where one species produces an intermediate and the other converts the intermediate to anti-cancer drugs (e.g. taxanes). In parallel, we will simulate selection of communities of several or many species, and identify general strategies.
This will help achieve our long-term goal of improving complex communities with, for example, probiotic activities. Second, an AMS professorship will allow us to develop methods to extract information from clinical data.
We are analysing data on vaginal microbiomes isolated from patients suffering bacterial vaginosis, a collaboration with FHCC clinicians that we hope to expand with UK clinical microbiologists.
To infer microbial interactions from observational time series data, we have scrutinized the “causal inference” literature.
We found that causal inference approaches are often misused, partly because they originated from diverse fields including philosophy, statistics, econometrics, and topology. While writing a synthesis article, we uncovered new problems that cast doubts on well-cited papers on causal inference. We are developing statistical methods to detect and resolve these problems for future data analyses.
The AMS professorship will help bring this important work to fruition, contributing the urgently-needed rigor as the microbiome field moves from correlation to causality. Finally, an AMS professorship will allow us to venture into new areas of mathematics to solve important problems.
The Covid-19 pandemic emphasizes the importance of mathematical modelling in devising strategies to curb infections and reopen society.
I decided to move to UCL because evolutionary biologists at UCL appreciate mathematics, and because UCL has excellent Mathematics and Physics Departments.
Taking advantage of these new opportunities, we will develop fundamental theories for evolutionary processes that impact diseases.
For example, in collaboration with mathematicians, we will derive equations that describe how growth rates of mono-species cultures diverge over time during evolution.
This is important for understanding person-to-person variations in microbial infections that are independent of host differences. Unlike simulations, which are tied to parameter choices, equations reveal universal phenomena.
Importantly, similar to how a seed grant initiated our collaboration with FHCC clinicians, an AMS professorship will allow us to flexibly direct our work to medical problems in collaboration with clinicians. An AMS professorship will be critical for cultivating these new projects to a stage suitable for traditional funding.
We will continue to creatively use experiments and mathematics to understand microbial population biology, and to solve important biomedical problems.
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