Pathogen Evolution

Project background

One of the biggest problems that mathematical modellers face when modelling biological systems is the lack of parameter values needed to parametrize their models. Many biological areas share this problem, and drug resistance is no exception.

While a wealth of data on drug resistance already exists and is generated daily, parameters needed for modelling, such as growth rates of individual strains, are often not published or scattered in obscure places, such as appendices and supplementary materials distributed across numerous papers. Several databases already exist that have some of the desired data. However, these databases have very different formats, content, and even terminology, depending on the research discipline and/or taxon in focus.

Objective:

This project aims to create a comprehensive database of pharmacodynamic data across disciplines and taxa. By consolidating and unifying existing data, extracting additional values from literature, and analyzing trends, this project will:

Identify data gaps and potential variability in missing values.
Provide essential parameters for model development.
Establish a valuable resource for researchers, fostering collaboration and future studies.

Approach:

We will use the latest advances in text mining and machine learning to collect and collate data from various sources.

This data will be analyzed, and we will identify important factors that play a role in drug resistance evolution. We will identify and predict missing values, identify important trends and patterns, and develop a platform where these data will be accessible to other researchers.

In addition, this pharmacodynamic data will be used to parametrize the modular model developed to investigate the role of important factors influencing drug resistance evolution in various taxa. In collaboration with experimental biologists, we will design simulations, propose hypotheses and generate concrete predictions that can be validated experimentally.

Furthermore, we will investigate temporal and spatial trends and patterns in the drug resistance evolution in bacterial, fungal, and helminth species.

Any potential differences between organisms in observed resistance trends will be analyzed and explained, using the modular model. We will look for underlying biological differences between the species in question, propose hypotheses to explain the observed differences, and design in silico experiments to test them. This will allow us to determine whether they can be at least partially attributed to reproductive strategy, ploidy, or lifestyle.

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