Professor Darren Martin
Affiliations
- Full Member, Institute of Infectious Disease and Molecular Medicine
- Division of Computational Biology
- Department of Integrative Biomedical Sciences, University of Cape Town
Key Expertise
Bioinformatics, HIV, SARS-CoV-2, Viral Evolution
Main Research Focus
The Computational Biology (CBIO) Group is the centre of Bioinformatics activities at the University. It aims to perform world-class bioinformatics research and provide high-quality bioinformatics education, training, and services. The CBIO Division is part of the Department of Integrative Biomedical Sciences and is located within the IDM. Darren Martin’s main research interests lie in studying the role that genetic recombination plays during virus evolution.
As a mechanism that generates virus sequence diversity, recombination participates in the evolution of enhanced pathogenicity, drug resistance, and vaccine escape. Using computational recombinationanalysis tools developed at the IDM, his group are searching for recombination hot- and cold-spots within the genomes of all currently described virus families. With a focus on combining computational and wetlab experimental approaches Darren’s group tests hypothesis relating to genetic recombination, the evolution of genome-scale secondary structures and fitness trade-offs. For example, the group has shown that obligatory maintenance of the delicate network of co-evolved intra-genomic interactions that define the biology of virus species, severely limits the types of recombination events that are tolerable in nature. By demarcating the sub-genome modules that are and are not tolerably exchanged between viruses, recombination hot and cold spot maps should be applicable to the rational design of "recombination 1 resistant" vaccines and drugs against viruses such as HIV and Hepatitis C. Such maps should also be applicable to the identification of genome regions that interact with one another.
Most Significant Paper Authored in 2024
Sinkala M, Naran K, Ramamurthy D, Mungra N, Dzobo K, Martin D, et al. (2024)
This study reveals how cell surface receptor (CSR) transcript levels can guide breast cancer treatment by linking them to drug response and side effects. Using machine learning and bioinformatics, we identified CSRs that are overexpressed in tumors but limited in healthy tissues—ideal targets for therapy. The analysis showed that CSR expression patterns not only predict treatment response across breast cancer subtypes but also explain adverse effects based on CSR presence in healthy organs. This approach paves the way for more precise, less toxic cancer therapies by tailoring drug selection to tumor-specific CSR profiles.