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Introducing the SCRU Approach of Science Through Computing

Over the last few decades computing and data science have been established as key drivers of innovation in commerce and industry.  However, the scientific method that was founded on circular relationships starting with observation followed by experiment and formalised in mathematical theory has been slow to embrace the tools of computational science. The Scientific Computing Research Unit at UCT is an interdisciplinary unit active across three faculties (Science, Engineering and Medicine) inspired by a self-imposed mandate of leading innovative solutions in science through the rigour of computational models and the observational insights gained from data analytics.

We have built unique and practical computational tools capable of discovering the reactions  mechanism of glycoenzymes key to Respiratory Infection and Cancer. These tools are used to develop Transition State Analogue inhibitor models from which drugs are designed. These designs are synthesised and tested in our Glycobiomedical Laboratory  The performance of these drug leads are then further refined using our informatics tools that seamlessly links computational chemistry with computational biology on our in-house developed BRIDGE Galaxy platform. This approach where computational experiments and models drive therapeutics for Cancer and Respiratory infection has shown impressive success and so is a significant advance in the rational approach to understanding mechanisms and producing solutions for microbial and neoplastic diseases.

In browsing through this site you will find that our mantra Science through computing has been a guide to our research that has taken the computer board to the laboratory bench to the patient bedside. Through this approach we have been awarded global patents for cancer biomarkers as well as local patents for a series of ricin inhibitors developed from their computational-driven rational design of enzyme transition state analogues. Our machine learning and bioinformatic group developed a unsupervised learning method that led to the discovery of breast cancer biomarkers from gene expressions. In 2021 we lead a Phase 1 clinical trial on a breast cancer diagnostic discovered in our laboratories.

Through this virtual introduction we hope that we have sparked your interest in our approach to using fundamental computational science to discover practical solutions to respiratory infection and cancer. We welcome you to visit or join our research team of computational scientists, chemists, chemical and molecular biologists, mathematicians, physicists, electrical and chemical engineers working to develop scientific solutions for health care through computing.


Kevin J. Naidoo

Director Scientific Computing Research Unit