I have previously worked in medical research for over five years. This experience spurred my desire for bioinformatics. At that point in time, my desire was to merge both wet and dry bench skills to drive my research interests. Upon completing a MSc in molecular biology at Makerere University in Uganda, I went on to pursue a Master of Statistics (Bioinformatics) at Universiteit Hasselt in Belgium. This introduced me to the world of computational modelling which is now a major part of my skill set. I have also come in contact with systems medicine. All these put together complete the puzzle.
Centre for Functional Genomics and Bio-Chips, Ljubljana
Supervisors: Prof.dr. Damjana Rozman, Assoc.Prof.dr. Miha Moškon
Secondment 1: Apply state-of-the art methods to develop models for MAFLD
Secondment 2: From models to bed side: translate molecular predictions into practice
ESR 5 Project
Personalized molecular signatures for modulating progression of metabolism associated liver disease (MAFLD) to hepatocellular carcinoma
Metabolism Associated Fatty Liver Disease (MAFLD) is a major health burden world over with an estimated prevalence of 25% (Younossi et al., 2016). Patients suffering from MAFLD have a short survival of approximately 5% in five years when MAFLD advances to hepatocellular carcinoma (Tomaš et al., 2018). This situation is worsened by the lack of fast non-invasive diagnostics and prognostic tools to monitor disease progression and a shortage of effective treatments options. This project seeks to unravel the molecular mechanisms of MAFLD, and the factors that facilitate its progression to hepatocellular carcinoma all in a bid to identify molecular targets that can be exploited to control the disease. We will apply computational approaches such as genome scale metabolic models for integration of omics data, among other modelling techniques. We know that MAFLD is characterised by accumulation of fat in and/or around the liver so we will pay special attention to cholesterol metabolism. The proposed molecular targets will be integrated and validated to yield tools that could ease the burden of MAFLD.
- Parasitology, virology and cancer research
- Data analytics, mathematical modelling and artificial intelligence
- Personalized medicine
- Volley ball