Normal and abnormal development: Simplifying and understanding its Complexity

Understanding development arising from stem cells using molecular profiles like gene expression microarray, genome wide methylation marks, RNASeq, and histone mark dynamics is currently our state of the art. All of these approaches measure a single dimension of molecular event. How can this be translated to how the cell is functioning at the developmental time point, and how can this be compared between experiments that are using different platforms, cell types, and whatever else?

We need to figure out what the overall functional state of the cell is at the developmental snapshot we are taking. If we can do that then its possible to compare functional states – which are made up of the sum of the assay activities we are performing already, and draw the conclusions accordingly.

Gene Discovery

We apply systematic organizing approaches to data from the genome, transcriptomics, protein-protein interaction and gene regulation and so reveal critical disease processes occurring in cancer and infectious disease. In cancer research we build and implement systems that allow discovery of key genes involved in Tumour Initiating (Cancer Stem) Cell gene regulation and drug resistance. In pathogen research we build systems that reveal the variation of the pathogen genome in response to drug or vaccine selection.

My group works at the School of Public Health and at the SA National Bioinformatics Insitute, University of Western Cape near Cape Town, South Africa.