Data science is revolutionising the research of biological systems – be it the study of common characteristics such as body weight, height, the colour of eye or skin, intelligence, mathematical ability, and ability to be an athlete. Or the study of variation in susceptibility to diseases such as cancers, metabolic, cardiovascular and neuronal diseases. Many genetic and environmental factors regulate all of these characteristics (traits).
Our laboratory is interested in deciphering the relationships between these traits and genetic and environmental factors. To understand these relationships we use a variety of experimental and computational methods. Using yeast natural and synthetic populations, we uncover complex relationships between genetic and phenotypic variation and the role of evolution in changing the dynamics of these relationships. For studying clinical traits, we apply computational methods on various genetic, gene expression, proteomics, phenomics and metabolomics datasets to discover relationships between genetic variation and disease susceptibility. We collaborate with clinical institutes and hospitals to analyse clinical datasets and build machine learning models to predict disease outcomes.
How population level variants affect phenotypes at transcriptional, post-transcriptional, and phenotypic levels by using both yeast model studies and large datasets. Applying evolutionary principles to understand the interplay between genetic and phenotypic variation.