A single roundworm has roughly the same number of genes as a human, yet we have trillions of cells and it has only a few thousand. The difference lies not in the genes themselves, but in how they are switched on and off. Nearly 90% of the genetic variants linked to common diseases—heart disease, cancer, dementia, multiple sclerosis—fall not in the protein-coding parts of the genome, but in the vast non-coding regions that act as control switches. These switches work differently in every cell type, making the system extraordinarily complex. This project uses experimental biology, computation, and machine learning to decode the basic principles of gene regulation—how and when these switches operate. If successful, this work will translate anonymous disease-linked DNA variants into specific, actionable information: which genes are affected, in which cell types, and what cellular functions go wrong. That knowledge could guide the development of new therapies by revealing precise molecular targets. The research is fundamental science—it asks how the genome’s control panel works—but it directly addresses a bottleneck that has stalled progress in understanding the genetics of common disease.
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A simple organism like C.Elgans has similar numbers of genes as a human even though it is made of only a few thousand cells compared to the many trillions in a human. This massive increase in complexity is therefore not linked to the genes themselves, but rather to the complexity of the combinations in which they are used. Therefore, complex mammalian life is founded on exquisite control of gene usage (expression); when and where they are switched on and off and their level of expression. The “switches” that control gene expression are located in the “non-coding” region of the genome rather than the regions that form the blueprint for production of the proteins of which we are made. However, this represents ~98% of the total genome and the DNA code that activates these switches is different in each cell type so understanding the systems that control gene expression is extremely complex. However, understanding these mechanisms is extremely important for human heath as nearly 90% of the genetics linked to all common human diseases (Heart disease, cancer, dementia, multiple sclerosis etc) lie within this region and change the activity of these switches. We use a combination of experimental, computational and machine learning approaches to understand the basic principles of gene regulation to decode common human disease genetics. We use these approaches to understand the genes and cellular functions affected by these genetics to ultimately guide therapeutic development.
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