8.2 million people die each year from cancer, an estimated 13% of all deaths worldwide, with an expected 70% increase of new cases over the next two decades. Elucidating if a genetic mutation is always required to cause a disease, or if epigenetic modifications are sufficient, can lead to the development of epigenomic profiles as markers of tumor prognosis. Cancer is a disease initiated and driven by genetic aberrations. Epigenetic modifications, from DNA methylation to histone alterations, play a significant role in tumor development. The aggregation of these effects influences the nuclear organization, which is implicated in different cellular processes, including gene expression.
How do genetic and epigenetic modifications influence the three-dimensional organization of the genome to keep a cell healthy or to degenerate into a disease?
Using Machine Learning algorithms and network theory, I analyze, integrate and interpret epigenetic and chromosome conformation capture (3C)-like data to predict the three-dimensional structure of chromatin and to understand how it influences gene expression. Learning how the genetic and epigenetic landscapes contribute to the cellular outcome could help understanding human biology and disease and could ultimately lead to the development of biomarkers for tumors prognosis.
Curriculum Vitae (pdf)