Research in our lab uses computational modeling and analysis of large-scale data sets to understand complex biological networks of the brain. These networks are sophisticated systems composed of thousands to billions of elements, such as genes or individual brain cells, all working together to achieve a biological function. Although we understand a great deal about the working of individual network elements, putting this information together to explain how networks function is a challenge that requires combining empirical data with new computational, statistical and conceptual approaches.
Specific questions we are addressing include:
Epigenetic processes which modulate gene expression are critical for the development, plasticity, and degeneration of neural circuits throughout the lifespan. For example, disruption of normal DNA methylation patterns plays a major role in neurodevelopmental disorders and age-related decline in learning and memory. However, the normal development of methylation patterns and their effect on gene expression in brain cells is largely unknown. In the 15 years since completing the human genome project, many individual labs and large-scale consortia have focused on mapping the epigenomic landscape of coding and non-coding DNA elements. Advances in sequencing techniques have accelerated the growth of substantial genomic and epigenomic databases. Our lab seeks to exploit the full potential of these resources for elucidating developmental and regulatory processes, by creating computational analysis tools and theoretical models which are tailored to the scope and resolution of the data.
We recently analyzed the landscape of DNA methylation, a key means of epigenomic regulation, in developing human and mouse brains. By profiling the distribution of methylation marks throughout the genome during key stages of brain development, we found a set of unique features of the brain methylome that distinguish it from other tissues and cell lines.In the Press: