Network-based models for continuous cell state inference

/ABSTRACT The trillions of cells that make up the human body have vastly different identities and functions while, for the most part, sharing identical genomes and originating and differentiating from a single cell. Cells are typically grouped into discrete cell types reflecting, among other attributes, their tissue of origin and function. However, with the advent of technologies that operate at cellular and subcellular resolutions it has become increasingly clear that cells exist in a continuous range of dynamic states, particularly in the contexts of development and disease. Over the next five years, my research group will develop computational methods aimed at understanding the continuous nature of cell states. In particular, we will develop network-based models to integrate multiple modalities of large-scale genomics data with a focus on epigenetics and the noncoding genome. Some of the core questions we will examine are how the differences in measurements between modalities can be used to infer change over time, what role cell-cell communication and spatial organization play in determining cell state, and what are the genetic mechanisms that drive changes in cell state. I envision that, through this line of inquiry, we will be better able to identify potential druggable targets that play a role in determining cell state. Project Number: 1R35GM162400-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Pawel Przytycki | Institution: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS), BOSTON, MA | Award Amount: $433,750 | Activity Code: R35 | Study Section: Special Emphasis Panel[ZRG1 MCST-G (56)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11271599