Deciphering Stochastic Gene Expression Dynamics from Static Imaging through Point Process Inference

The molecules of life operate with behavior dominated by randomness and disorder. Yet, from these ingredients, robust cellular function emerges. Understanding this paradox requires both mechanistic models that capture molecular-scale stochasticity and statistical approaches to extract meaningful patterns from noisy, heterogeneous data. This talk presents a case study in bridging these gaps to infer gene expression dynamics from static spatial patterns of mRNA molecules in cells. The approach links spatial point processes for individual molecule locations with tractable solutions to (stochastic) partial differential equations. This framework combines the strengths of mechanistic modeling and machine learning, enabling new discoveries from challenging large-scale biological datasets. I will discuss recent advances and future directions, including the incorporation of additional biological complexities, the development of more sophisticated computational methods, and expanding the framework to address a range of other biological questions.