Presented By: The Center for the Study of Complex Systems
Research Talk: Learning the spatial stochastic dynamics of gene expression from static images
Christopher Miles (UCI)
Abstract: The molecules inside cells operate with behavior dominated by randomness and disorder. Yet, from these ingredients, robust life emerges. Understanding this paradox demands new mathematics that bridges mechanistic models that capture molecular-scale stochasticity with statistical approaches for extracting patterns from large-scale, 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 insight with the power of modern data science, enabling discoveries from challenging biological datasets while raising mathematical questions about inference in stochastic systems. I will conclude with future directions in connecting spatial stochastic dynamics with biological data more broadly.
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