Speaker
Description
Acute myeloid leukemia (AML) evolves through stochastic and interconnected changes in cell differentiation, clonal composition, and immune response, revealed through longitudinal multi-genomic profiling. We present a stochastic modeling framework in which a Langevin equation describes noise driven fluctuations in genomic states in a mouse model of AML. These dynamics are embedded within a state space model that integrates time resolved multimodal data, including bulk and single cell RNA and microRNA expression, epigenomic features, and immune profiling features, into latent variables that summarize disease progression and therapeutic response. The Fokker–Planck equation corresponding to the Langevin equation of motion in the state-space gives the evolution of probability densities over these high dimensional genomic states, enabling predictive modeling of disease trajectories and treatment outcomes.
By coupling multi omic longitudinal sequencing with a stochastic dynamical systems model, we link complex genomic dynamics to individualized probabilistic predictions in AML. In this talk, I will show how mouse models, publicly available multimodal datasets, and mechanistic mathematical modeling converge to generate new insights into AML evolution and response to therapy, and outline our efforts to translate these methods into clinical trials at City of Hope.
