Speaker
Description
Acute myeloid leukemia (AML) progression reflects a stochastic and adaptive process driven by cellular plasticity and the continual reshaping of epigenetic regulatory programs. To capture these dynamics, we develop a stochastic modeling framework in which a Langevin equation describes noise‑driven fluctuations underlying shifts in differentiation potential, chromatin state, and lineage identity in mouse models of AML. These evolving processes are embedded within a state‑space that maps observed molecular and phenotypic changes onto latent variables summarizing disease evolution and therapeutic response. The associated Fokker–Planck equation characterizes how probability densities propagate across epigenetically regulated cellular states, enabling quantitative prediction of phenotype switching, treatment adaptation, and the emergence of resistant cell populations. By linking measurements of epigenetic reprogramming and lineage plasticity with a stochastic dynamical system, this framework provides a quantitative platform for forecasting AML behavior under treatment. In this talk, I will highlight how mouse models, public datasets, and mathematical modeling provide insight into plasticity in AML evolution and outline our efforts to translate the predictive models into clinical trials at City of Hope.
