Joint Maud Menten /Math Bio Seminar: Hao Wang

Stoichiometric principles provide a rigorous, law-based backbone for building mechanistic models that are robust and empirically testable through conservation and physiological constraints. In this talk, I will introduce stoichiometric models that resolve key biological paradoxes by explicitly tracking elemental and quota-driven limitations, then demonstrate how data-driven components can (i) learn missing processes, (ii) improve short-term forecasting, and (iii) enable real-time monitoring. Methane biogenesis in oil sands tailings serves as a central application: we couple stoichiometric biodegradation dynamics with ML-enabled monitoring to better predict emissions trajectories. This research contributes to the broader goal of carbon neutrality.