(639g) Invited Talk: Tackling Uncertainty in Metabolism and Metabolic Modeling

Uncertainty can be found in almost all aspects in biology and biological data. Some of this uncertainty can be a nuisance, for example, noise that contaminates important signals. But, in many cases, biological uncertainty has functional roles. In this talk, I will give vignettes of research in my laboratory that delves into the creation and use of mathematical models to tackle biological noise and uncertainty, focusing on cell metabolism. In the first part of the talk, I will present ensemble modeling strategies to deal with parametric uncertainty when building metabolic network models. In the second part, I will highlight our more recent work using machine learning and flux balance analysis to model biological uncertainty in metabolism using single cell data.