Debusschere earns honors
for sorting out uncertainty
(page 2 of 4)
There are two main problems: First, the scale at which these reactions take place is so small that even those involving individual molecules generate great variability or "noise." "The fact they're so tiny makes them hard to resolve discriminately. You're counting molecules rather than concentration levels," Debusschere says.
Second, experiments generally capture but a sparse subset of molecules reacting in the system.
The noise in the reactions creates intrinsic uncertainty, which contributes to model uncertainty: Given the variability in such systems, how can researchers even be sure they have the right model for what they're studying?
With a standard deterministic model, "every time you run it with the same parameter you get the same answer," Debusschere says. "With intrinsic variability, every time you run the model you can get a different answer," even with identical parameters – and each answer is valid.
Finally, there's also parametric uncertainty – will a model make sense, given a certain set of parameters that influence the process? Different reaction networks are more or less sensitive to parameter changes, and it's often difficult to sort them out.
Because of intrinsic variability, it's difficult to compare predictions with experiments or to infer a model that could explain experiments, Debusschere says. Researchers try to understand what parameters are most responsible for an outcome or change the parameters to produce a desired result, but they often can't do either with much confidence.
"People have come a long way in figuring out these models, but what's missing is a comprehensive computer framework to investigate the uncertainties," he says.
That's Debusschere's mission. With Khachik Sargsyan and Habib Najm at Sandia, Olivier Le Maître of France's Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur and others, he's developing tools to analyze intrinsic noise and parametric uncertainty.
It starts with probability. In stochastic reaction networks, each time point presents "a certain probability of reactions taking place rather than a certain rate of reactions taking place," Debusschere says.
