Ponente
Descripción
Usually we try to mathematically model biological phenomena observed directly from reality. It is a common practice to use statistical data to give our models plausibility and to check their predictions. But modeling reality directly is not the only path, and sometimes it's not even the right one.
In this talk I want to tone that idea down a bit: rather than model reality head-on, we should start collaborating in model experiments.
As occurs in physics, biological experiments are controlled simplifications of the phenomenon we care about. We can tweak some variables and measure others.
What I really want to highlight is the back-and-forth process: the mathematical model guides the experimental design, and the experimental results then help us refine and validate the mathematical model. That feedback loop is standard in physics, but it's not always obvious at the biology–math interface.
So I'll walk you through two cancer examples from my own work where exactly that happens — the math model tells the experiment where to go, and the experiment gives back to the math model.