As I contemplate presenting my research plans in job talks, I’m worried about clearly conveying what we get out of quantitative models. The vast majority of biologists don’t build or use quantitative models, which I recognize is a reasonable consequence of the history of the field, but I find it shocking nonetheless. What this means is that many of these researchers don’t share my fundamental outlook, and, as good skeptical scientists, they won’t take it for granted that models are useful. In fact they’ve probably seen plenty of examples of bad models.
So here is how I justify my mathematical modeling work: people often justify models by saying we can use them to make predictions, or we can use them to test the completeness of our ideas, to determine whether we are missing any major system players. Models can be used to formally summarize large amounts of experimental results.
To me, these are all important but derivative reasons; they derive from what I see as the most fundamental and compelling reason to model, which is: to understand how the behavior of a system arises as a consequence of the interactions of its parts. This understanding is generally only achievable with quantitative models, because we are not dealing with simple Rube-Goldbergian linear chains of causality. In most real-world systems, we deal with large numbers of complex and often non-linear interactions, and thus we cannot understand them without quantitative models.