Biology lessons from economics

The science of economics often takes a pummeling in the press, largely because this field is so intertwined with the messy business of policy. But from a pure research perspective, the way economists successfully handle extremely heterogeneous systems with some relatively simple mathematical models offers some lessons for biologists.

While reading this piece (Paul Krugman talking about his research career), I was struck by how biologists are faced with similar problems:

Robert Solow used to tell his students that there were two kinds of theorists: those who like to generalize, and those who like to look for illuminating special cases. Continue reading “Biology lessons from economics”

This sounds like my problem…

One physicist’s experience trying to model the EGF signaling pathway with a 48-parameter ODE model

Cerione explained that none of the parameters are known to better than a factor of between two and ten, and that it was so boring to measure them that he couldn’t pay anyone to do so. The model has 48 total parameters!…

We could fit to the data and make predictions, but with 48 free parameters could we trust our answers? To see if an answer was trustworthy, we did statistical mechanics in model space. (Doing a Monte Carlo in parameter space, it turns out, is called stochastic Bayesian analysis.) As I had suspected, the parameters varied over huge ranges. In fact, every parameter varied by over a factor of fifty, and many varied over factors of many thousands. Remember – all of these parameter sets still fit the existing experimental data.

Check out Cornell physicist James Sethna’s page on ‘sloppy models’, where he explains how to deal with biological systems that have dozens of parameters, most of which, thank God, don’t actually matter.

Why We Model

There is still considerable skepticism in many quarters regarding the utility of mathematical models in molecular biology. Those of us who do model are frequently required to justify our activities. The preferred justification is the ‘you can’t argue with success’ rationale, which can be used in cases where you already have a working model on hand that’s generating accurate predictions and yielding useful insights.

Unfortunately, in cases where you have yet to build that model, it is necessary to resort to other, more long-winded justifications for modeling. This Gilman and Arkin review nicely sums up the reasons I typically give for modeling:

The detailed models we examine demonstrate the principal strength of modeling: It is a means to formulate all available knowledge about a system in as precise a manner as possible Continue reading “Why We Model”

Complexity is Winning

Over at Ars Technica’s Nobel Intent, Robert Fortner takes a long, insightful look at synthetic and systems biology:

So far, neither systems nor synthetic biology has been able to find an unambiguous anchor point from which to spin a web of equations enclosing living matter. Instead, systems biology models grow and grow, but the end result is a molecular anatomy of overwhelming detail. Without models, making use of this mass of knowledge for medical applications becomes very difficult. Although synthetic biology begins at the bottom and looks up, it eventually comes face to face with the same complexity that systems biology sees peering from the top down. In either direction, the more we look, the more we find. In the pincer movement of systems and synthetic biology on complexity, complexity is winning.

Complexity certainly kicked my ass this summer.

Continue reading “Complexity is Winning”

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