Getting back on my feet after the holiday rush and the expansion of our family: A meeting was held in mid-December to examine "The Impact of Modeling on Biomedical Research." This was held under the umbrella of the IMAG and the MSM Consortium.

The acronyms stand for Interagency Modeling and Analysis Group and the MultiScale Modeling Consortium, which are being operated by various federal science agencies, with the goal of helping the biomedical sciences get serious about modeling.

The main idea being IMAG is to get funding agency program officers together to think about how to promote the application of "modeling and analysis methods to biomedical systems."

The slides of some of the presentations are posted on the IMAG Wiki (follow my first link). The meeting covered modeling at various scales - populations, organ systems, cellular pathways, and molecules.

As I perused the summary slides, three overriding concerns of IMAG became clear: 1)acceptance of modeling in the biomedical community, 2) education - "non-modelers" need easy-to-use software tools, and current students need more computational training, and 3) successes of modeling need to be publicized (in order to promote acceptance of modeling).

I'm on board with all three goals - if we ever want to understand biology like engineers, and not like amateur mechanics, we need to go quantitative and adopt the kinds of tools already in use in other physical sciences. People who aren't trained with that perspective will be reluctant to make much time for modeling, and acceptance of modeling will be limited until experimentalists see successes that make a difference at the lab bench. These successes do exist, but they're easy to miss - either in the large mass of computational papers that show little direct relevance to what goes on at the bench, or in the bulk of non-quantitative molecular biology papers that are aimed at working out molecular details - which proteins interact with each other and when.

So the typical experimentalist thinks that modeling a) leads to a bunch of untestable or biologically uninteresting hypotheses (if hypotheses are generated at all, and b) there is no point to modeling until all the molecular players in the system of interest have been comprehensively identified.

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