When (specialized parts of) two heads are better than one...

A recent review highlighted the small army of databases that has sprung up to help keep track of what we're learning about cells. Many of these databases focus on a particular feature of cell signaling (like protein-protein interactions or post-translational modifications), with a few databases combining information across multiple features to help build a more complete picture. A question that remains is how these collections of information can be used to help us achieve practical goals - identifying drug targets or predicting the physiological effects of mutations.

Computational modeling could have a role to play by turning descriptions of interactions into quantitative predictions. As databases tend to be managed by groups of people, one might expect that large-scale modeling projects could also benefit from a community-driven approach. However, modeling tends to be carried out by individuals or small groups. Are there ways to turn modeling into a community activity?

A first step is probably to put models into a format that is easy to navigate and that encourages interactions among people. One such format is a wiki, and there are actually a few examples of wikis being used to simultaneously annotate models and to consolidate information about a signaling pathway - a little like an interactive literature review that you can simulate on a computer. I think this is a cool concept, although it seems like these wikis tend to stop being updated soon after their accompanying paper is published. There have also been some efforts to establish databases for models, which would in principle make it easier for people to build on past work. But in practice, so far, it seems that these databases are not very active either.

  

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The issues involved in community-based modeling is also something I thought about when I read (the verbose yet interesting) "Reinventing Discovery" by Michael Nielsen, a book that advocates for "open science": a culture in which data and ideas are shared freely, with the goal of facilitating large-scale collaborations among people with diverse backgrounds. The underlying motivation is that progress can be accelerated if problems are broken down into modular, specialized tasks that can be tackled by experts in a particular area. I can see how such an approach would be beneficial in modeling and understanding cell signaling - a topic that can encompass everything from ligand-receptor interactions to transcriptional regulation to trafficking, each of which are complicated fields in their own right. So, how can experts in these fields be encouraged to pool their knowledge?

Nielsen's book has many examples of where collaborative strategies in science have succeeded and failed. As it turns out, creating wikis just for the sake of it is not always a good idea, because scientists often have little incentive to contribute. They would (understandably) prefer to be writing their own papers rather than spending time contributing to nebulous community goals. It seems like in most examples of where "collective intelligence" has succeeded, specific rewards have been in participants' minds. There's Foldit, the online game where players compete at predicting protein structures. And perhaps the most famous example is Kasparov vs. The World. (It's noteworthy that in both these examples, many participants are not trained professionals in the activity that they are participating in - structural biology and chess, respectively.)

I wonder what the field of cell signaling can learn from these examples. Does there need to be a better incentive for people to help with wikis/databases? One might imagine a database where an experimentalist can contribute a piece of information about a protein-protein interaction, which would automatically gain a citation any time it was used in a model. Or, can some part of the modeling process be turned into a game or other activity that many people would want to participate in? It seems like there are a lot of possibly risky, but also possibly rewarding, paths that could be tried.