Thanks, but no thanks

I am posting from the q-bio Summer School, where we are enjoying many discussions about modeling. Several lecturers have advised the junior modelers attending the school, who are mostly graduate students and postdocs, to find an experimental collaborator. I appreciate the advice and the benefits of having an experimental collaborator, but I am usually quite irked by the reasons stated for seeking out opportunities to collaborate with an experimentalist. One reason I've heard many times is that modelers need an experimentalist to explain the biology to them and to help them read papers critically. It certainly could be useful to have a more experienced researcher aid in formulating a model, but that person might as well be a modeler familiar with the relevant biology. I don't subscribe to the idea that modelers need a collaborator to evaluate the soundness of a paper. To suggest so seems insulting to me. Modelers do need to consult experts from time to time to understand the nuances of an unfamiliar experimental technique, for example, but so do experimentalists. I am probably more annoyed by the popular sentiment that a collaborator is essential for getting predictions tested. If I were an experimentalist, I might be insulted by this idea. It's unrealistic to think that experimentalists are lacking for ideas about which experiment to do next. If your prediction is only appealing to your experimental collaborator, then maybe it's not such an interesting prediction? Modelers should be more willing to report their predictions and let the scientific community follow up however they may, partly because it's unlikely that your collaborator is going to be the most qualified experimentalist to test each and every prediction you will ever make. I think the real reason to collaborate with an experimentalist is shared goals and interests and complementary expertise. Finding such a colleague is wonderful, but it shouldn't be forced, and the absence of a collaborator shouldn't be an impediment to progress. If you have a good prediction, you should report it, and if you want to model a system, you should pursue that. Eventually, you will know the system as well as the experimentalists studying it, if not better. After all, it's your role as a modeler to integrate data and insights, to elucidate the logical consequences of accepted understanding and plausible assumptions, and to suggest compelling experiments. Finally, I want to speak to the notion that modelers should do their own experiments. I think that's a good idea if you want to be an experimentalist. If you want to be a modeler, be a modeler.