Collaboration between experimental and theoretical work is crucial to unraveling the complexity of living systems. To be productive, such collaborations require some often-scarce resources: most importantly, the time, manpower, and perseverance to iterate through multiple model formulations and experimental measurements. We argue that much can be learned from models which are highly simplified and initially ‘wrong’ or inconsistent with observations, and that the goal of theory in biology should not be primarily to provide a fit to existent experimental data. Instead, theoretical models should shed light on the key features of the system, providing insight on the missing pieces in our conceptual pictures and suggesting new measurements which can help fill in the gaps. Here, we describe a case-study from our own collaborative experience, focused on understanding the distribution of mitochondria in dendritic arbors. This story proceeded in an iterative manner from initial observations of dendritic structure and mitochondrial dynamics, to the construction of simple models, and back to original measurements and model refinement. Along the way, we came to appreciate some general principles for productive theory — experiment collaboration, which we proceed to highlight here.
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