The Lin lab develops theoretical models and uses computational tools to find the performance limits of complex biological systems. These systems often occupy a tiny functional fraction of a much larger space, mostly consisting of nonfunctional systems. This is true whether considering the conformational space of macromolecules or the connectivity space of neural networks. From this perspective, the conceptual challenge is to understand how the structure of the space dictates the types of search algorithms that can find the functional subspace in the relevant time-scale. Depending on the biological process, the relevant time-scale can differ by over twenty orders of magnitude, from molecular to evolutionary time.
Problems of interest include the multi-scale mechanisms of protein folding, dynamics and aggregation, as well as design principles for signal transmission in molecular and cellular networks. Questions driving the lab are 1) how system-wide properties emerge from the elementary interactions, and 2) why such emergence is evolutionarily scalable.