The human genome encodes a master program for construction of the human body. The program includes subroutines for thousands of different cell types and cell states. Each subroutine prescribes a molecular logic for maintenance of a state, or transition to a new state, depending on local input and output signals. At a basic level, the set of states and the transition rules connecting them constitute a complete description of human biology. A simple analogy is Conway's game of life. Two states and four transition rules completely account for the dazzling collection of complex animated patterns that emerge. The challenge is to infer the states and transition rules by observing the patterns.

Our aspiration is to visualize the corresponding pattern of cellular programmatic states that exist within intact, three‑dimensional tissues, and infer the control architecture of the genome from these data. In biological terms, a cell's programmatic state is reflected in the subset of instructions in the human genome that it is actively reading, i.e. the profile of expressed RNA's and proteins. To measure this, we have developed scalable techniques for tagging the distinct gene products in tissues with barcodes. We are working on an optical imaging approach with dozens of distinct spectro‑temporal "color channels", so that can detect many barcodes in a single micrograph. Our aim is to achieve a rate of information throughput comparable to next‑gen sequencing. As a first model system, we have been studying the murine lung in collaboration with the labs of Tushar Desai and Mark Krasnow. The project presents many fascinating challenges, ranging from designing novel fluorophores, constructing microscopes, inventing in situ molecular biology, genetically engineering mice and computationally analyzing enormous imaging data sets.