Ryan Adams, Harvard University
Recent work on molecular programming has explored new possibilities for computational abstractions with biomolecules, including logic gates, neural networks, and linear systems. In the future such abstractions might enable nanoscale devices that can sense and control the world at a molecular scale. Just as in macroscale robotics, it is critical that such devices can learn about their environment and reason under uncertainty. At this small scale, systems are often modeled as chemical reaction networks. I will describe a procedure by which arbitrary probabilistic graphical models, represented as factor graphs over discrete random variables, can be compiled into chemical reaction networks that implement inference. I will show how marginalization based on sum-product message passing can be implemented in terms of reactions between chemical species whose concentrations represent probabilities. Tthe steady state concentrations of these species correspond to the marginal distributions of the random variables in the graph. As with standard sum-product inference, this procedure yields exact results for tree-structured graphs, and approximate solutions for loopy graphs.
This is joint work with Nils Napp.
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