Year: 2013
Type: article
Abstract: Recent work on molecular programming has explored new possibilities for com-putational 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 rea-son under uncertainty. At this small scale, systems are typically modeled as chem-ical reaction networks. In this work, we develop a procedure that can take arbitrary probabilistic graphical models, represented as factor graphs over discrete random variables, and compile them into chemical reaction networks that implement infer-ence. In particular, we show that marginalization based on sum-product message passing can be implemented in terms of reactions between chemical species whose concentrations represent probabilities. We show algebraically that the steady state concentration of these species correspond to the marginal distributions of the ran-dom variables in the graph and validate the results in simulations. As with stan-dard sum-product inference, this procedure yields exact results for tree-structured graphs, and approximate solutions for loopy graphs. 1
Authors: Nils Napp, Ryan P. Adams
Language: English
Topic: DNA and Biological Computing
Subfield: Theoretical Computer Science
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2166918926