We propose a scheme for computing Maximum Likelihood Estimators for Log-Linear modelsusing reaction networks, and prove its correctness. Our scheme exploits the toric structure of equilibrium points of reaction networks . This allows  an efficient encoding of the problem, and reveals how reaction networks are naturally suited to statistical inference tasks. Our scheme is relevant to molecular programming, an emerging discipline that views molecular interactions as computational primitives for the synthesis of sophisticated behaviors. In addition, such a scheme may  provide a template to understand how biochemical signaling pathways integrate extensive information about their environment and history.
We propose a novel molecular computing scheme for statistical inference. We focus on the much-studied statistical inference problem of computing maximum likelihood estimators for log-linear models. Our scheme takes log-linear models to reaction systems, and the observed data to initial conditions, so that the corresponding equilibrium of each reaction system encodes the corresponding maximum likelihood estimator. The main idea is to exploit the coincidence between thermodynamic entropy and statistical entropy. We map a Maximum Entropy characterization of the maximum likelihood estimator onto a Maximum Entropy characterization of the equilibrium concentrations for the reaction system . This allows for an efficient encoding of the problem, and reveals that reaction networks are superbly suited to statistical inference tasks. Such a scheme may also provide a template to understanding how in vivo biochemical signaling pathways integrate extensive information about their environment and history.
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scheme for computing Maximum Likelihood Estimators for Log-Linear modelsusing reaction networks, and prove its correctness. Our scheme exploits the toric structure of equilibrium points of reaction networks
novel molecular computing scheme for statistical inference. We focus on the much-studied statistical inference problem of computing maximum likelihood estimators for log-linear models. Our scheme takes log-linear models to reaction systems, and the observed data to initial conditions, so that the corresponding equilibrium of each reaction system encodes the corresponding maximum likelihood estimator. The main idea is to exploit the coincidence between thermodynamic entropy and statistical entropy. We map a Maximum Entropy characterization of the maximum likelihood estimator onto a Maximum Entropy characterization of the equilibrium concentrations for the reaction system
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Our scheme is relevant to molecular programming, an emerging discipline that views molecular interactions as computational primitives for the synthesis of sophisticated behaviors. In addition, such
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