A Scheme for Molecular Computation of Maximum Likelihood Estimators for Log-Linear Models
This work addresses statistical inference challenges for researchers in computational biology and molecular computing, offering a novel approach that could inform understanding of biochemical signaling pathways.
The authors tackled the problem of computing maximum likelihood estimators for log-linear models by proposing a molecular computing scheme that maps statistical inference to reaction systems, resulting in an efficient encoding where equilibrium concentrations encode the estimators.
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.