The goal is to design an "intelligent chemical soup" that can do statistical inference. This may have niche technological applications in medicine and biological research, as well as provide fundamental insight into the workings of biochemical reaction pathways. As a first step towards our goal, we describe a scheme that exploits the remarkable mathematical similarity between log-linear models in statistics and chemical reaction networks. Our scheme encodes the information in a log-linear model into a chemical reaction network. Observed data is encoded as initial concentrations. We prove that concentrations asymptotically tend towards the maximum likelihood estimators. The simplicity of our scheme suggests that molecular environments, especially within cells, may be particularly well suited to performing statistical computations.
Manoj Gopalkrishnan has recently joined as an Associate Professor in Electrical Engineer at IIT Bombay. He was previously a faculty member in the School of Technology and Computer Science at TIFR Mumbai. He received his PhD from University of Southern California and BTech from IIT Kharagpur. He is broadly interested in connections between Information, Biology, and Physics.