Speaker
Description
A fundamental question in the field of molecular computation is what computational tasks biochemical systems are capable of carrying out. In this talk, we will see that chemical reaction networks can do maximum likelihood estimation of log-affine models in the following sense: Given a basis for the kernel of the design matrix of a given model, we construct a detailed-balanced network such that the MLE can be read off from the unique equilibrium when the initial concentrations are set to the observed distribution. Interestingly, the choice of basis for the kernel in the construction has a large influence on the dynamical properties and chemical complexity of the network. The desire to make this choice "optimally" (in a number of different senses of the word) leads to several interesting questions at the crossroads between dynamics, chemistry, statistics, and algebra. This is based on the paper \cite{HARY25}.
Bibliography
@article{HARY25,
author = {Henriksson, Oskar and Am{\'e}ndola, Carlos and Rodriguez, Jose Israel and Yu, Polly Y.},
title = {Maximum likelihood estimation of log-affine models using detailed-balanced reaction networks},
fjournal = {Journal of Mathematical Biology},
journal = {J. Math. Biol.},
issn = {0303-6812},
volume = {91},
number = {4},
pages = {30},
year = {2025},
language = {English},
doi = {10.1007/s00285-025-02262-5},
}
