The Santa Fe Institute (SFI) presents “Optimal Learning For Finding Minimal Peptide Substrates” 12:15 p.m. Friday, April 24 at Collins Conference Room in Santa Fe.
Scientists use laboratory experiments to search for molecules with desirable properties, e.g., that make efficient solar cells; or that cure cancer. The success of such a search hinges on making good decisions about which experiments to perform.
We show how Bayesian statistics and value of information analysis can be used to choose good experiments, that reach experimental goals more reliably with fewer experiments, and describe how these methods were used to find minimal peptide substrates for a pair of protein-modifying enzymes.
These novel peptides support reversible protein tagging, with application to medicine and biochemical sensors.