Work

Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures

Year: 2022

Type: article

Abstract: Bayesian regression is performed to infer parameters of thermodynamic binding models from isothermal titration calorimetry measurements in which the titrant is an enantiomeric mixture. For some measurements the posterior density is multimodal, indicating that additional data with a different protocol are required to uniquely determine the parameters. Models of increasing complexity-two-component binding, racemic mixture, and enantiomeric mixture-are compared using model selection criteria. To precisely estimate one of these criteria, the Bayes factor, a variation of bridge sampling is developed.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0273656

Authors: Trung Hai Nguyen, Van N. T. La, Kyle Burke, David D. L. Minh

Institutions: Ton Duc Thang University, Illinois Institute of Technology

Language: English

Topic: thermodynamics and calorimetric analyses

Subfield: Physical and Theoretical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,805

Funders: National Science Foundation, Directorate for Mathematical and Physical Sciences, Division of Chemistry