Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetry.

ABSTRACT: Isothermal titration calorimetry (ITC) is a powerful technique for probing biomolecular interactions. However, accurate determination of binding parameters-such as enthalpy and free energy-as well as associated uncertainties can be hindered by noise and concentration variability. Notably, the mathematical ambiguity surrounding analyte concentrations in standard binding models intrinsically limits the precision with which binding parameters, particularly binding enthalpies, can be determined. Here, we present a Bayesian pipeline that resolves this ambiguity by combining two key strategies: simultaneous analysis of multiple ITC datasets and a hierarchical Bayesian treatment of analyte concentration priors. This dual approach not only lifts the degeneracy inherent in single-dataset studies bu

SUBMITTER: Otten L 

PROVIDER: S-EPMC12265561 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature