Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetry Article Swipe
Lisa Arndt
,
Douglas R. Walker
,
Elisar Barbar
,
Daniel M. Zuckerman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.07.09.663792
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.07.09.663792
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 but also removes an ambiguity typically present in Bayesian analysis by self-consistently refining concentration estimates, ensuring optimal joint inference of binding parameters and concentrations. Using modern Monte Carlo techniques enables our pipeline to provide robust posterior sampling for more than 10 datasets and 40 total parameters. We validate the approach with synthetic ITC datasets for single- and multi-site binding models and apply it to experimental data, including 14 datasets for 1:1 binding of Mg(II) to the chelator EDTA and multiple datasets of the hub protein LC8 with diverse binding partners. This work serves as a foundation for improving the precision of binding constants using multiple ITC datasets, while providing a systematic framework for assessing the reliability of experimental concentration estimates, paving the way for more accurate biomolecular interaction studies.
Related Topics
Concepts
Isothermal titration calorimetry
Ambiguity
Inference
Bayesian probability
Isothermal process
Titration
Calorimetry
Bayesian inference
Econometrics
Statistical inference
Computer science
Statistics
Artificial intelligence
Mathematics
Chemistry
Thermodynamics
Physics
Physical chemistry
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.07.09.663792
- https://www.biorxiv.org/content/biorxiv/early/2025/07/11/2025.07.09.663792.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412360284
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412360284Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.07.09.663792Digital Object Identifier
- Title
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Improving parameter inference by resolving Bayesian prior ambiguity via multi-dataset analysis: Application to isothermal titration calorimetryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-11Full publication date if available
- Authors
-
Lisa Arndt, Douglas R. Walker, Elisar Barbar, Daniel M. ZuckermanList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.07.09.663792Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/07/11/2025.07.09.663792.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2025/07/11/2025.07.09.663792.full.pdfDirect OA link when available
- Concepts
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Isothermal titration calorimetry, Ambiguity, Inference, Bayesian probability, Isothermal process, Titration, Calorimetry, Bayesian inference, Econometrics, Statistical inference, Computer science, Statistics, Artificial intelligence, Mathematics, Chemistry, Thermodynamics, Physics, Physical chemistry, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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