CrossLabFit: A novel framework for integrating qualitative and quantitative data across multiple labs for model calibration

Rodolfo Blanco-Rodriguez, Tanya A Miura and Esteban Hernandez-Vargas

PLOS Computational Biology, 2025, vol. 21, issue 11, 1-20

Abstract: The integration of computational models with experimental data is a cornerstone for gaining insight into biomedical applications. However, parameter fitting procedures often require a vast availability and frequency of data that are challenging to obtain from a single source.Here, we present a novel methodology called “CrossLabFit”, which is designed to integrate data from multiple laboratories, overcoming the constraints of single-lab data collection. Our approach harmonizes disparate qualitative assessments, ranging from different experimental labs to categorical observations, into a unified framework for parameter estimation. By using machine learning clustering, these qualitative constraints are represented as dynamic “feasible windows” that capture significant trends to which models must adhere. For numerical implementation, we developed a GPU-accelerated version of differential evolution to navigate the cost function that integrated quantitative and qualitative information.We validate our approach across a series of case studies, demonstrating significant improvements in model accuracy and parameter identifiability. This work opens a new paradigm for collaborative science, enabling a methodological roadmap to combine and compare findings between studies to improve our understanding of biological systems and beyond.Author summary: Understanding complex biological processes often requires building mathematical models. These models can simulate how cells, tissues, or populations behave, but they must be calibrated to data. In practice, calibration typically uses measurements from a single experiment, the primary dataset the model aims to explain. Adding additional information can improve parameter estimates, but due to biological variability, data collected across experiments or labs are rarely aligned point-by-point. Even so, they carry trustworthy cues within ranges, for example, that a signal stays elevated over a time window, shows a single peak, or remains within certain range of values.

Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013704 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 13704&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013704

DOI: 10.1371/journal.pcbi.1013704

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().