Model certainty in cellular network-driven processes with missing data

• Main Authors: Irvin, M.W (Author), Lopez, C.F (Author), Ramanathan, A. (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2023
• Subjects: apoptosis; Apoptosis; Bayes theorem; Bayes Theorem; calibration; Calibration; machine learning; Machine Learning; Models, Theoretical; theoretical model
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 15537358 (ISSN)
• DOI: 10.1371/journal.pcbi.1011004

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a combined Bayesian and Machine Learning Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. For instance, two orders of magnitude more ordinal (e.g., immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g., fluorescence) data for calibration of an apoptosis execution model. Notably, ordinal and nominal (e.g., cell fate observations) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power. Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.