Model certainty in cellular network-driven processes with missing data

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.Author summary: Mathematical models used to explore network-driven cellular processes from a systems perspective face a challenge in the dearth of quantitative data required for model calibration. We address this challenge with a combined Bayesian and Machine Learning measurement model approach to explore how quantitative and many non-quantitative data constrain models of apoptosis execution. We find model prediction accuracy and certainty depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. We also find different non-quantitative dataset can be combined to synergistically better support model calibration. Finally, we demonstrate the use of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.