Identifying Bayesian optimal experiments for uncertain biochemical pathway models.
Sci Rep
; 14(1): 15237, 2024 07 02.
Article
in En
| MEDLINE
| ID: mdl-38956095
ABSTRACT
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bayes Theorem
Limits:
Humans
Language:
En
Journal:
Sci Rep
Year:
2024
Document type:
Article