Defining informative priors for ensemble modeling in systems biology

Area of Science:

• Molecular Systems Biology
• Computational Biology
• Biophysics

Background:

• Ensemble modeling in molecular systems biology necessitates reproducible kinetic parameter data translation into probability distributions (priors).
• Existing frameworks lack methods for generating informative priors while maintaining thermodynamic consistency.
• This gap hinders the development and application of robust ensemble models.

Purpose of the Study:

• To present a protocol for generating informative probability distributions (priors) for ensemble modeling.
• To address the challenge of creating priors from diverse kinetic parameter data sources.
• To enable thermodynamically consistent parameter sampling for molecular models.

Main Methods:

• Protocol for collecting parameter values from literature, databases, and experiments.
• Assessment of parameter plausibility and creation of log-normal probability distributions.
• Method for sampling from distributions while preserving thermodynamic consistency.

Main Results:

• A protocol for generating informative priors for ensemble modeling is presented.
• The protocol facilitates the creation of log-normal probability distributions from diverse data.
• Thermodynamic consistency is maintained during parameter sampling.

Conclusions:

• The protocol effectively fills the gap in generating informative priors for ensemble modeling.
• It offers a rapid (~5-10 min/parameter) and reproducible method.
• Facilitates advanced ensemble modeling in synthetic biology and systems medicine.