Bayesian metamodeling of complex biological systems across varying representations

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Comprehensive cell modeling necessitates integrating diverse information on cellular components and functions.
• Existing modeling approaches often struggle with heterogeneity in data types, mathematical representations, and scales.

Purpose of the Study:

• Introduce Bayesian metamodeling as a general framework for integrating heterogeneous input models of complex biological systems.
• Develop a proof-of-principle metamodel for glucose-stimulated insulin secretion in human pancreatic beta-cells.

Main Methods:

• Input models are converted to a standardized statistical representation using probabilistic graphical models.
• Models are coupled by defining their mutual relations with the physical world.
• Heterogeneous models are harmonized to create a unified metamodel.

Main Results:

• A proof-of-principle Bayesian metamodel for glucose-stimulated insulin secretion was successfully constructed.
• The metamodel integrated diverse input models including spatiotemporal simulations, molecular networks, and ordinary differential equations.
• The approach demonstrated potential for improved accuracy, precision, and completeness in biological modeling.

Conclusions:

• Bayesian metamodeling provides a robust framework for integrating heterogeneous models in systems biology.
• This approach facilitates collaborative science by enabling sharing of expertise, data, and models.
• The developed metamodel offers a more accurate and complete representation of pancreatic beta-cell function.