Bayesian Metamodeling of pancreatic islet architecture and functional dynamics

Abstract

The pancreatic islet (islet of Langerhans) is a mini-organ comprising several thousand endocrine cells, functioning jointly to maintain normoglycemia. Cellular networks within an islet were shown to influence its function in health and disease, but there are major gaps in our quantitative understanding of such architecture-function relations. Comprehensive modeling of an islet architecture and function requires the integration of vast amounts of information obtained through different experimental and theoretical approaches. To address this challenge, our lab has recently developed Bayesian metamodeling, a general approach for modeling complex systems by integrating heterogeneous input models. Here, we further developed metamodeling and applied it to construct a metamodel of a pancreatic islet. The metamodel relates islet architecture and function by combining a Monte-Carlo model of architecture trained on islet imaging data; and an ordinary differential equations (ODEs) mathematical model of function trained on calcium imaging, hormone imaging, and electrophysiological data. These input models are converted to a standardized statistical representation relying on Probabilistic Graphical Models; coupled by modeling their mutual relations with the physical world; and finally, harmonized through backpropagation. We validate the metamodel using existing data and use it to derive a testable hypothesis regarding the functional effect of varying intercellular connections. Since metamodeling currently requires substantial expert intervention, we also develop an automation tool for converting models to PGMs (step I) using feedforward neural networks. This automation is a first step towards automating the entire metamodeling process, working towards collaborative science through sharing of expertise, resources, data, and models.