Understanding Connectivity of Pancreatic Beta Cells through Artificial Neural Networks

Abstract

The islet of Langerhans consists of hundreds of beta cells (β-cells) whose synchronization is key to the proper secretion of insulin from the endocrine component of the pancreas. Experiments have suggested the existence of a type of β-cell in the islet, called the hub cell, which controls islet synchronicity. If silenced, the hub cell appears to desynchronize the islet. Simulations based on the experimental data have not confirmed the proposed high functional connectivity of hub cells. Instead, numerical exploration has shown the existence of a similar β-cell, termed the switch cell, which can control the activity of the islet but is not characterized by high functional connectivity. We used artificial neural network techniques to identify islets containing switch cells based upon cell characteristics and cell-coupling values. We began with a two-cell network using three parameters to identify a switch islet. Our network accurately predicted switch islets using both homogeneous and heterogeneous coupling values. We moved on to test a three-cell network and will continue to scale up to a network with 57 or more cells. The network can be used to discover what biophysical features are important for defining islets with switch cells.