Simulating Pancreatic ß-Cell Metabolism with Flux Balance Analysis

The pancreatic ß-cell monitors blood glucose and affects its concentration via insulin. Conceptually, we can formulate this as a simple control loop: Increased intracellular glucose leads to increased intracellular ATP, subsequent depolarization and release of insulin, which decreases blood glucose. Prior measurements of the ß-cell`s metabolome and gene expression revealed a thrifty mode of gene expression, as the ß-cell uses but a limited number of metabolites and enzymes in its single-minded pursuit of insulin synthesis and secretion. However, this deceptively straight-forward model belies the complex relation between the scant players, manifesting as both tight blood-glucose control, and derangements such as ß-cell exhaustion and diabetes mellitus.
We study this system by applying a computational technique known as Flux Balance Analysis (FBA). Here, we consider the complete metabolic network of the ß-cell, allowing us to express its steady-state behavior as a linear optimization problem. This approach has proved successful in predicting the behavior of the growth of bacteria on various media, human cancer metabolism and more.
The ß-cell, with its apparently simple metabolism and virtually singular goal of glucose management via insulin, is a prime target for FBA. We form a ß-cell representation based on network models reconstructed from human cells, which are then parsed down according to ß-cell gene expression. Then, we simulate the various scenarios encountered by healthy and sick ß-cells by restricting the network`s input and output, and derive the appropriate steady-state results and network utilization. Ideally, this formulation will elucidate the inner workings of cells under physiological and pathological scenario.