Year: 2012
Type: article
Abstract: The aim of this study is to build a mathematical model of the pancreas and run this model in lifelong simulations in order to understand the mechanisms that predict an increased risk of pancreatitis. The dilemma is that pancreatitis is a complex process with multiple variables, which currently make the onset, severity and outcomes unpredictable in individual patients. Genetic, environmental and metabolic factors are likely to be important for disease severity and progression, with somewhat stochastic events initiating theprocess when stress leads to injury signals. Modeling should begin in the acinar cell, with ability to incorporate duct cells, inflammatory cells, other cells and their interactions into large model. In this work we attempted to build a foundational model that incorporates the main features and interactors. We built a framework that focuses on trypsinogen activation as a major cause of auto-digestion and injury. Additional variables such as production ofpancreatic secretory trypsin inhibitor(PSTI) molecules as a defense line against active trypsin as well as bicarbonate secretion were included in the model. The effects of mutation were modeled as modified rates of trypsinogen production/activation, trypsin inactivation, PSTI production, and bicarbonate secretion. Our framework contains three compartments that represent domains where trypsin could be activated. The domains are acinar cell, lumen of the acinus, and main duct of the pancreas. We used a stochastic approach to test the general flow of the simulation. The public health and translational significance of this model is thatit would help us to understand the physiology of the pancreas more deeply. It would also allow us to consider many factors that lead to pancreatitis and predict their behavior under different conditions (e.g., therapy).
Author: Yerkebulan Talzhanov
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W193668163