An Agent Based Model of Liver Damage, Inflammation, and Repair: in Silico Translation of Cellular and Molecular Mechanisms to the Clinical Phenomena of Cirrhosis Using Netlogo

Abstract

Introduction: The liver is one of the largest and most metabolically active organs in the body. It plays a vital role in metabolism, protein synthesis and detoxification, and its proper functioning is vital to the maintenance of health. Fortunately, the liver has an enormous capacity for regeneration and repair. However, in certain situations repeated injury/insult leads to progressive scarring and eventual reduced function, a condition known as cirrhosis. Currently cirrhosis cannot be reversed once it has occurred, and despite a great deal of research the actual mechanisms of liver cirrhosis are not well understood. There is current research on the role of inflammation on the evolution of cirrhosis, but these mechanisms exist in multiple parallel and redundant pathways, and it is difficult to characterize the overall effects of each of these mechanisms merely by examining them in isolation. What is needed is a means by which the accumulated knowledge regarding the pathogenesis of cirrhosis can be integrated into a platform that would allow representation of the behavior of the system as a whole. This can be accomplished using dynamic, mechanistic computer modeling. Agent based modeling (ABM) is a type of computational modeling that can be used for this purpose. Methods: An abstract ABM of liver damage, inflammation, and repair was developed using NetLogo. Rules for the cellular agents were derived from literature reviews of the mechanistic knowledge regarding the cellular and molecular components of the pathogenesis of cirrhosis. “Influence” diagrams of these interactions were created and converted into a computational model. The model was designed to resemble a generalized liver structure of the functional hepatic lobule. Cellular agents include hepatocytes, liver endothelial cells, kupffer cells, stellate cells, hepatic progenitor cells, and inflammatory cells. The dynamic interplay between the various cellular agents involved in the process of repair exemplifies key control points that tip the liver from healthy regeneration to the development of cirrhosis. Results: The liver cirrhosis ABM was able to simulate the general dynamics of typical liver repair when exposed to environmental stress and damage. By varying the frequency and magnitude of the stressors the model was able to simulate the loss of the regenerative capabilities of the normal liver and the transition from simple hepatocyte division to more complex processes including the division and differentiation of hepatic progenitor cells and the incorporation of bone marrow stem cells into the liver and the associated fibrotic activity. Visually, the modeled responses closely resemble histological preparations of a cirrhotic hepatic lobule. Discussion: The production of a model of the development of liver cirrhosis in an in silico environment provides a way of dynamically representing mechanistic knowledge that is very often depicted in static diagrams, and will potentially allow researchers to “bring their hypotheses to life.” The ABM is a tool which can be used to better predict patient outcomes by altering the variable of the software. In the future, the ABM could even be used to examine the efficacy of treatments in attempting to reverse or halt the progression of liver cirrhosis. In summary, this project fills a significant translational gap in way we as physicians and scientists