A computer simulation of lung disease will be created to be a tool for virtual experimentation. It will incorporate what is known about the complex biological processes involved in lung injury and repair and apply it to the analysis of acute and chronic, fibrotic, interstitial lung diseases. The simulation process is called agent-based modeling, a state of the art method for logical computational modeling(1-4). This dynamic model will explore the possible causes of interstitial lung disease(s) including idiopathic (the cause is unknown) pulmonary fibrosis (IPF), an untreatable and deadly lung disease(5). Although IPF is rare, the death rate is increasing [55.1 deaths/106 in 2003;(6)]. There are numerous theories about the causes of IPF, but no cure is in sight(7). There are several forms of pulmonary fibrosis (PF), some are chronically debilitating and others may be overcome with treatment(8). There is enough understanding to diagnose the form of the disease after its onset. The pharmaceutically controllable forms of PF provide information about mechanisms that do not propagate IPF, and years of experimentation with animal models and in- vitro studies also provide information that will be integrated into the computer simulation. This computer simulation will be an extension of an existing, validated, published and publicly available, agent-based simulation of the immune system, created by the principal investigator (9). This multidisciplinary group of pulmonologists, a pulmonary pathologist, a murine PF modeler and an immunologist/computer modeler will condense relevant information about lung diseases and lung repair mechanisms into a (logical, plain English) form that can be programmed into the computer model. Al of the lung cells and cytokines/chemokines involved will be represented. This is an activity that rarely occurs in the world of biomedical research, the combination of existing information to gain insight into the system as a whole. Agent-based modeling was invented for this purpose, to analyze complex systems (like the lung and immune systems) using information gathered about the parts of the system via the traditional laboratory and clinical approaches. The simulation will be validated by comparison of the behavior (and output) from the simulated injured lung to existing pathology specimens with known diagnosis and outcomes from human patients. Simulation parameters will be adjusted iteratively [using indirect parameterization(3)] until the simulation output is comparable to disease patterns with known cause and effect. Then the numerous postulated causes for IPF will be tested to determine if any of them cause patterns of simulated lung cell behavior that emulate real disease processes. Everyone involved will benefit from the learning experience that agent-based modeling provides. More importantly, virtual experimentation will provide insight into an incurable disease that attacks people in their prime, and will provide information that indicates potential strategies for successful intervention.
The time has come for the scientific information that exists about incurable lung diseases such as pulmonary fibrosis to be compiled and analyzed using the latest computational methods. Computational modeling based on logic will be used to analyze the existing, abundant scientific knowledge base and generate logical conclusions about plausible causes for these debilitating lung diseases. This approach can help determine what interventions may potentially be employed for their prevention or cure.
| Nuovo, Gerard J; Hagood, James S; Magro, Cynthia M et al. (2012) The distribution of immunomodulatory cells in the lungs of patients with idiopathic pulmonary fibrosis. Mod Pathol 25:416-33 |
| Folcik, Virginia A; Broderick, Gordon; Mohan, Shunmugam et al. (2011) Using an agent-based model to analyze the dynamic communication network of the immune response. Theor Biol Med Model 8:1 |
