title: An in silico modeling approach to understanding the dynamics of sarcoidosis.
abstract: BACKGROUND
  Sarcoidosis is a polygenic disease with diverse phenotypic presentations characterized by an abnormal antigen-mediated Th1 type immune response. At present, progress towards understanding sarcoidosis disease mechanisms and the development of novel treatments is limited by constraints attendant to conducting human research in a rare disease in the absence of relevant animal models. We sought to develop a computational model to enhance our understanding of the pathological mechanisms of and predict potential treatments of sarcoidosis.
  
  
  RESULTS
  Based upon the literature, we developed a computational model of known interactions between essential immune cells (antigen-presenting macrophages, effector and regulatory T cells) and cytokine mediators (IL-2, TNFα, IFNγ) of granulomatous inflammation during sarcoidosis. The dynamics of these interactions are described by a set of ordinary differential equations. The model predicts bistable switching behavior which is consistent with normal (self-limited) and "sarcoidosis-like" (sustained) activation of the inflammatory components of the system following a single antigen challenge. By perturbing the influence of model components using inhibitors of the cytokine mediators, distinct clinically relevant disease phenotypes were represented. Finally, the model was shown to be useful for pre-clinical testing of therapies based upon molecular targets and dose-effect relationships.
  
  
  CONCLUSIONS
  Our work illustrates a dynamic computer simulation of granulomatous inflammation scenarios that is useful for the investigation of disease mechanisms and for pre-clinical therapeutic testing. In lieu of relevant in vitro or animal surrogates, our model may provide for the screening of potential therapies for specific sarcoidosis disease phenotypes in advance of expensive clinical trials.
authors.affiliation: Neuro-Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, United States of America.
authors.forename: Baltazar D
authors.initials: BD
authors.lastname: Aguda
authors.forename: Clay B
authors.initials: CB
authors.lastname: Marsh
authors.forename: Michael
authors.initials: M
authors.lastname: Thacker
authors.forename: Elliott D
authors.initials: ED
authors.lastname: Crouser
mesh_terms: D000906:Antibodies / Q000494:pharmacology / Q000627:therapeutic use; D000941:Antigens / Q000276:immunology; D002450:Cell Communication / Q000187:drug effects; D003198:Computer Simulation; D004359:Drug Therapy, Combination; D006099:Granuloma / Q000276:immunology / Q000473:pathology; D006801:Humans; D007371:Interferon-gamma / Q000276:immunology; D007376:Interleukin-2 / Q000276:immunology; D008213:Lymphocyte Activation / Q000187:drug effects / Q000276:immunology; D018448:Models, Immunological; D010641:Phenotype; D012507:Sarcoidosis / Q000188:drug therapy / Q000276:immunology* / Q000473:pathology; D050378:T-Lymphocytes, Regulatory / Q000187:drug effects / Q000276:immunology; D018417:Th1 Cells / Q000187:drug effects / Q000276:immunology; D014409:Tumor Necrosis Factor-alpha / Q000276:immunology
chemical_list: D000906:Antibodies; D000941:Antigens; D007376:Interleukin-2; D014409:Tumor Necrosis Factor-alpha; D007371:Interferon-gamma