title: Asthma phenotyping, therapy, and prevention: what can we learn from systems biology?
abstract: Asthma has a high prevalence worldwide, and contributes significantly to the socioeconomic burden. According to a classical paradigm, asthma symptoms are attributable to an allergic, Th2-driven airway inflammation that causes airway hyperresponsiveness and results in reversible airway obstruction. Diagnosis and therapy are based mainly on these pathophysiologic concepts. However, these have increasingly been challenged by findings of recent studies, and the frequently observed failure in controlling asthma symptoms. Important recent findings are the protective "farm effect" in children, the possible prenatal mechanisms of this protection, the recognition of many different asthma phenotypes in children and adults, and the partly disappointing clinical effects of new targeted therapeutic approaches. Systems biology approaches may lead to a more comprehensive view of asthma pathophysiology and a higher success rate of new therapies. Systems biology integrates clinical and experimental data by means of bioinformatics and mathematical modeling. In general, the "-omics" approach, and the "mathematical modeling" approach can be described. Recently, several consortia have been attempting to bring together clinical and molecular data from large asthma cohorts, using novel experimental setups, biostatistics, bioinformatics, and mathematical modeling. This "systems medicine" approach to asthma will help address the different asthma phenotypes with adequate therapy and possibly preventive strategies.
authors: Systems Biology Platform & Molecular Pulmonology/iLung, German Center for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Marburg, Germany.
authors: Alexandra
authors: A
authors: Sittka
authors: Julio
authors: J
authors: Vera
authors: Xin
authors: X
authors: Lai
authors: Bernd T
authors: BT
authors: Schmeck
mesh_terms: D000818:Animals; D001249:Asthma / Q000235:genetics* / Q000378:metabolism / Q000503:physiopathology*; D015415:Biomarkers / Q000378:metabolism; D056808:Biostatistics / Q000379:methods; D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods; D055106:Genome-Wide Association Study; D006801:Humans; D007249:Inflammation / Q000276:immunology; D018793:Interleukin-13 / Q000378:metabolism; D015848:Interleukin-5 / Q000378:metabolism; D051379:Mice; D008962:Models, Theoretical; D010641:Phenotype; D017393:RNA Editing; D049490:Systems Biology / Q000379:methods*; D018418:Th2 Cells / Q000276:immunology
chemical_list: D015415:Biomarkers; D018793:Interleukin-13; D015848:Interleukin-5