The Role of Systems Biology in Deciphering Asthma Heterogeneity


Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Original languageEnglish
Article number1562
Issue number10
Pages (from-to)1562
Publication statusPublished - 10.2022

Research Areas and Centers

  • Academic Focus: Center for Infection and Inflammation Research (ZIEL)

DFG Research Classification Scheme

  • 205-32 Medical Physics, Biomedical Engineering
  • 205-22 Clinical Immunology and Allergology
  • 204-05 Immunology
  • 205-07 Medical Informatics and Medical Bioinformatics


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