PT  - JOURNAL ARTICLE
AU  - Howard H.F. Tang
AU  - Peter D. Sly
AU  - Patrick G. Holt
AU  - Kathryn E. Holt
AU  - Michael Inouye
TI  - Systems biology and big data in asthma and allergy — recent discoveries and emerging challenges
AID  - 10.1183/13993003.00844-2019
DP  - 2019 Jan 01
TA  - European Respiratory Journal
PG  - 1900844
4099  - http://erj.ersjournals.com/content/early/2019/09/25/13993003.00844-2019.short
4100  - http://erj.ersjournals.com/content/early/2019/09/25/13993003.00844-2019.full
AB  - Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent “omic”-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or “endotypes” that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic, and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.With the recent influx of “big data” in asthma research, clinicians and scientists need to become familiar with analytical approaches that use systems-based methods to make sense of large datasets.FootnotesThis manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.Conflict of interest: Dr. Tang has nothing to disclose.Conflict of interest: Dr. Sly has nothing to disclose.Conflict of interest: Dr. Holt has nothing to disclose.Conflict of interest: Dr. Holt has nothing to disclose.Conflict of interest: Dr. Inouye has nothing to disclose.