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 http://bit.ly/2oUO1tG

中文翻译：

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哮喘和过敏的系统生物学和大数据——最近的发现和新出现的挑战

哮喘是一种由免疫和呼吸功能障碍引起的常见疾病，通常与过敏有关。系统视角可能有助于解开哮喘和过敏的复杂性。我们的目标是概述用于过敏和哮喘研究的系统生物学方法。具体来说，我们描述了最近的“组学”级别的发现，并研究了如何系统地整合这些发现以产生进一步的洞察力。目前的研究表明，过敏是由遗传和表观遗传因素驱动的，与微生物组和饮食等环境因素相结合，导致免疫发育的早期障碍和关键免疫炎症通路内的平衡破坏。遗传易感性和暴露的变异导致哮喘和其他过敏性疾病表现的异质性。机器学习方法正被用于探索这种异质性，并探索与哮喘和过敏亚表型相关的病理生理模式或“内型”。正在基于基因组、转录组和蛋白质组数据建立数学模型，以预测或区分疾病表型，并描述哮喘背后的生物分子网络。系统生物学在过敏和哮喘研究中的应用正在迅速增长，迄今为止已经取得了丰硕的成果。然而，这项研究的规模和多学科性质意味着它伴随着新的挑战。最终，希望系统医学将组学数据整合到临床实践中，可以为更精确、个性化和有效的哮喘管理铺平道路。随着最近哮喘研究中“大数据”的涌入，临床医生和科学家需要熟悉使用基于系统的方法来理解大型数据集的分析方法 http://bit.ly/2oUO1tG