Growing evidence suggests an etiological link between asthma and chronic obstructive pulmonary disease (COPD) wherein mild-to-moderate persistent asthmatics are susceptible to persistent airflow obstruction, putting them at a higher risk for developing COPD. Furthermore, epigenetic modulation due to in utero smoke (IUS) exposure during fetal lung development may play a role in asthma and COPD. These and other data suggest a lifelong trajectory of lung impairment from prenatal lung development to childhood asthma to COPD in adulthood. Longitudinal and carefully phenotyped multi-omic data offer the opportunity to understand the molecular determinants of this disease trajectory. Systems and network biology methods hold the potential to effectively integrate, analyze and interpret multi-omics data. Multilayer networks, in particular, offer a feasible first step to model disease perturbations jointly across multiple molecular levels, from the genome to the proteome. In this application, we will simultaneously analyze the rich multi-omics data (SNP genotyping, DNA methylation, mRNA and miRNA expression) collected as part of long-standing asthma and COPD cohorts using multilayer network methods. We will first integrate IUS exposure, asthma and COPD multi-omics data into networks and develop their statistical framework to facilitate subsequent bioinformatics analyses. We will then track the developmental origins of asthma by the integrated temporal analysis of fetal lung and childhood asthma multi-omics data. Finally, we will identify key molecules and pathways of the phenotypic transition from asthma in early life to COPD in adulthood using multilayer networks. Dr. Halu?s training in statistical physics and complex networks has prepared him well for his proposed research. However, understanding the molecular basis connecting complex lung diseases such as asthma and COPD through the analysis of multi-omics data is a formidable task that will require further training in specific areas. Dr. Halu will leverage the excellent intellectual environment of Harvard Medical School (HMS) and its teaching hospitals, and will have access to extensive computational resources through the Channing Division of Network Medicine and HMS. Through formal coursework and workshops, and with the help of a mentoring and advisory team with complementary expertise, Dr. Halu will immerse himself in a training program focusing on statistical genetics, epigenetics, and omics integration, big data in medical informatics, and the biology of pulmonary diseases and clinical translation. Dr. Halu will also participate in regular meetings with his mentors and advisory board members, which will allow him to share his progress. Altogether, Dr. Halu?s training and research plan will enable him to expand his current skill set to include the ability to address the challenges of analyzing the complex genomic and epigenomic data of large epidemiological cohorts, identify open questions in the systems biology of asthma and COPD, and ultimately contribute to the precision medicine of lung disease.

Public Health Relevance

Increasing molecular evidence suggests a lifelong trajectory of lung impairment from prenatal lung development to asthma in childhood to chronic obstructive pulmonary disease (COPD) in adulthood. Multi-omics data collected over time across well-characterized cohorts hold the potential to understand this complex disease progression. Here, we propose to apply network-based computational methods that will integrate and jointly analyze genetic, epigenetic and transcriptomic data to better understand the temporal progression from fetal lung development to childhood asthma, and to determine the key molecules mediating between asthma and COPD.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Mentored Quantitative Research Career Development Award (K25)
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NHLBI Mentored Transition to Independence Review Committee (MTI)
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Lu, Qing
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Brigham and Women's Hospital
United States
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