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Channing Division of Network Medicine Research

The Channing Division of Network Medicine is organized into three units that, together, create an unparalleled multidisciplinary research environment.

Chronic Disease Epidemiology

The Chronic Disease Epidemiology unit has developed several of the world’s largest longitudinal study populations, with a total sample size of approximately 280,000 subjects, including the landmark Nurses’ Health Study, Study and others such as the Growing Up Today Study. Read more about our cohorts- Cohorts.

The epidemiology of a broad range of complex chronic diseases is studied in these cohorts using traditional and novel approaches, including genomics, metabolomics, and molecular pathoepidemiology.

Systems Genetics and Genomics

The Systems Genetics and Genomics unit has grown from a focus on the genetic and environmental determinants of complex respiratory diseases, most notably asthma and chronic obstructive pulmonary disease (COPD), to use a variety of integrated genomics approaches to studying multiple disease areas.

Our study populations in asthma and COPD house, in total, DNA samples for approximately 100,000 well-phenotyped subjects. Read more about our cohorts- COPD study populations and Asthma study populations.

Systems Pathobiology

The Systems Pathobiology unit develops multidisciplinary approaches to complex diseases, with a focus on methods development for network research.

  • Dynamic systems and control theory: Many dynamic properties of complex biological systems can be learned from the structure of the underlying networks. We combine tools from control theory and network science to address a series of questions related to complex biological systems, from systems identification to optimal control.
  • Combinatorial optimization and statistical physics: This approach develops new formalizations of disease module detection and uses tools from statistical physics to guide the algorithmic development. By uncovering the molecular pathways through which multiple genetic factors jointly affect a disease phenotype, network-based approaches emerged as powerful tools for studying complex diseases.