Many kinds of biological networks are encompassed by the field of network medicine. These include, but are not limited to, protein-protein interactions networks, gene regulatory networks, metabolic networks, ecological networks, and phenotypic networks.
The goal of Channing Division of Network Medicine network methods investigators is to develop new network-based approaches that have multidisciplinary applications, including in the domains of network medicine and complex diseases.
Our group has developed multiple network methods that span a broad spectrum of expertise in network science and can be applied to:
Over the past few years, our group has developed a suite of methods to effectively integrate multi-omic data and reconstruct gene regulatory networks.
The basis of this work is a method we developed called PANDA (Passing Attributes between Networks for Data Assimilation) that constructs directed genome-wide regulatory networks by using a “message passing” approach to integrate multiple types of genomic data, including protein-protein interactions, gene expression, and predicted transcription factor regulatory relationships. We have since expanded PANDA to also integrate micro-RNA regulatory relationships and epigenetic information in the form of DNase hypersensitivity data.
We coexist with a vast number of microbes—our microbiota—that live in and on our bodies. Although much has been learned about the diversity and distribution of human-associated microbial communities, little is known about the biology of microbiota, how it interacts with the host, and how the host responds to its resident microbiota.
The complex topology and dynamics of the ecological network underlying the human gut microbiota make quantitative study of microbiome-based therapies extremely difficult. Our group works to more fully understand the structure and dynamics of our gut microbial ecosystems.
Our long-term objective is to construct a modeling framework based on community ecology and dynamical systems to better design microbiome-based therapies.
Control theory deals with dynamic systems that respond to external inputs with specific output signals. Concepts common in control theory, from dynamics and feedback to signal processing, found their way into biology, especially in systems biology, where the need to capture systems-level knowledge requires us to conceptualize and organize large amount of quantitative biological interactions.
We use tools from control theory to analyze biochemical reaction networks and genetic regulatory networks, and to learn the dynamic properties of complex biological systems from the structure of these networks.
