Systems Biology Approaches to Microbiome Research


Drs. Daniel Segrè ( and Evan Johnson (


Understanding microbial communities can have great impact on human health and on environmental sustainability. Despite the availability of large datasets and theoretical frameworks, computational tools are needed to transform microbiome science into a predictive discipline. The goal of this ARC is to develop a new, multi-level mechanistic understanding of how microbe-microbe, microbe- environment, and microbe- host interactions determine microbial community dynamics, diversity and stability, and use this knowledge to understand how to engineer microbial communities for defined purposes. The aim is to accomplish this goal by combining systems biology models of metabolic networks, physics-based theory of ecosystem dynamics, experimental studies of molecular-level processes, and microbial community data analysis. In the past round of ARC founding, there has been great progress towards the construction of new computational tools that will collectively constitute a core resource for multiple BU investigators, and for the broad community, the “Microbiome Junction” (MJ). This will serve as the point of convergence of multiple data types generated by different investigators, helping them interpret the data in the form of microbial interaction networks. Going forward, in addition to improving the user interface of existing tools and implementing a unified website, the plan is to pursue algorithms for integration of multiple data types, (including meta-transcriptomics, metabolomics and simulation data), and explore the applicability of our tools to address questions related to uncharted territory in human microbiome and environmental microbiology.


Vipul Chitalia, Medicine
Charles DiLisi, Engineering, Bioinformatics
Zhenjun Hu, Bioinformatics
Kirill Korolev, Bioinformatics
Pankaj Mehta, Physics
Jennifer Talbot, Biology