µBiospheres SFA

Tools and functionality to support a systems biology approach to understanding interactions in bioenergy-relevant microbial communities

The µBiospheres Science Focus Area (SFA): Community systems biology of microscale interactions for sustainable bioenergy is led by Dr. Rhona Stuart at Lawrence Livermore National Laboratory (LLNL), and focuses on microbial interactions within algal and plant “microbiospheres” of influence (phycosphere, rhizosphere, endosphere, hyphosphere). The ultimate goal is to discover cross-cutting principles that regulate formation and maintenance of these interactions and their system-level resource allocation consequences, in order to develop a general predictive framework for system-level impacts of microbial partnerships. The approach encompasses single cell analyses, quantitative isotope tracing of elemental exchanges, system-scale ‘omics measurements, and multi-scale modeling including constraint-based genome-scale metabolic modeling to predict and test interactions within microbial communities.

The µBiospheres SFA improved the quality of genome annotations to improve draft metabolic models within KBase, by developing tools for importing, comparing, and merging metabolic annotations. These tools expedite the curation process during model building and produce the high-quality, multi-evidenced curated models needed to investigate the complex interactions within microbial communities.

Longer term, the team seeks to engage with KBase to interpret and integrate data streams to further goals around understanding and predicting ecological, biophysical, and biochemical dynamics of multi-taxa communities, as well as the metabolite fluxes that regulate trophic interactions.

Collaborative Development Projects

Improving Draft Metabolic Models:

Create annotation tools that accept multiple lines of evidence to determine an optimal consensus annotation from data like EC numbers, KEGG, and MetaCyc reaction identifiers.

Functionality and Tools

Probabilistic Annotation and Ensemble Metabolic Modeling

Given multiple multiple annotation sources: 1) calculate reaction probabilities, 2) propagate reaction probabilities into metabolic modeling, and 3) condense results of probabilistic modeling to actionable insights. (in Development)

Additional Information

Meet the Members

SFA Principal Investigator: Rhona Stuart1
KBase Contact: Chris Henry2,3
Lead Developers: Patrik D’haeseleer1, Jeffrey Kimbrel1
Project Team Leads: Patrik D’haeseleer1, Ali Navid1, Ben Bowen4
Affiliations: 1Lawrence Livermore National Laboratory, 2Argonne National Laboratory, 3KBase, 4Lawrence Berkeley National Laboratory