Collin Timm — Oak Ridge National Laboratory
KBase is a great environment with many tools that are easy to use. Using the metabolic modeling (FBA) tools we have studied differences in metabolic properties of Pseudomonas fluorescens isolates from Populus trees. As a first-time user of metabolic modeling tools I found it very fast (~15 minutes) and easy (~5 steps) to go from genome sequence to draft models that could be used for biological discovery. The interface allows for easy organization of steps and data for easy documentation of modeling steps. Using KBase tools we found that endosphere (internal root compartment) have significantly different metabolic properties than rhizosphere isolates (external root compartment) that is consistent with what is known about these compartments. This work helps us rapidly characterize new bacterial isolates and will help identify processes important for plant-microbe interactions. We are preparing this work for publication in a paper describing phenotypic variation between endosphere and rhizosphere bacterial isolates.
Matt Scarborough — Great Lakes Bioenergy Research Center
My research interests include constructing and using metabolicmodels of numerous microbial species. As part of this research, our lab at the Great Lakes Bioenergy Research Center developed a metabolic and regulatory model of Rhodobacter sphaeroides 2.4.1. We worked with KBase collaborators at Argonne National Laboratory to load this model, iRsp1140, into the KBase system. Since that time, iRsp1140 has become a central example in KBase. Furthermore, the publications created with this model have been classified as “highly accessed,” a designation reserved for only a few papers and one that demonstrates the scientific community’s interest in this research.
Since then, I have used KBase to propagate our published Rhodobacter iRsp1140 model to new genomes, dramatically speeding the rate at which I’m able to produce new high-quality models. I’ve extensively used KBase’s metabolic modeling, genome annotation, and proteome comparison tools to help determine how other purple non-sulfur bacteria species vary from the R. sphaeroides 2.4.1 strain.
Others in my group and university are using KBase to annotate and build metabolic models for various microbes, including novel species involved in nitrogen cycling in the environment. We also hope to use KBase to construct metabolic models of multi-organism systems to better understand syntrophic interactions among microorganisms.
Steve Lindemann — Pacific Northwest National Laboratory / Purdue
We have used KBase extensively in our work on the Principles of Microbial Community Design Science Focus Area. We applied KBase to annotate and construct genome-scale metabolic models for the 22 species comprising our unicyanobacterial consortia. We manually reviewed many of these annotations, finding good agreement with our own annotations performed outside of KBase. This improved our confidence in using the KBase tools. We then applied KBase to place our genomes into a phylogenetic context and compare our closely related species in detail to identify subtle differences between strains. This provided a valuable new perspective to versions of this analysis that we performed outside of KBase. Additionally, we applied the phenotype simulation tool in KBase to predict numerous growth conditions for each of our species, which we used to identify media formulations that might enrich for specific species that we wish to isolate. This was a capability that was unique to KBase, and it provided valuable support for new experiment design. We plan to expand this approach to use KBase to study interactions between pairs of species.
We are now working with the KBase to construct a community metabolic model of our Hot Lake microbial mat, integrating transcriptomic and metabolomic data using new prototype tools developed by the science engagement team of KBase. Overall, we have been impressed at how easy the Narrative Interface is to use. Members of our team who are not computational biologists, such as myself, were able to perform complex workflows using the Narrative interface. The Narrative is also valuable as a collaborative tool, as it shows every detail of the analyses performed by our collaborators using our data This makes it possible for us to not only understand the analysis, but also to tweak it to explore new scientific questions.