The Jsbml Project: A Fully Featured Java API for Working with Systems Biological Models

With continued rapid growth in the number and quality of fully sequenced and accurately annotated bacterial genomes, we have an unprecedented opportunity to understand metabolic diversity. In previous work, we selected 101 diverse and representative completely sequenced bacteria and implemented a manual curation effort to identify 846 unique metabolic variants present in these bacteria. The presence or absence of these variants act as a metabolic signature for each of the bacteria, which can then be used to cluster them into a metabolic tree and analyze similarities and differences between and across bacterial groups. In our current work, we are developing an application to automate the assignment of metabolic variants to bacterial genomes in the Department of Energy’s Systems Biology Knowledgebase (KBase). This will enable us to expand the set of bacteria to which this approach is applied and use the resulting tree to test broad questions about metabolic diversity and complexity across the bacterial tree of life.

This work is funded by the National Science Foundation awards MCB-1716285 to Hope College and MCB-1715211 to Dordt College.

References:

Nathaniel Bowerman, Nathan Tintle, Matthew DeJongh, Aaron A Best. Identification and Analysis of Bacterial Genomic Metabolic Signatures. Pacific Symposium on Biocomputing 2017, pp. 3-14. 2017.

AP Arkin, RW Cottingham, CS Henry et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology, Vol 36, pp. 566-569, 2018.