Case study: SRI enhances a bioinformatics firm’s mining industry offerings
Koonkie, a Canada-based bioinformatics venture, leveraged SRI’s BioCyc platform to bring new capabilities to mining industry clients.
“Since most mine microbes aren’t known, we need to build detailed maps of their DNA to understand how they operate. Building pathway genome databases helps us understand the genetic blueprints of these tiny miners so that we can use them to solve industry-wide challenges related to critical mineral yield, tailings stabilization, and more.”
Erin Marshall, Chief Operating Officer, Koonkie
CHALLENGE
Mining industry leaders now recognize that a mine site’s microbial population is critical to mine management and remediation. Microbes can produce dangerous levels of chemicals like methane and arsenic but can also promote mineral recovery. Koonkie, a bioinformatics firm based in Vancouver, BC, sought to provide mining companies with unique insights about how microbes can remediate mine sites and make mineral extraction more efficient.
SOLUTION
To better understand the microbial populations at mine sites, Koonkie turned to SRI’s BioCyc platform. Most of the microbes that Koonkie discovers at mine sites are unknown to science. By analyzing these newly discovered microbes using the BioCyc platform, and particularly its Pathway Tools software, Koonkie’s scientists can learn how the metabolic pathways of these tiny organisms might contribute to efficient mineral extraction or toxic outputs. Based on these findings, mining companies can then build more comprehensive environmental management plans.
IMPACT
Working at a decommissioned gold mine, the Koonkie team leveraged BioCyc to better understand a newly discovered species of archaea. The team sequenced and annotated the organism’s genome and quickly detected a gene that is often utilized for methane formation. To determine whether or not the methane-producing gene resulted in the production of methane in this new species, the team leveraged BioCyc to computationally generate a pathway genome database for the organism. This database allowed Koonkie to model the microbe’s metabolic inputs and outputs based on how the sequenced genes interacted with each other. The results indicated that methane was not a predicted output of this particular organism’s metabolism. Additionally, the pathway genome database predicted two pathways for arsenic detoxification, including one that converts arsenic from a highly toxic form to a more tolerable one — a function that might be implemented in the future to bolster reclamation efforts.
OUR EXPERTS
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Peter Karp
Technical Director, Artificial Intelligence Center