Department of Biology

Honey bees provide a valuable service to plants and humans, pollinating more than 30% of the world’s crops. As you’ve likely heard, multiple factors—including industrial agriculture, bee-killing pesticides, infection with pathogens, and climate change—have placed the health of honey bee populations in jeopardy, potentially compromising the world’s food supply. Dr. Eric Smith, a postdoctoral fellow in Associate Professor Irene Newton's lab in Indiana University’s Department of Biology, is invested in revitalizing honey bee populations by studying how their microbiomes support and influence bee function.

Though we spend countless hours cleansing our bodies and homes of bacteria, viruses, and other microbes, their presence can also benefit their host organisms. Scientists have known for some time that almost all living organisms have symbiotic microbes that live within and on them and are beneficial to them. For instance, they recognize the importance of microbes for digestion and nutrient absorption, but they’re only beginning to understand the many other ways microbes aid their hosts. As there is still much that science doesn’t understand about these processes, Smith’s work focuses broadly on how microbes impact the biology of the hosts they live in.

To do this work, Smith uses IU’s large memory computer cluster Carbonate, which is configured to support high-performance, data-intensive computing. With Carbonate, he can write and run computational programs to analyze DNA sequences. Smith generates immense amounts of data, which requires an enormous amount of disk space and computational power to analyze it.

For Smith, Carbonate provides “the computational infrastructure necessary to perform large amounts of data simulation to test computational pipelines and eventually to analyze DNA sequencing data.” He praises the cluster’s flexibility and power, and the way it facilitates data sharing. Smith notes that “as long as collaborators each have accounts on Carbonate, it is very easy to be working on the same datasets from slightly different angles and provide complementary analyses.”