[ad_1]
Information about how a small group of bacteria interact and influence one another could lay the groundwork for understanding how human microbiomes change — and, eventually, for engineering probiotic solutions for sickness.
New research from the University of Virginia’s Department of Biomedical Engineering has produced a computational model for how six species of bacteria interact and create an ecosystem. By identifying rules of behavior and translating findings into parameters for the model, the researchers distilled a complex system into a model and discovered new behaviors of a bacterium.
“Most of the previous research has focused on if the specific bacteria is there or if it isn’t there, you’re either sick or you’re healthy,” said Jason Papin, a professor of biomedical engineering who helped to lead the project. “There hasn’t been much understanding of the mechanisms of that. So, as all of that data was starting to come out, our lab started thinking about how to bridge the computer modeling expertise that we had and look instead at complex communities of bacteria.”
Greg Medlock, a doctoral student in Papin’s lab, began sifting through possible bacterial systems and decided to focus on a group critical to supporting the immune systems of mice. Many of the bacteria are similar or identical to those found in the human gut microbiome.
Medlock looked at the interactions among 15 pairs of bacteria and tried to identify the metabolites — small proteins — each pair of organisms produced and shared as they grew.
“Then we actually had all of the data measuring the metabolites to start to put them in different categories and say, this bacteria is producing this metabolite, and that bacteria is eating the metabolite,” Papin said. “We could see some of that data coming out and see there are some really complex relationships here that we’re going to be able to understand a little better.”
Modeling helped researchers to identify additional variables that were really important, Papin said, and be more confident in relationships between data. Seeing a relationship about a specific metabolite also helped researchers to identify a new biological feature of a particular bacteria.
“It didn’t appear that this specific metabolite should have this effect,” Medlock said. “But we realized that both of these metabolites being present allowed something to happen that hadn’t been identified before.”
Papin teaches a class on computational modeling to first-year graduate students; he tries to impress on them that failures and gaps in a model often help lead to discoveries.
“Lots of times, people think computational models are most valuable when predictions you make turn out to be true,” he said. “But they’re actually more valuable when you’re wrong, because it helps you realize that you don’t understand something.”
Researchers say next steps are to conduct trials on mice and seeing how the community of bacteria responds to particular inputs such as sugar, or a virus. Eventually, they hope to create more and more complex models for both people who are healthy and those with particular diseases. Such information could help engineer medicine or diets to maintain and improve health.
“In the end, you want to have a model that shows how your microbiome affects your health,” Papin said. “The only way to do that is if we can explore this process.”
[ad_2]
Source link
