National Institute for Mathematical and Biological Synthesis News

NIMBioS Study Finds Biological Fitness Trumps Other Traits in Mating Game

The brighter the colors, the more popular the butterfly will be with the females. A new study from the National Institute for Mathematical and Biological Synthesis finds that a female’s mating decisions are largely based on traits that reflect fitness or those that help males perform well under the local ecological conditions.

Three Students Selected for Summer Research Experience at National Institute

Three UT students have been selected for the highly competitive Research Experience for Undergraduates program currently underway at the National Institute for Mathematical and Biological Synthesis taking place on campus. Samuel Estes, Brittany Hale, and Jacob Lambert, are among nineteen students from acrross the country participating in the eight-week, research-intensive program.

NIMBioS Study Analyzes Animal Social Networks

A new study finds that animals use the same level of sophistication as humans in judging social configurations. The National Institute for Mathematical and Biological Synthesis study brings a new understanding of the structure of animal social networks. The researchers analyzed the relationships between three individuals by analyzing longstanding behavioral data.

NIMBioS Study Puts Supreme Court Under the Microscope

The current Supreme Court may be criticized for its lack of diversity on the bench, but according to a study conducted by UT law professor Ben Barton, the Court is actually more diverse overall today than ever in history. The study, published in the Journal of Empirical Legal Studies, borrows statistical methods from ecology to reveal a more precise picture of diversity.

NIMBioS Study: Avoiding a Cartography Catastrophe

Today’s global mapping of infectious diseases is considerably unreliable and may do little to inform the control of potential outbreaks, according to a study produced at a NIMBioS workshop held on UT’s campus. Social media could help. Using crowdsourcing techniques to gather data, such as analyzing the content and frequency of Twitter messages about disease, predicted outbreaks sooner than traditional disease surveillance methods.