Signals from the environment set off a cascade of changes that affect different genes in different ways. Therefore, traditionally it has been difficult to study how such signals influence an organism. In a new study, researchers have developed a machine learning approach called FUN-PROSE to predict how genes react to different environmental conditions.

Microbial communities often contain several species that coexist even though they share similar metabolic abilities. How they do so is unclear. Researchers have now developed a model to show that if these species have complementary preferences for what they consume, they can more easily coexist.

The human gut consists of a complex community of microbes that consume and secrete hundreds of small molecules—a phenomenon called cross-feeding. However, it is challenging to study these processes experimentally. A new study, published in Nature Communications, uses models to predict cross-feeding interactions between microbial species in the gut. Predictions from such computational methods could eventually help doctors get a more complete understanding of gut health.

Microbial communities can be found everywhere – from lakes to the soil on the ground, they are omnipresent yet invisible to the naked eye. Within those environments there exist dynamic communities which fluctuate in response to environmental changes. One such example is the human gut microbiome, which is comprised of microbes that influence the overall landscape of the gut.

A popular economic concept is helping IGB researchers understand how microbial communities operate.

Microbial communities are in our bodies, in the soil, in forests and oceans, and more. They’re made up of microorganisms that interact with each other in various ways, and these interactions can affect the surrounding environment.

Researchers like Sergei Maslov, a Bliss Faculty Scholar and professor of bioengineering in the Biocomplexity theme, want to understand microbial communities so they can learn how to manipulate them.