For a century, evolutionary biologists have probed how genes encode an individual’s chances for success or fitness in a specific environment.
To reveal a possible evolutionary trajectory biologist, measure the interactions between genes to see which mixtures are most fit. An organism that’s evolving ought to take the rightest path. This idea is named a fitness landscape, and numerous mathematical methods have been developed to explain it.
How does the colony of microbes living in our gastrointestinal systems affect our health a question? Carnegie’s Will Ludington was a part of a team that helped answer this question.
Just like the genes in a genome, microorganisms within the gut microbiome interact, but there is not a broadly accepted mathematical framework to map the patterns of those interactions. Existing structures for genes deal with local information about interactions; however, don’t put together a global picture.
Joswig and Ludington then joined with Holger Eble of TU Berlin, a graduate student working with Joswig, and Lisa Lamberti of ETH Zurich. Lamberti had previously collaborated with Ludington to apply a slightly different mathematical framework for the interactions to microbiome data. Within the present work, the group expanded upon that earlier framework to provide another global picture by mapping the patterns of interactions onto a landscape.
However, the sheer diversity of species within the human microbiome makes it very difficult to elucidate how these communities affect our physiology. That is why the fruit fly makes such a superb model. Unlike the human microbiome, it consists of solely a handful of bacterial species.
The authors observe that the framework applies equally well to traditional genetic interactions. Their work is published within the Journal of Mathematical Biology.