For
nearly a century, evolutionary biologists have probed how genes encode an
individual's chances for success -- or fitness -- in a specific environment.
In
order to reveal a potential evolutionary trajectory biologists measure the
interactions between genes to see which combinations are most fit. An organism
that is evolving should take the most fit path. This concept is
called a fitness landscape, and various mathematical techniques have been
developed to describe it.
Like
the genes in a genome, microorganisms in the gut microbiome interact, yet there
isn't a widely accepted mathematical framework to map the patterns of these
interactions. Existing frameworks for genes focus on local information about
interactions but do not put together a global picture.
So,
Ludington began talking to mathematician Michael Joswig of the Technical
University in Berlin.
"Michael
thinks natively in high dimensions -- many more than four. He understood the
problem right away," said Ludington.
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. In the present work, the
team expanded upon that previous framework to produce a more global picture by
mapping the patterns of interactions onto a landscape.
But
the sheer diversity of species in the human microbiome makes it very difficult
to elucidate how these communities influence our physiology. This is why the
fruit fly makes such an excellent model. Unlike the human microbiome, it
consists of only a handful of bacterial species.
"We've
built a rigorous mathematical framework that describes the ecology of a
microbiome coupled to its host. What is unique about this approach is that it
allows a global view of a microbiome-host interaction landscape," said
Ludington. "We can now use this approach to compare different landscapes,
which will let us ask why diverse microbiomes are associated with similar
health outcomes."
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See:
Posted by Dr. Tim Sandle, Pharmaceutical Microbiology
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