Metabolic networks are mathematical models of every possible sequence of chemical reactions available to an organ or organism, and they’re used to design microbes for manufacturing processes or to study disease. Based on both genetic analysis and empirical study, they can take years to assemble.
Unfortunately, a new analytic tool developed at MIT suggests that many of those models may be wrong. Fortunately, the same tool may make it fairly straightforward to repair them.
“They have all these models in this database at [the University of California at] San Diego,” says Bonnie Berger, a principal investigator at CSAIL and one of the tool’s developers, “and it turns out that many of them were computed with floating-point arithmetic” — an approximate numerical representation that most computer systems use to increase efficiency.
“We were able to prove that you need to compute them in exact arithmetic,” Berger says. “When we computed them in exact arithmetic, we found that many of the models that were believed to be realistic don’t produce any growth under any circumstances.”
Berger and colleagues describe their new tool, and the analyses they performed with it, in the latest issue of Nature Communications. First author on the paper is Leonid Chindelevitch, who was a graduate student in Berger’s group when the work was done and is now a postdoc at the Harvard School of Public Health. He and Berger are joined by Aviv Regev, an associate professor of biology at MIT, and Jason Trigg, another of Berger’s former students.
Read more at MIT News: http://bit.ly/1CVcQlL