The researchers, including first author Ishanu Chattopadhyay, a Cornell postdoctoral associate, teamed with Anna Kuchina and Gurol Suel, molecular biologists at University of California, San Diego, to test their algorithms using real data. In one experiment, they applied the algorithm to a set of gene expression measurements of a model bacterium B. subtilis.
They gleaned similar insights by studying the fluctuating numbers of micro-organisms in a closed ecosystem; the algorithm came up with reactions that correctly identified the predators, the prey and the dynamical rules that defined their interactions.
Their key insight was to look at relative changes of the concentration of the interacting agents, irrespective of the time at which such changes were observed. This collective set of relative population updates has some important mathematical properties, which could be related back to the hidden reactions driving the system.
"We figured out that there's what's called an invariant geometry, a geometrical feature of the data set that you can uncover even from sparse intermittent samples, without knowing any of the underlying rules," Chattopadhyay said. "The geometry is a function of the rules, and once you find that out, there is a way to find out what the reactions are."
The bigger picture in this study is to give scientists better tools for taking massive amounts of data and coming up with simple, insightful explanations, Lipson said.
"This is a tool in a suite of emerging 'automated science' tools researchers can use if they have data from some experiment, and they want the computer to help them understand what's going on but in the end, it's the scientist who has to give meaning to these models," Lipson said.
|Contact: Syl Kacapyr|