A forest full of rabbits and foxes, a bubbling vat of chemical reactants, and complex biochemical circuitry within a cell are, to a computer, similar systems: Many scenarios can play out depending on a fixed set of rules and individual interactions that can't be precisely predicted chemicals combining, genes triggering cascades of chemical pathways, or rabbits multiplying or getting eaten.
Predicting possible outcomes from a set of rules that contain uncertain factors is often done using what's called stochastic prediction. What has eluded scientists for decades is doing the reverse: To find out what the rules were, simply by observing the outcomes.
Researchers led by Hod Lipson, associate professor of mechanical and aerospace engineering and of computing and information science at Cornell University, have published new insight into automated stochastic inference that could help unravel the hidden laws in fields as diverse as molecular biology to population ecology to basic chemistry.
Their study, published online July 22 in Proceedings of the National Academy of Sciences, describes a new computer algorithm that allows machines to infer stochastic reaction models without human intervention, and without any previous knowledge on the nature of the system being modeled.
With their algorithm, Lipson and colleagues have devised a way to take intermittent samples for example, the number of prey and predating species in a forest once a year, or the concentration of different species in a chemical bath once an hour and infer the likely reactions that led to that result. They're working backward from traditional stochastic modeling, which typically uses known reactions to simulate possible outcomes. Here, they're taking outcomes and coming up with reactions, which is much trickier, they say.
"This could be very useful if you wanted to learn the driving rules for not just foxes and rabbits, but any evolving system wi
|Contact: Syl Kacapyr|