Bigger Biology Tackled Through Keyboards

January 17, 1997

Solving crucial biological problems these days can involve trying to figure out huge arrays of individual activities on vastly different scales and timelines, from the rapid genetic mutations of certain viruses to the long-term impact of species extinctions on global ecological processes.

Fortunately, a relatively new field of science known as computational biology is coming of age, according to a review paper in the Jan. 17 issue of the weekly journal Science by researchers from the University of California, Davis, and other universities.

"Important biological issues can and need to be understood through computational biology," says co-author Alan Hastings, chair of the UC Davis environmental studies division. "Dynamic models are an essential tool for understanding complex biological systems."

Written by four of the most noted scientists in the field, the paper reviews the role of powerful computational tools in studying ecology, genetics and evolution, infectious diseases, and immunology and virology.

"Math and computation are playing greater and greater roles in addressing contemporary biological problems," says Simon Levin, director of the Princeton University Environmental Institute and first author of the paper. "There's been continuous progress over the last 25 years; but, like doing a crossword puzzle, it's really hard to make progress in the beginning and then at a certain point it goes very fast."

Computational biology is at that point, the authors agree. Dazzling new high-speed computational technology, enormous and growing sets of data, and more sophisticated approaches to problem solving are now converging.

Promising advances in computational techniques allow researchers to begin to account for the fact that neighboring individuals might behave differently toward each other than distant individuals -- whether these are trees competing for light or individuals spreading disease.

For example, the evolution of altruism has long intrigued biologists. Why would an organism intent on spreading its genes on to the next generation risk anything by helping another organism. Well, computational work has revealed that individuals are more likely to help neighbors than random strangers they expect never to see again and that this can help explain how altruism has evolved.

"It's a strategy of, if you scratch my back, I'll scratch yours," Hastings said. Different strategies for interacting (cooperating for mutual gain or competing for advantage) can be put into mathematical terms and run through a computer to determine the results of each strategy over time.

Computational biologists begin to understand these kinds of problems by building a mathematical model. Like an architect's blueprint, such models aim to capture only the essential elements of a complicated phenomenon in a form that can be manipulated, investigated and used as an experimental tool itself.

"The art of modeling is knowing what to leave out," said Hastings, who recently helped bring to campus a research training grant in nonlinear dynamics in biology from the National Science Foundation. UC Davis is considered among the world's leading campuses in quantitatively oriented biology.

Some of the general challenges faced by computational biologists include dealing with phenomena on different scales of time, space and organizational complexity. "If you put a pot on the stove, you just want to know when it will boil. You don't care about every molecule inside," Levin said. "But if you want to build physical theory, you may begin at the scale of molecules colliding."

So it is with biological modeling in epidemiology, ecology and immunology. Scientists want to understand the behavior of the entire system, which is mediated by things happening on smaller and larger scales.

When using models, it's important to understand their limits, Levin said. Day-to-day weather forecasts may not be possible, but long-term averages produce more reliable predictions. Land managers using models may need to develop adaptive management strategies to monitor the system and change management in response to changes in the ecological system.

Other co-authors of the paper are Bryan Grenfell, in the zoology department of Cambridge University in England, noted for his work on measles and other diseases; and Alan Perelson, in theoretical biology and biophysics at Los Alamos National Laboratory in New Mexico, distinguished for his work in immunology and for recent theoretical work fundamental in developing new treatments for AIDS.


University of California - Davis

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