NSF PR 03-35 - March 27, 2003
From Biology to Computing and Back Again
NSF workshop links advanced mathematical methods to cells, organs, organisms
ARLINGTON, Va. - For decades, computer scientists have used mathematical models inspired by biological processes - such as "genetic algorithms" and "cellular automata" - to address complex scientific problems.
As Melanie Mitchell, an expert on such adaptive computation, puts it: "Biological evolution is an appealing source of inspiration for addressing these problems. Evolution is, in effect, a method of searching among an enormous number of possibilities for 'solutions.'"
In borrowing the notion, the computational methods even take analogous terms - like "chromosome" (for a candidate solution), "gene" (for the encoding bit or bits that yield a part of the solution), and "mutation" (the deliberately random changing of a piece in the processing to see potential variations in a puzzle's ultimate assembly).
As computing benefits from mimicking the way biology works, mathematics and computational algorithms are being applied in innovative ways to advance understanding of biology. The field of bioinformatics, for example, uses high-level mathematics to analyze the chemical structure and functions of genes.
However, much of medium-scale biology has, by and large, missed the benefits of these and other algorithms. That could begin to change with "The Roles of Mathematics and Computation in Systems and Integrative Biology," a workshop being held today and tomorrow, March 27-28, at Utah State University sponsored by the National Science Foundation (NSF).
At the Logan campus, Mitchell and 20 other biologists, mathematicians and engineers are exploring how such computational innovations can lead to a greater understanding of how the components of life interact at levels larger than chromosomes and smaller than populations - or generally in the context of cells, organs and organisms.
The scientists, many of them researchers supported by NSF, are examining the bio-derived methods and other mathematical and computational tools not widely used in systems biology. They will try to determine which ones hold the most promise, what obstacles exist, and how to promote their broader use by biologists.
For example, Roger Nisbet, a biologist at the University of California-Santa Barbara, is showcasing a mathematical model he uses to track energy flow through an individual organism - from its acquisition to its use in growth, reproduction, and survival. Such models of "dynamic energy budgets," Nisbet says, can also improve understanding of energy requirements of populations. The approach may also be used to help scientists predict the consequences of climate change.
Another example comes from the study of the heavens. In his earlier career as an astrophysicist, Niels Otani, now a biomedical engineer, became familiar with eigenmode analysis, a standard method in astro- and geophysics, but generally unknown among biologists. It has been applied to studies of the surface of the sun, earthquakes, hurricanes and condensed-matter physics. Did Otani ever consider using it in his new realm?
In a heartbeat.
Otani and his research team at Case Western Reserve University in Cleveland are combining the eigenmode approach with other simulation techniques to track more closely the chemical dynamics that determine cardiac rhythms. According to Otani, "This has enabled us to devise novel methods which may be effective in eliminating undesirable components in abnormal cardiac rhythms."
Other sessions focus on how computational tools can advance research into the biochemistry of evolution, the causes of asthma, the prediction of epileptic seizures, the uptake of carbon dioxide by forests, and, in vertebrates, the ability of cells to "self-organize" into tissues and the mechanisms that create limbs. The participants are also examining efforts to add mathematical-modeling exercises into undergraduate biology labs.
Eugene Bruce, who directs integrative activities in biology at NSF, said, "There's been much interest in recent years applying mathematics and computing to deciphering gene sequences and to managing the massive amounts of data in them. The other areas of biology, though, have been slower to incorporate these methods. These people are doing that - using math tools to see how cells function, to see how tissues grow, to look at an organism and see how it can satisfy its metabolic requirements.
"We want to know what are the driving issues at this level of biology that can benefit from these approaches," Bruce said. "That's why we're excited about biologists and mathematicians coming together at this workshop."
Details about the workshop are at this web site: www.math.usu.edu/~powell/workshop03.
Workshop coordinator: James Powell, Associate Professor, Department of Mathematics and Statistics, Utah State University, (435) 797-1953, firstname.lastname@example.org.
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