Similar patterns in genes, brains, feeding

Oct. 24, 2002 at 5:55 PM
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REHOVAT, Israel, Oct. 24 (UPI) -- An international team of scientists said Thursday they have used a mathematical algorithm to detect recurring patterns in the networks making up everything from food webs to the Internet to gene regulation in cells.

By uncovering these crucial building blocks of networks, researchers have taken an important step toward unraveling the bewildering complexity of these systems, which they term "motifs."

"Understanding a motif's function may open the way to new ways of dealing with diseases for which there is no cure at present, diseases that are complex network-level malfunctions," researcher Uri Alon, a physicist at the Weizmann Institute of Science, told United Press International.

The research is based on the assumption that information and energy tend to flow in distinct networks. Certain plants and animals are eaten by specific lifeforms in food webs, for instance.

"21st-century sciences are obsessed with networks. The big problem is how to break down these complex networks into parts we can understand," Alon said from Belgium.

For instance, although scientists have mapped out where every human gene is, they do not yet fully understand how these thousands of genes interact, said physicist Albert-Laszlo Barabasi of the University of Notre Dame in Indiana. "In order to cure some of the major diseases such as cancer or depression, where several genes are functioning simultaneously, you need to understand the networks of the cells," he explained.

Similar issues apply to the Internet, Barabasi added. "It's a huge information depository that has a very complex network structure. The same questions we ask of cells could help on the World Wide Web."

Using the algorithm, Alon and colleagues have developed a new experimental technique that maps out the wiring diagrams of these networks.

"We start with a network -- a list of elements and their connections," he said. "We then count how many times different patterns appear in this network. To understand which of the many patterns that occur are significant and potentially important, we compare the network to a large set of randomized networks. These are networks ... made of the same elements but rewired so that the connections are scrambled. In each of the randomized networks we again count the number of appearances of the different patterns."

After a while, the computer program reveals some patterns occur much more often than they would at random. "These are likely to be patterns that are 'designed in' or 'highly selected for' by evolution," Alon said.

As the researchers report in the Oct. 25 issue of the journal Science, very different networks can sometimes demonstrate the same motifs.

"For example, some of the most significant circuitry patterns made of biomolecules within a bacterium also occur on the connections between neurons in a worm," Alon said. "Both networks process information but one uses gene circuits and the other neurons."

Separate research also appearing in Science, from Rick Young of the Whitehead Institute for Biomedical Research in Cambridge, Mass., and colleagues, combined computer science with biological science techniques to map the complex network of gene regulation -- the process of turning specific genes on or off -- in yeast.

Young told UPI his team developed advanced high-throughput biological and computing technologies that allowed them "to watch the behavior of tens of thousands of (genes) simultaneously," he said. Prior to this, it was a major undertaking just to watch the regulation of one gene and scientists had been able to figure out the regulation process of a only few dozen genes, he said.

Now they have a good grasp of how nearly all the genes in a yeast cell are regulated, Young said. There are hundreds of different transcription factors that regulate how genes are turned on and off. By using techniques that allowed them to observe which genes were active, and by combining that data with other information in a computational analysis, Young's team was able to determine which transcription factors turn on which genes in yeast, he said.

Yeast cells operates essentially the same as human cells, he said, so the findings could open the door for developing new treatments for disease. It also could lead to entirely new ways of thinking about disease.

Biologists traditionally have been taught to think about one function at a time but the network studies are allowing them -- indeed, forcing them -- to think about multiple functions simultaneously, he said. The National Institutes of Health in Bethesda, Md., recognizes the value of such an approach and is encouraging its funded researchers to develop projects that utilize this network type of approach because many researchers think it will "accelerate discovery in really unanticipated ways," Young said.

Barabasi agreed the findings had implications for human diseases. "The method is not specific to yeast," he said. "One can take the same experimental method and apply it to humans. That will probably be done in the near future. It's the obvious goal down the line to help design better drugs, for example."

Young said his team currently is using its discoveries about gene regulation to gain a better understanding of how the process goes awry in cancer cells.

By discovering what motifs are crucial to network operations, Alon said scientists could figure out how best to intervene. "In ecology, these might perhaps represent key predator-prey organizations that should not be harmed to avoid extinction," he suggested.

The challenge now is finding out what these motifs mean and what they do, Barabasi said. "It's a very important step in understanding networks, but not something that right away is going to give you an Internet startup company."


(Reported by Charles Choi, UPI Science News, in New York and Steve Mitchell, UPI Medical Correspondent, in Washington)

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