ARLINGTON, Va., Nov. 26, 2002 – Biomedical engineers have used a computer model to predict how one strain of a common bacterium will evolve over hundreds of generations.

Bernhard Palsson, Ph.D., of the University of California, San Diego (UCSD), used the model to determine how well the bacterium would adapt to a specific change in its environment.

The research could be an early step toward predicting when a drug-resistant strain of bacteria will emerge and how to combat it.

Drug resistance is a growing problem. A number of microbes, including tuberculosis bacteria, have evolved to resist modern antibiotics.

Palsson has modeled Escherichia coli, a usually harmless and well-studied bacterium that lives in the intestines. Some strains cause illness as a food contaminant.

His model, described in the Nov. 14 issue of the journal Nature, uses mathematics and computer simulations to show how genes and the proteins they produce interact to control the function of living cells. Thousands of different combinations are possible, making cause-and-effect relationships difficult to decipher by other means.

The model is being developed over time and is now about 80 percent accurate in foretelling the evolutionary impact of a change in the bacteria's environment.

"The adaptive evolutionary path itself cannot be predicted; however, the final outcome can be," wrote Palsson and his colleagues, Rafael Ibarra of UCSD and Jeremy Edwards of the University of Delaware.

Their experiments followed the observation that a strain of E coli was not growing very well on glycerol, which aids in energy metabolism.

Palsson thought this might be the first time that particular strain had been exposed to glycerol. He further speculated that given time to evolve, the bacteria would achieve an optimal growth rate with the new metabolite. He then used the computer model to accurately predict what that growth rate would be.

The research group tested the theory by allowing the bacteria to grow on glycerol for about two months, during which time the E coli population went through 800 generations of natural selection. The organisms that grew well survived and flourished, while those that fared poorly in the test environment died off.

The group conducted similar experiments with four other metabolites and accurately predicted the growth outcomes.

These results suggest the possibility of computer-aided design and testing of microorganisms to improve their metabolic activity before actually growing them.

The goal of combating drug-resistant bacteria will take time. A more immediate application might be in better drug design and improved commodities, such as detergents.

Palsson has also created computer models of metabolism for red blood cells, yeast, and organisms that cause influenza and stomach ulcers.

Under a grant from The Whitaker Foundation, Palsson and Whitaker investigator Sangeeta Bhatia, M.D., Ph.D., of UCSD are completing a textbook on tissue engineering.

Contact:
Bernhard Palsson, UCSD
Frank Blanchard, The Whitaker Foundation