Scientists Successfully Predict Evolution of E. Coli Bacteria

From: Jack Haas (
Date: Thu Dec 05 2002 - 09:47:03 EST

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    I would appreciate the comments of the more biologically informed members=
    the list of the following NSF press release.=0D
    Jack Haas=0D
      NSF Press Release=0D
    NSF PR 02-92 - November 14, 2002 =0D
    Media contact: Josh Chamot (703) 292-8070 =0D
    Program contact: Fred Heineken (703) 292-7944
    Using Computers, Scientists Successfully Predict Evolution of E. Coli
    For more than a decade, researchers have been trying to create accurate
    computer models of Escherichia coli (E. coli), a bacterium that makes
    headlines for its varied roles in food poisoning, drug manufacture and
    biological research.=0D
    By combining laboratory data with recently completed genetic databases,
    researchers can craft digital colonies of organisms that mimic, and even
    predict, some behaviors of living cells to an accuracy of about 75 percen=
    t. =0D
    Now, NSF-supported researchers at the University of California at San Die=
    have created a computer model that accurately predicts how E. coli metabo=
    systems adapt and evolve when the bacteria are placed under environmental
    constraints. Bernhard Palsson, Rafael Ibarra (now at GenVault Corporation=
    Carlsbad, California) and Jeremy Edwards (now at the University of Delawa=
    at Newark) report their findings in the November 14 issue of Nature.=0D
    "Ours is the only existing genome-scale model of E. coli," says Palsson. =
    addition, while many approaches to genetics experiments "knock out"
    individual genes and track the results, the new model takes a whole-syste=
    approach. Changing one aspect of a genetic code could be irrelevant if an
    organism adapts and evolves, says Palsson. The constraints-based models
    allow the E. coli to evolve more naturally along several possible paths.=0D
    Scientists may use the approach to design new bacterial strains on the
    computer by controlling environmental parameters and predicting how
    microorganisms adapt over time. Then, by recreating the environment in a
    laboratory, researchers may be able to coax living bacteria into evolving
    into the new strain.=0D
    The resulting strains may be more efficient at producing insulin or
    cancer-fighting drugs than existing bacterial colonies engineered by
    researchers using standard techniques. =0D
    "Now we have a better tool to predict how bacteria evolve and adapt to
    changes," says National Science Foundation program director Fred Heineken=
    As a result, this constraints-based approach could lead to better
    custom-built organisms," he says.=0D
    The researchers based their digital bacteria on earlier laboratory studie=
    and E. coli genome sequences, detailed genetic codes that have been
    augmented with experimental information about the function of every gene.=
    Such digital models are known as "in silico" experiments -- a play on wor=
    referring to biological studies conducted on a computer.=0D
    In the first days of testing on living organisms, the bacteria did not ad=
    into the strain predicted by the simulation. Yet, with more time (40 days=
    or 500-1000 generations), the E. coli growing in the laboratory flasks
    adapted and evolved into a strain like the one the in silico model predic=
    "The novelty of the constraints-based approach is that it accounts for
    changes in cellular properties over time," says Palsson. "Fortunately," h=
    adds, "the other advantage is that it actually works surprisingly often."=
    For many years, drug manufacturers have manipulated the genetic code in E=
    coli strains, creating species that can produce important substances, suc=
    as the hormone insulin for use by people with diabetes or the experimenta=
    cancer drug angiostatin.=0D
    Using the new constraints-based techniques Palsson and his colleagues
    developed, drug manufacturers and bioprocessing companies could use
    computers to determine the genetic code that could yield the most efficie=
    and productive versions of E. coli, and then use adaptive evolution to
    create bacterial strains that have the desired properties.=0D
    Says Palsson, "This development potentially opens up a revolutionary new
    direction in the design of new production strains." In addition, says
    Palsson, "now that we have gained a greater understanding of this process=
    E. coli, developing similar simulations of other organisms should take le=
    time." =0D
    Photomicrograph of E. coli.=0D
    Photo Credit: Image courtesy of National Institute of Allergy and Infecti=
    Diseases, National Institutes of Health=0D
    The two images show how E. coli bacteria in a laboratory evolved over tim=
    to metabolize and grow at a rate predicted by a computer simulation. The
    bottom left axis represents the amount of oxygen that the bacteria consum=
    the bottom right axis represents the amount of glycerol (the food) the
    bacteria consumed, and the vertical axis represents the rate at which the
    bacteria grew. =0D
    The colored surfaces are called "phenotype phase planes." They graphicall=
    represent the researchers' in silico (computer) prediction for the possib=
    ways in which the bacteria could grow under specific environmental
    conditions. The red shading represents faster growth conditions, the gree=
    represents slower growth.=0D
    The red line is the "line of optimality," the optimal growth rate predict=
    by the researchers' computer model. Region 1 represents a phase where gro=
    is not optimal, Region 2 represents a phase where the bacteria consume to=
    much food and therefore have to secrete some as byproducts of metabolism.=
    The white dots are measurements of how several E. coli specimens were
    metabolizing food, and how fast they were growing when the researchers
    tested them. After 40 days (700 generations), the bacteria evolved to
    metabolize as predicted by the researchers' in silico model (all specimen=
    cluster along the line of optimality). =0D
    Select image for a larger version.=0D
    Photo Credit: Images courtesy of the Genetic Circuits Research Group (Raf=
    U. Ibarra, Jeremy S. Edwards, and Bernhard O. Palsson). =0D
    Select images for larger versions =0D
    Larger versions (Total Size: 128KB) of images from this document=0D
      Note About Images=0D

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