Phillips and his colleagues wanted to create a more realistic model by adding in this competition. To do so, they looked at how the level of gene expression varies depending on the amount of transcription factor present in the cell. To limit complexity, they worked with a relatively simple casea gene in the bacterium E. coli that has just one binding site where a transcription factor can attach. In this case, when the transcription factor binds to the gene, it actually prevents the gene from making its productit represses expression.
To build their mathematical model, the researchers first considered all the various ways in which the available transcription factor can interact with the copies of this particular gene that are present in the cell, and then developed a statistical theory to represent the situation.
"Imagine that you go into an auditorium, and you know there are a certain number of seats and a certain number of people. There are many different seating arrangements that could accommodate all of those people," Phillips says. "If you wanted to, you could systematically enumerate all of those arrangements and figure out things about the statisticshow often two people will be sitting next to each other if it's purely random, and so on. That's basically what we did with these genes and transcription factors."
Using the resulting model, the researchers were able to make predictions about what would happen if the level of transcription factor and the number of gene copies were independently varied so that the proteins were either in high demand or there were plenty to go around, for example.
With predictions in hand, the researchers ne
|Contact: Deborah Williams-Hedges|
California Institute of Technology