Study provides evidence that characterizing simple gene networks can help elucidate more complex systems
By Melissa Lee Phillips | February 16, 2006
Researchers studying simple, synthetic gene networks created mathematical models that enabled them to predict more complex network behaviors, providing a proof-of-principle of one of the central tenets of synthetic and systems biology, according to a report in this week?s Nature.
?This broad kind of approach that we call a bottom-up approach is in fact feasible,? senior author J. J. Collins of Boston University told The Scientist. He added that other studies have shown that mathematical models can describe simple synthetic network behavior, but no one had yet shown that these models could then be used to predict more complex behaviors.
Led by Collins and first author Nicholas Guido, the researchers engineered four different types of simple gene networks in Escherichia coli cultures. Due to differences in transcriptional promoters, adding certain chemicals to the cultures activated transcription of the green fluorescent protein (GFP) gene in one system, but repressed transcription of GFP in another system. GFP expression was simultaneously activated and repressed in a third system and was neither activated nor repressed in a control.
The researchers measured the amount of GFP fluorescence to determine the amount of gene transcription in each of the four systems. They developed and fit a mathematical model that accounted for the variation in each system, and they then used this model to predict how much fluorescence they would see in a new type of network behavior, positive feedback.
They tweaked a component of the simultaneous activation/repression system so that expression of the activator component fed back to induce its own transcription. They found that GFP fluorescence in this new system matched the amount predicted by their model.
?The model predictions seem to correspond quite well to the experimental observations of the more complex system,? said Ron Weiss of Princeton University, who was not involved in the study. Many researchers in synthetic biology have been operating on the assumption that this bottom-up approach to studying gene regulation would work, Weiss told The Scientist, but no one had yet proven that.
Collins and his co-workers also found that their model predicted an unexpected effect of cell growth and division on variation in GFP expression between bacterial cells. According to the model, this variation should decrease when cells are actively growing and dividing ? a counterintuitive pattern, given that the researchers suspected that variation in gene expression should actually increase during cell division, Collins noted.
When they ran the experiment, however, they found that the model?s prediction was indeed correct: Dividing cells show less variation in expression levels than do stationary cells. The authors theorize that cell division may allow continuous balancing of the number of plasmid copies in each cell. In stationary phase, on the other hand, discrepancies in plasmid numbers between cells are permanent and contribute to increased variation. ?We were able to use the model to make testable predictions that turned out to be biologically meaningful,? Collins said.
Creating a model that can predict new network behaviors is an ?encouraging result,? said James Liao of the University of California, Los Angeles, who was not involved in the study. The methods that Collins and colleagues used to build the mathematical model are extremely detailed, Liao said, which likely led to their ability to conduct a more complicated study than what?s been done in the past, Liao said. ?This method applies to this particular system very well.?
Whether this method is useful for investigating other types of gene networks, however, ?remains to be seen,? Liao told The Scientist. ?We hope it is.?
?If you can characterize the [network] components relatively well? then I think you could do a reasonable job for even much more complicated networks,? Collins predicted. ?But the models are only going to be as good as the experimental data that go in.?
Melissa Lee Phillips
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J. J. Collins
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