The discovery is one step forward to further understanding the full picture of gene regulation in a cell and allows scientists who are interested in blocking transcription factors access to powerful new tools to narrow their search for the most promising candidates.
Scientists from the University of California, San Diego utilised a 'systems biology' approach that uses many different lines of evidence. The approach serves to give a much more revealing and detailed picture of how cells orchestrate gene regulation to cope with different environments.
The team discovered that transcription factors act not only in isolation, but also in pairs, trios, and combinations of up to 13 to regulate distinct sets of genes. All in all, a list with 363 combinations of 91 transcription factors that regulate a large proportion of genes in the yeast genome was compiled.
In order to determine their significance, the team used statistical tests to discover active combinations of transcription factors, as if a cell were mixing and matching parts of its regulatory-protein selection to different environmental conditions.
"A cell's surprising ability to mix and match so many different combinations of these factors to achieve a high degree of complexity and specificity in the expression of its genes is impossible for even the most experienced cell biologists to conceptualise," said Andreas Beyer, a post-doctoral fellow at the UCSD Jacobs School of Engineering's Department of Bioengineering.
The researchers expect that human cells use a similar system of transcription-factor combinations, but on a much larger scale.
The researchers combined the results of their laboratory with other large-scale measurements of transcription factor-gene binding,
The team were able to identify new transcription factor binding patterns by borrowing a concept from computer science. The team considered the binding of one transcription factor to one gene as analogous to one "hop" of a data packet from one Internet router to another.
In the case of gene regulation, the team identified "2hop" relationships by first focusing on single transcription factor-gene associations, plus other experimental evidence that indicates that that gene regulates a second gene.
To enlarge the scope of the model further, Ideker's group also incorporated other previously discovered transcription-factor interactions and related genetic results.
They relied on a total of eight types of direct and indirect evidence to create a model. That model predicts 980 as-yet-undiscovered transcription factor-gene binding interactions.
Bioengineering researchers at UCSD and two research institutes in Germany report in the June 16 issue of >PLoS Computational Biology.