This is because cells must deal with certain global constraints. For example, the total protein concentration in a cell is approximately constant. When the environment changes and cells adapt by regulating the expression of certain genes, these global constraints force additional changes in the expression of not only these genes, but also others that are not directly regulated.
While systems biologists have not considered these global constraints when writing equations to model gene expression, Hwa’s group looked at the problem from the opposite end. They started with the constraints and then made quantitative statements with absolute measurements, beyond the relative measurements that are commonly used.
“We invested a lot of time and effort in quantifying these changes so we could filter out the small-magnitude changes that are really just distractions on a global level,” Hwa stated. “Absolute quantitative measurements will allow researchers to quantitatively relate mRNA levels to protein levels and vice versa. One cannot make these kinds of statements based on relative measurements.”
Hwa believes this research will reframe how gene expression and regulation is taught in biology textbooks and classrooms around the world, saying it already runs contrary to things he currently teaches in his own classroom.
Controlling gene expression is a complex process. A good design rule is essential so the same genetic circuit can work in multiple conditions. Currently scientists often see circuits they spent much effort developing in one environment fail in another.
“We were using the wrong framework,” stated Hwa. Now this work has provided a simple recipe that can be used to decipher gene-gene interactions in bacterial responses and can be used to design genetic circuits more effectively in synthetic biology, helping to solve some of the world’s pressing issues in biotech and health sciences. .”
Co-first authors on this paper were Rohan Balakrishnan and Matteo Mori (both UC San Diego). Other contributors include Igor Segota and Zhongge Zhang (both UC San Diego), Ruedi Aebersold (University of Zurich), and Christina Ludwig (Technical University of Munich).
This work was supported by NIH grant R01GM109069 and NSF grant MCB1818384.