Tuneable Genetic Oscillators Could be Key to Optimisation of Cellular Output
Accurate temporal control of biological processes is of primary importance across all kingdoms of life and is often realized through genetic ‘‘clocks,’’ i.e., transcriptional networks with periodic gene expression. Heart beats, cell cycles, circadian rhythms, developmental processes, and energy metabolism are all, in one way or another, driven by robust genetic clocks.
Such oscillators can also form the building blocks to engineer complex genetic networks that can be used to synchronize cellular activity or to optimize the efficiency of metabolic pathways. Understanding the design principles of genetic oscillators and finding ways to engineer and precisely control them is therefore of crucial importance.
Now, scientists at the Imperial College London, have worked out how to fine-tune cellular clocks, which might lead to optimised production of drugs, biofuels and other chemicals. They have discovered new ways of better controlling genetic oscillations by tuning the desired amplitude and frequency.
Lead author Dr Guy-Bart Stan, from Imperial’s Department of Bioengineering, said: “We have shown that it’s possible to independently tune the amplitude and frequency of genetic clocks, giving us greater control over their output.”
The researchers used computer modelling to unravel the connections between amplitude and frequency
in current genetic oscillators, and, in light of this, proposed new design principles to control amplitude and frequency independently. These newly designed genetic clocks could help build more complex genetic networks for drug delivery, and even introduce a personalised medicine element.First author Dr Marios Tomazou, also from the Department of Bioengineering, said: “Our findings could help us make drugs that take effect at specific times depending on benefit to specific patients. This approach could make drugs even more effective, while reducing any side effects caused by being always ‘on’.”
Next, the researchers will take the concept from computer models to laboratory tests to find out whether it works in a practical setting.
