Avoiding transcription factor competition at promoter level increases the chances of obtaining oscillation.
BACKGROUND
The ultimate goal of synthetic biology is the conception and construction of genetic circuits that are reliable with respect to their designed function (e.g. oscillators, switches). This task remains still to be attained due to the inherent synergy of the biological building blocks and to an insufficient feedback between experiments and mathematical models. Nevertheless, the progress in these directions has been substantial.


RESULTS
It has been emphasized in the literature that the architecture of a genetic oscillator must include positive (activating) and negative (inhibiting) genetic interactions in order to yield robust oscillations. Our results point out that the oscillatory capacity is not only affected by the interaction polarity but by how it is implemented at promoter level. For a chosen oscillator architecture, we show by means of numerical simulations that the existence or lack of competition between activator and inhibitor at promoter level affects the probability of producing oscillations and also leaves characteristic fingerprints on the associated period/amplitude features.


CONCLUSIONS
In comparison with non-competitive binding at promoters, competition drastically reduces the region of the parameters space characterized by oscillatory solutions. Moreover, while competition leads to pulse-like oscillations with long-tail distribution in period and amplitude for various parameters or noisy conditions, the non-competitive scenario shows a characteristic frequency and confined amplitude values. Our study also situates the competition mechanism in the context of existing genetic oscillators, with emphasis on the Atkinson oscillator.
Munteanu
Andreea
A
ICREA-Complex Systems Lab, Universitat Pompeu Fabra (PRBB-GRIB), Dr Aiguader 88, 08003 Barcelona, Spain. andreea.munteanu@upf.edu
Constante
Marco
M
Isalan
Mark
M
Solé
Ricard V
RV
D000465:Algorithms; D001683:Biological Clocks / Q000235:genetics / Q000502:physiology*; D001709:Biotechnology / Q000379:methods*; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D008962:Models, Theoretical; D011401:Promoter Regions, Genetic / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors