Given that gene expression and growth rate are affected by changes in the environment a reliable representation of genetic dynamics has been a constant problem when characterizing biological systems, especially in the characterization of synthetic genetic circuits. Several methods have been developed to address this issue, among which the direct method of Zulkower et al. is worth mentioning.
Although this method has been effective, it presents difficulties to recreate the lag and exponential phase patterns in the growth profiles, with a lower accuracy at a low biomass due to noise in the measurements. As a laboratory we have developed a new method using an inverse problem approach to reconstruct dynamic gene expression rate and growth rate dynamics from noisy kinetic measurement data.
Compared to the direct method our approach was shown to reduce the mean squared error for simulated growth rate data by 20-fold and gene expression rate by more than 2-fold, allowing to reconstruct characteristics that were not apparent from the direct method.
Using our method on multiple promoter-reporter fusions with the same promoter we showed that gene expression rate dynamics is affected by neighboring genetic elements, making it important to consider these elements when generating genetic circuits.
We encourage you to take a look and start using this new method! You can access a web implementation of the algorithm at flapjack.rudge-lab.org. Preprint now available on bioRxiv:
Rudge Lab 