Team:SYSU-Software new


Wet-lab verification


   Although our focus in the development of our software is modeling and algorithms, as students majoring in biology, we find it necessary to testify the accuracy of our core ODEs and algorithms by wet-lab experiments. Instead of building a complex synthetic circuit and spending lots of time on it, it’s wiser to construct a handy,but elegant circuit.


   The host cell we used was BL21 (DE3) strain of E.coli, which had been genetically engineered to incorporate the gene for T7 RNA polymerase, the lactose promoter and operator in its genome.

   The plasmid we used was pET-28a. It contains a lacI gene, specific T7 promoter, kanamycin resistance gene. The coding gene, GFP, was cloned into the plasmid, located downstream of T7 promoter.e


   When IPTG is added, the repressor from the lac operator is inactivated thus T7 RNA polymerase can bind to the T7 promoter, which greatly stimulates the transcription. Because the strength of fluorescence(FSG) can be measured by SpectraMax M5 ,which is closely related to the expression of GFP, so that we can compare it with the values calculated by our models using the data collected from previous papers.


   In order to make our experiment as valid and accurate as we can, we set a standardized protocol and divided the experiments into three groups: blank group (no circuits), control group(no IPTG) and the test group. For the 3 test group, we set a series of IPTG gradients(0, 0.1,0.2,0.4,0.6,0.8 and 1.0, unit: nmol/ml) and observe the strength of fluorescence every hour. Here are our results (1.0nmol/ml):

   And then we simulate this circuit using the data we collected from papers, some important parameters are as follows: transcription strength (TS) of LacI promoter equals 0.488[1] , K1 and Hill equation coefficient(n1) are 1.5×10-3 and 1,respectively[2].K2 and n2 for IPTG inducer are 2 and 1.5×10-6[2]. The concentration of IPTG is also 1.0nmol/ml .Here is the result of the stochastic time-delay simulation in our software:

   Please note that two Y-axis value is not the same(the GFP fluorescence strength and the GFP concentration) but two parameters are in proportional relationship. As a result, when we observe the similar tendency in two curves, we can draw a primary conclusion that our models and algorithms are able to describe the intricacy of this circuit.

   However, what’s more significant is whether the change-fold, or the regulation ratio, remains the same between the two methods. In the experiments, the regulation ratio is 17.16, 14.00, 14.55, 20.51, 9.73, 22.95 for each observation. And according to our ChangeFoldDecoder algorithms, the ratio is 20.34. Based on the theory of significance test of difference, we can see that there is no significant differences between the two values, which verifies our modeling in another perspective.


   [1] JR Kelly, Tools and reference standards supporting the engineering and evolution of synthetic biological systems, Department of Biological Engineering,2006

   [2] Iadevaia S., and Mantzaris N.V., Genetic network driven control of PHBV copolymer composition, Journal of Biotechnology, 2006,122(1), 99—121.