Team:SYSU-Software/wetlab

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, widely-used but useful circuit.e

   First, we choose a circuit similar to Lac Operon construct it within the plasmid in E.coli. A LacI promoter (BBa_R0010) is followed by two coding sequences( LacI and GFP, BBa_C0012 and BBa_K082003,respectively ). Anderson RBS family BBa_J61101 is adopted as the RBS. And the strong double terminator we select is BBa_k864600.The circuit is constructed as following:

   Here is our design: when the repressible protein LacI is expressed, it binds to the certain sequences of the promoter, which represses the transcription and translation process. As a result, there are low-content GFP proteins in the cell. While the IPTG, a inducer, is added, the repressors are inactivated so that high-content GFPs are generated. 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 our stochastic time-delay simulation in MATLAB:

   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.

References