Team:CSMU NCHU Taiwan/Model/Parts

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Parts model

Overview

This year, our parts modeling focuses on predicting the result of the productivity of MSMEG_5998(BBa_K2382001) and Thioredoxin-MSMEG_5998 fusion protein(BBa_K2382009). Since our anti-aflatoxin protein could be used widely in many applications, such as antidote, and feed additive(see more in our human practice page), it is an indispensable important part to our project to have this data when we mass-produce our anti-aflatoxin protein in future work.

Material and Method

We transformed the plasmids that contained MSMEG_5998(BBa_K2382001) and Thioredoxin-MSMEG_5998 fusion protein(BBa_K2382009) respectively into competent cell E.coli BL21. After cultured overnight, measure the ABS600 and diluting the LB medium to O.D.=0.1. Then incubate at 37℃, 150 rpm until the O.D. of the samples reach 0.4 to 0.6 . Add 80ul 100mM IPTG( final concentration : 0.4mM ) to 125 ml flask and return to 37°C. From then on, after measure the O.D. values, transfer 1 ml from the induced sample and centrifuge at maximum speed for 60 seconds at RT and remove supernatant at 0, 1, 2, 3, 4, 5, 6, 7, 8 hours and 0, 0.5, 1.0, 1.5, 2.0 ,2.5 , 3.0, 3.5, 4 hours. Then we use Western Blot mehtod to amalyze the quantaty of MSMEG_5998 at each time spot.

Result

Reaction formula



ODE equation



Fit the experimental data:

1.  Estimation method: Estimating non-mixed effects model with Lsqnonlin.

2.  Estimated parameters:



  •  Raw data:




Fig. I Experimental data of degradation of aflatoxin.




Fig. II Data comparison between experimental data and simulation.



Fig. III Estimated parameter values and their standard errors. Note that the standard errors are extremely higher than the parameter values, demonstrating that though parameter values shown here are best estimated, they are not accurate for lacking adequate experimental data.


Stochastic simulation

1.  Simulation method: Gillespie algorithm

2.  Number of samples: 30000 (300shown)


Fig. IV Stochastic simulation results of degradation of aflatoxin by MSMEG_5998. Red lines represent the stochastic simulations, and the black line represents the mean level of degradation.


3.  Comparison between experiments and simulations


Fig. V Comparison between experimental data and simulation results. Simulation data is the mean level of the stochastic simulations, R^2 is calculated to be 0.9962.


Discussion

1.  According to the raw data, the fusion protein is able to degrade Aflatoxin B1, which matches the prediction from the results we learned in the docking model.

2.  Via the stimulation, the team is able to learn after a certain period of time that how much Aflatoxin B1 will be remaining. According to the results, the fusion protein is able to degrade 50% of the toxin within 3 hours and reach a 70% degradation in 7 hours. It is an amazing information to our team that how great the ability of degradation of syn-MSMEG5998 is.

3.&nbsplSince the cofactor, F420 is not easily accessible and the control variables are complicated, it is difficult to construct an enzyme model by fully working in wet lab. Fortunately, with the modeling techniques, the team is able to systematically conduct analysis under limited data and resource; therefore modeling enables us to predict results for wet lab and assist the whole project.