Modeling

Overview

We have divided our models into two levels. In the gene level, we aimed to gain an insight of the gene expression dynamics of our entire circuit via using hypoxia inducible promoters and hypoxia response elements. We also expected to gain knowledge about how our designed promoter works, which includes sensitive parts which are HRE and NFKβ for ROS and O2 respectively. These parts will work under hypoxic and normoxic conditions during ischemia and reperfusion. We also tried to characterize our parts further and analyze the experimental data.

In the protein level, we proposed the multi-compartment model to analyze the changes of SOD, GPX and aprotinin levels which are the antioxidant enzymes able to degrade ROS present in the cells. On the other hand, we tried to calculate the level of tPA transported from the endothelial cell to blood during hypoxia. By using two separate sensitive promoter systems, we aimed to regulate the release of tPA synthesized by our engineered endothelial cell.

1.Gene Expression Dynamics

Analysis of the problem

Firstly, we built a model to help us understand the gene expression dynamics of the whole circuit. In our promoter design, we added hypoxia response element to the upstream of our effector protein gene. In hypoxic conditions, HIF1-α protein levels are high enough to translocate it into the nucleus and form a transcriptional complex with the HIF1-β. We decided not to include a separate step for the dimerization and binding processes to hypoxia response element (HRE). We assumed the HIF1-α level as a constant in hypoxia which does not change dynamically. HIF1-α/ HIF1-β complex can bind to HRE which then activates the transcription of our gene interest. These are tPA, SOD, GPX, aprotinin or luciferase. In addition, CMV promoter has a leaky luciferase expression without HIF1-α. Therefore, we assumed that luciferase expression from HRE + CMV + Luciferase without HIF1-α is equal to the expression of CMV + Luciferase in the normoxic condition.

These are the four chemical reactions which represent each process.

The symbol declaration is:

X1 : HIF1-α

Y : HRE- pCMV

X1Y : HIF1-α + [HRE- pCMV] Complex

X2 : Interested protein (in model, luciferase)

X3 : Only pCMV Promoter (distincted from HRE)

Finally, we assigned each process with a reaction rate constant.

How did we build it?

Using the SimBiology toolbox for Matlab, we designed a framework of the hypoxia inducible promoter system (Figure 1). The processes of binding, dissociation of transcription factor, expression of luciferase in hypoxic/normoxic conditions, and the degradation of luciferase were included in framework.

After designing the basic framework of our model, we were required to establish mathematical equations for each reaction withan appropriate rate constants. These equations and the corresponding values are shown below in Tables 1 and 2.

What did it show?

• Before running the model, we had to decide on how the hypoxia response element affects gene expression The pCMV promoter (distincted from HRE) expressed approximately 0.15 nM and the [HRE- pCMV] complex expressed 1.86 nM of luciferase in 12 hours. In our simulations, we show that adding hypoxia response element in our promoter design increases the protein expression approximately 20 times more in hypoxic conditions.

• This data was then used to design an experiment that could be performed in the lab to verify the model. You can visit our results page to see the comparison.

2.Safety approach of gene expression dynamics of tetracycline regulatory system

pTRE-Tet Off System

• For clinical use and application, we are required to add new features in order to manage and monitor our therapy. To make this possible, we have designed our parts under tetracycline regulatory operon which interacts with the tetracycline antibiotic. This interaction results in the inhibition of our desired proteins production. Therefore clinician may use this as an advantage to stop the treatment when necessary.
• In order to realize how our system will work. We try to calculate the inhibition of production of interested protein by adding tetracycline antibiotic.
• Using the SimBiology toolbox for Matlab we created a framework of the pTRE-Tet Off System (Figure 1).

• We wrote down the seven chemical reactions in represent of each process.

• The symbol decleration is:

X1: pCMV

Y: tTA

X2 : [TetRE -PminiCMV]

X2Y: tTA[TetRE -PminiCMV]

X3 : Luciferase

Z : Tc

YZ: [Tc-tTA]

After creating the basic framework for the model we needed to create mathematical equations for each reaction with appropriate rate constants. These equations and the corresponding values are shown below in Tables 1 and 2.

What did it show???

• In order to prove the safety of our project, we added a switch of mechanism in our design and we calculate how it will change the gene expression by modeling. Then, the result of model compared with wet lab experiments to check is it reliable or not.
• In our model approach, we calculated the final effect of addition of different amount of tetracycline on amount of interested protein.
• Inhibition of luciferase reached %25 by adding 106 molecules tetracycline in wet and dry lab results.

Modelling Results

Experiment Result