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Degradation of DNA in Plasma
Introduction
Gene therapy is one biotechnology example of a clinical application where it is possible to produce proteins in vivo, using the body’s own mechanisms for protein production. Major issues in gene delivery involve the transport of plasmid DNA (pDNA) to target sites and the conversion between different forms of pDNA.
This example uses the Parameter Estimation feature with the Reaction Engineering interface to find the rate constants of three consecutive reactions involved in a DNA degradation process.
Note: This application requires the Optimization Module.
Model Description
pDNA can be used to express proteins in the human body, proteins that can have therapeutic effects. pDNA exists in three forms — a supercoiled form (SC), an open-circular form (OC), and a linear form (L) — each with varying protein-expression rates. These pDNA-forms interconvert and degrade with time, which means a patient’s therapy benefits from knowledge about the distribution of pDNA-forms over time.
The protein expression rate for the SC form is greater than the one for the OC form, which in turn is significantly greater than that for the L form. The kinetic model in this study assumes that the pDNA-forms interconvert and decompose according to the mechanism in Figure 1.
Figure 1: Kinetic model of plasmid DNA interconversion and decomposition. Supercoiled pDNA (SC) converts to an open-circular form (OC), which in turn converts to a linear form (L). The linear pDNA decomposes to form linear fragments (F).
This example proposes a set of irreversible reactions in which an SC-form pDNA converts to the OC form and then to the L form. Then the L-form decomposes into a number of linear fragments, collectively denoted as F.
The three irreversible reactions in Figure 1 translate into these reaction rate expressions:
The rate constants k1 through k3 are found by parameter estimation, making use of the experimental data summarized in the table:
cSC (ng/μl)
cOC (ng/μl)
cL (ng/μl)
Results and Discussion
The following rate constants are calculated from the experimental data and proposed reaction mechanism: k1 9.6·10-3 (1/s), k2 4.8·10-4 (1/s), and k9.6·10-4 (1/s). In addition, the initial concentration of the SC species is estimated to 9.7 ng/μl.
Figure 2 shows the experimental values in the same plot as the simulation results. Clearly, the assumptions of the kinetic model are in agreement with the experimental findings.
Figure 2: A plot resulting from reading in experimental data and comparing it to simulation results.
The estimated rate constants show that the supercoiled pDNA rapidly transforms into the open-circular form with a half-life of approximately 1.2 minutes:
The open-circular and linear pDNA decay with half-lives of 24.1 and 12.0 minutes, respectively. As mentioned, the supercoiled pDNA has the highest protein-expression rate of the three forms. However, because the SC form has a half-life of only 1.2 minutes, it is likely that it decomposes during transport to the therapeutic target sites. These findings imply that you have to find ways to hinder the relatively fast decay of SC.
Reference
1. B.E. Houk, G. Hochhaus, and J.A. Hughes, “Kinetic modeling of plasmid DNA degradation in rat plasma,” AAPS Pharmsci, vol. 1, no. 3, pp. 15–20, 1999.
Application Library path: Chemical_Reaction_Engineering_Module/Ideal_Tank_Reactors/dna_degradation
Modeling Instructions
From the File menu, choose New.
New
In the New window, click  Model Wizard.
Model Wizard
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In the Model Wizard window, click  0D.
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In the Select Physics tree, select Chemical Species Transport>Reaction Engineering (re).
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Click Add.
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Click  Study.
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In the Select Study tree, select General Studies>Time Dependent.
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Global Definitions
Read model parameters from a text file.
Parameters 1
1
In the Model Builder window, under Global Definitions click Parameters 1.
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In the Settings window for Parameters, locate the Parameters section.
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Click  Load from File.
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Start by entering the reaction properties in the Reaction Engineering interface.
Reaction Engineering (re)
1
In the Model Builder window, under Component 1 (comp1) click Reaction Engineering (re).
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In the Settings window for Reaction Engineering, click to expand the Mixture Properties section.
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From the Phase list, choose Liquid.
Reaction 1
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In the Reaction Engineering toolbar, click  Reaction.
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In the Settings window for Reaction, locate the Reaction Formula section.
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In the Formula text field, type SC=>OC.
Reaction 2
1
In the Reaction Engineering toolbar, click  Reaction.
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In the Settings window for Reaction, locate the Reaction Formula section.
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In the Formula text field, type OC=>L.
Reaction 3
1
In the Reaction Engineering toolbar, click  Reaction.
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In the Settings window for Reaction, locate the Reaction Formula section.
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In the Formula text field, type L=>F.
Species 1
The species are dissolved in water. Add water as a solvent.
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In the Reaction Engineering toolbar, click  Species.
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In the Settings window for Species, locate the Species Name section.
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Locate the Species Type section. From the list, choose Solvent.
Parameter Estimation 1
Choose a Parameter Estimation feature to optimize the three reaction constants in the reactions entered in the interface.
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In the Reaction Engineering toolbar, click  Parameter Estimation.
Select the parameters to be estimated and provide an initial guess. The parameter c_SC_init will be used to estimate the initial concentration of the species SC. Note that the experimental data have compositions in ng/μl unit and only first order reaction constants are optimized. Therefore, the same unit will apply also for the model compositions.
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In the Settings window for Parameter Estimation, locate the Estimation Parameters section.
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In the Parameter table, enter the following settings:
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In the Parameter table, enter the following settings:
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In the Parameter table, enter the following settings:
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In the Parameter table, enter the following settings:
Prescribing scales for the estimation parameters increases the efficiency of the optimization procedure. A good starting point is to use scales of the same order as the initial values.
Experiment 1
Select an Experiment feature to import experimental data to which the simulation will be optimized.
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In the Reaction Engineering toolbar, click  Attributes and choose Experiment.
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In the Settings window for Experiment, locate the Experimental Data section.
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Click Browse.
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Click Import.
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Initial Values 1
Note that the unit ng/l should be used due to the unit of the imported data in the Parameter Estimation feature. For water, enter the value 1000 ng/μl.
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In the Model Builder window, click Initial Values 1.
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In the Settings window for Initial Values, locate the Volumetric Species Initial Values section.
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Set all reaction rate constants to the same names chosen in the Parameter Estimation feature.
1: SC=>OC
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In the Model Builder window, click 1: SC=>OC.
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In the Settings window for Reaction, locate the Rate Constants section.
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In the kf text field, type k1.
2: OC=>L
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In the Model Builder window, click 2: OC=>L.
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In the Settings window for Reaction, locate the Rate Constants section.
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In the kf text field, type k2.
3: L=>F
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In the Model Builder window, click 3: L=>F.
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In the Settings window for Reaction, locate the Rate Constants section.
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In the kf text field, type k3.
In the Study node, add an optimization step to finalize the optimization settings.
Study 1
Step 1: Time Dependent
1
In the Model Builder window, under Study 1 click Step 1: Time Dependent.
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In the Settings window for Time Dependent, locate the Study Settings section.
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In the Output times text field, type 0 4000.
Optimization
1
In the Study toolbar, click  Optimization.
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In the Settings window for Optimization, locate the Optimization Solver section.
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From the Method list, choose Levenberg-Marquardt.
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In the Optimality tolerance text field, type 1.0E-4.
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Locate the Output While Solving section. Select the Plot check box.
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In the Study toolbar, click  Compute.
Follow these steps to create Figure 2. The experimental and simulation data should match.
Results
Concentrations
1
In the Model Builder window, under Results click Experiment 1 Group.
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In the Settings window for 1D Plot Group, type Concentrations in the Label text field.
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Click to expand the Title section. From the Title type list, choose None.
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Locate the Plot Settings section. Select the x-axis label check box.
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Select the y-axis label check box.
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In the associated text field, type Concentration (ng/\mu l).
Experimental Data
1
In the Model Builder window, expand the Concentrations node, then click Experiment 1 Data.
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In the Settings window for Table Graph, type Experimental Data in the Label text field.
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Click to expand the Legends section. From the Legends list, choose Manual.
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Simulation Data
1
In the Model Builder window, under Results>Concentrations click Global 1.
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In the Settings window for Global, type Simulation Data in the Label text field.
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Click Replace Expression in the upper-right corner of the y-Axis Data section. From the menu, choose Component 1 (comp1)>Reaction Engineering>re.c_SC - Concentration - mol/m³.
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Click Add Expression in the upper-right corner of the y-Axis Data section. From the menu, choose Component 1 (comp1)>Reaction Engineering>re.c_OC - Concentration - mol/m³.
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Click Add Expression in the upper-right corner of the y-Axis Data section. From the menu, choose Component 1 (comp1)>Reaction Engineering>re.c_L - Concentration - mol/m³.
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Click to expand the Coloring and Style section. Find the Line style subsection. From the Line list, choose Cycle.
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In the Width text field, type 2.
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Click to expand the Legends section. Select the Show legends check box.
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From the Legends list, choose Manual.
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In the Concentrations toolbar, click  Plot.
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Click the  Zoom Extents button in the Graphics toolbar.
Output the values of the estimated parameters to a table.
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In the Results toolbar, click  Evaluate and choose Evaluate All.
Last plot group is not used and is therefore removed.
Concentration (re)
In the Model Builder window, right-click Concentration (re) and choose Delete.