"
Team:Freiburg/Project/modeling
From 2013.igem.org
| Line 133: | Line 133: | ||
<p id="formel" > <img src="https://static.igem.org/mediawiki/2013/0/0d/Freiburg2013_Modeling_Act3.png"> </p> | <p id="formel" > <img src="https://static.igem.org/mediawiki/2013/0/0d/Freiburg2013_Modeling_Act3.png"> </p> | ||
| - | <p>There is a leaky SEAP production and one that depends on the current concentration of the Cas9/RNA Complex. This dependency is assumed to follow the Monod-kinetic. Because of the long half time (T<sub>2</sub> > 500 h) of SEAP we can neglect the SEAP decay.( | + | <p>There is a leaky SEAP production and one that depends on the current concentration of the Cas9/RNA Complex. This dependency is assumed to follow the Monod-kinetic. Because of the long half time (T<sub>2</sub> > 500 h) of SEAP we can neglect the SEAP decay.<a id="link" href="#(2)>[2]</a><a id="link" href="#(3)>[3]</a></p> |
Revision as of 14:07, 3 October 2013
Modeling our dCAS
Introduction
We used a thermodynamic approach to model and characterize our system. It is based on various ordinary differential equations (ODE) that describe the behaviour of our network. Due to the limited measurment possibilities and the unwritten law, that you should at least measure half of the number of components of your network we started by using a small network with a limited amount of different components.
The Networks
1. dCAS-VP16
Our network includes four different components dCas-VP16, a RNA complex (tracr/cr RNA), a RNA-dCas-VP16 complex and the Secreted alkaline phosphatase (SEAP). DCas-VP16 binds the RNA-complex and the whole complex binds the DNA, which leads to the production of SEAP.
 |
| Fig. 1: Transcriptional Activation via dCAS-VP16: The dCAS-VP16 fusion protein is guided to the desired DNA sequence by a co-expressed crRNA and tracrRNA. The binding of the gene recognition complex leads to an expression of SEAP. |
Setting up the ODE
According to the graphical reaction network the ODE can be set up.
Cas9 is constitutively expressed by the CBh promoter and degraded proportional to the current concentration. It is used to build the DNA recognition complex and produced during complex decay.
The RNA-complex is build linearly. The production constant can be seen as production constant of the lower expressed RNA, because this expression regulates the complex building. It is assumed that the RNA is degraded after DNA recognition complex decay and therefore the complex decay does not lead to more RNA.
The complex is build, when Cas9 and RNA meets and degraded proportional to the current DNA recognition complex-concentration.
There is a leaky SEAP production and one that depends on the current concentration of the Cas9/RNA Complex. This dependency is assumed to follow the Monod-kinetic. Because of the long half time (T2 > 500 h) of SEAP we can neglect the SEAP decay. 
| The parameters are: | |
|---|---|
| k1: linear production rate of Cas9 k2: Cas9 degradation rate k3: tracr/crRNA production rate k4: tracr/crRNA degradation rate k5: gene recognition complex building rate |
k6: cr/trRNA /Cas9 degradation rate k7: SEAPs leaky production rate k8: Complex dependent SEAP production rate k9: |
Finding the parameters
By setting up the ODE a n-dimensional hypothesis space (n is the number of
parameters) is generated and finding the right parameter combination means finding a point in the space which fits the data best.
To find these parameters we used the maximum likelihood approach. The maximum likelihood hypothesis is the hypothesis which has the highest probability to
generate the measured data. It is shown (Müller et al., 2013 ), that using the maximum likelihood approach and assuming gaussion noise in the data (an assumption that holds in our case) leads to a
least-square error minimization problem.
A minimization problem is an optimization problem. You search for parameters (p0) for which holds, that the value of the function (f) at the point of the parameters is smaller than all other values. (f(p0)<=f(p)). In three dimensions the function can be thought as a landscape and minimization is finding the deepest valley. Depending on the method you use different problems arise. The most common problem is finding only a local minimum and not the global one.
 |
| Fig. 2: Example of a minimization problem. Shown is a 3D landscape. Depending on the start position (the initial parameters), the found minimum is either a local or the global one. |
To avoid this and to be sure to have found a global minimum we started our minimization procedure using different start values for our parameters. To sample these parameters we used the latin hypercube sampling on a logarithmic scale.
 |
Fig. 3: Illustration of the latin hypercube sampling in a two dimensional parameter space. The number of initial parameter vectors is 5. Therefore the parameter space is divided in 25 subspaces. Shown is one possible parameter combination. |
N is set as the number of different initial parameter settings and the parameter range is divided by N. For the initial parameter the values are chosen so that there is only one parameter in each row and column.
The resulting errors we plotted in an increasing order to be sure to have found a global minimum.
Data generation
Cas is quantified by using Western blot and we used SEAP as target protein that can be quantified by a SEAP assay. For more detailed information refer our modeling notebook.
Sources
The Code Files
