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<div align="center"> Figure 1. Key Assumption of the Thermodynamic Model</div> <br><br> | <div align="center"> Figure 1. Key Assumption of the Thermodynamic Model</div> <br><br> | ||
+ | First, let's start with the case without any transcription factors. In this simple case, we will ignore the RNAPs that are in the cytoplasm or engaged in transcription, and assume that our pool of free RNAPs is the "non-specifically" bound RNAPs (Non-specific here means the protein is bound to sequences other than the target promoter). According to the paper (Bintu, 2005), the sum of Boltzmann weights of all possible states of P polymerase molecules on DNA must be calculated to evaluate the probability of RNAP bound. Shown below is a table of variables used in the calculations that follow. | ||
+ | *** TABLE *** | ||
+ | <div align="center"> Figure 2. Table of Variables </div> <br><br> | ||
+ | |||
+ | All the possible outcome of P RNAP molecules binding to the genome can be classified into two: 1) all RNAP bound non-specifically, or 2) (P-1) RNAP bound non-specifically and 1 RNAP bound specifically to promoter of interest. Next, we count the number of ways these two outcomes can be achieved and sum their statistical weights calculated by: | ||
+ | |||
+ | [[File:Statistical_weight.png|200px|center]] | ||
+ | <div align="center"> Figure 3. Statistical Weight (Boltzmann Weight) </div> <br><br> | ||
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+ | |||
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+ | cite bintu | ||
</div> | </div> |
Revision as of 07:14, 20 September 2013
Mathematical Modeling of Bistable Toggle Switch
Thermodynamic Model of Cooperative Repression
A thermodynamic approach was used to model the cooperative repression by transcription factors (iTAL and CRISPR) binding to multiple binding sites. The model was largely based on the models shown in "Transcriptional regulation by the numbers: models" (Bintu, 2005), which base on the reasoning that gene expression level is related to the probabilities of various molecules--such as transcription factors (TFs) and RNA Polymerase (RNAP)--binding to the DNA of interest. The key concept in this model is using the "probability that RNAP occupies the promoter of interest" instead of using "concentration of protein" produced. One of the most important assumptions of this model is that promoter occupancy by RNAP is directly proportional to the level of gene expression.
First, let's start with the case without any transcription factors. In this simple case, we will ignore the RNAPs that are in the cytoplasm or engaged in transcription, and assume that our pool of free RNAPs is the "non-specifically" bound RNAPs (Non-specific here means the protein is bound to sequences other than the target promoter). According to the paper (Bintu, 2005), the sum of Boltzmann weights of all possible states of P polymerase molecules on DNA must be calculated to evaluate the probability of RNAP bound. Shown below is a table of variables used in the calculations that follow.
- TABLE ***
All the possible outcome of P RNAP molecules binding to the genome can be classified into two: 1) all RNAP bound non-specifically, or 2) (P-1) RNAP bound non-specifically and 1 RNAP bound specifically to promoter of interest. Next, we count the number of ways these two outcomes can be achieved and sum their statistical weights calculated by:
cite bintu