Difference between revisions of "Team:CUNY Kingsborough/Collaborations"
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<li>\(\frac{d[Cas13a_{crRNA}]}{dt} = k_3 \cdot [Cas13a][crRNA] - k_4 \cdot [Target][Cas13a_{crRNA}]\)</li> | <li>\(\frac{d[Cas13a_{crRNA}]}{dt} = k_3 \cdot [Cas13a][crRNA] - k_4 \cdot [Target][Cas13a_{crRNA}]\)</li> | ||
<li>\(\frac{d[Target]}{dt} = -k_4 \cdot [Target][Cas13a_{crRNA}] - \beta \cdot [Target]\)</li> | <li>\(\frac{d[Target]}{dt} = -k_4 \cdot [Target][Cas13a_{crRNA}] - \beta \cdot [Target]\)</li> | ||
| − | <li>\(\frac{d[Cas13a_{crRNA/Target}]}{dt} = k_4 \cdot [Target][Cas13a_{crRNA} | + | <li>\(\frac{d[Cas13a_{crRNA/Target}]}{dt} = k_4 \cdot [Target][Cas13a_{crRNA}]\)</li> |
<li>\(\frac{d[RNA]}{dt} = -k_{col} \cdot \frac{[Cas13a_{crRNA/Target}][RNA]}{k_M+[RNA]}-\beta \cdot [RNA]\)</li> | <li>\(\frac{d[RNA]}{dt} = -k_{col} \cdot \frac{[Cas13a_{crRNA/Target}][RNA]}{k_M+[RNA]}-\beta \cdot [RNA]\)</li> | ||
</ol> | </ol> | ||
Revision as of 22:30, 4 December 2018
Collaborations
Data Collection for the EtBr Spot Protocol
We want our standard curve to be able to predict concentrations from varying levels of pixel intensity. In order to do so, we needed a larger and more diverse dataset. So we asked iGEM teams to perform the following task: dilute 1 uL of DNA of an unknown concentration into 9 uL EtBr (1 ng/uL), photograph the samples under UV light, and send us the images. Using ImageJ®, we compared the pixel intensity of the sample with a known DNA concentration to the predicted pixel intensity based on our standard curve. Special thanks to HD Resolution from the US and Tec de Monterrey_Gdl from Mexico!
Predicting Collateral Cleavage Activity of Cas13a
We collaborated with the Columbia University iGEM Team to help them model the collateral cleavage activity of Cas13a, an enzyme used to cleave RNA in CRISPR technology. Our team provided the system of equations and resulting graphs.
What is CRISPR?
CRISPRs, or Clustered Regularly Interspaced Short Palindromic Repeats, are DNA sequences commonly found in bacteria and archaea that serve as a defense mechanism. They originate from pathogens that previously infected the host are are used to detect said pathogens when they try to re-infect the host.
A Nuclease Defends - Its Mechanism of Action
When pathogens that have previously infected the host try to re-invade, host nucleases use CRISPR sequences to detect the RNA sequences of the pathogen. A nuclease is an enzyme the cleaves nucleic acids in DNA or RNA. In CRISPR derived technology, the nuclease is lead by a guid RNA (gRNA) - a sequence of nucleotides complementary to a target strand area - to the CRISPR originating from the pathogen, and cleaves it. Here, Cas13a is the nuclease.
The Model
We based our model and parameter values on the 2017 Munich iGEM team's model. For values which could not be found in literature, values can be populated based on the desired system behavior. We created a slide tool in Mathematica using the built-in ManipulatePlot function to do this (see image below).
- \(\frac{d[Cas13a_{M}]}{dt} = k_1 - \beta \cdot [Cas13a_{M}]-k_2 \cdot [Cas13a_{M}]\)
- \(\frac{d[crRNA]}{dt} = k_1- \beta \cdot [crRNA] - k_3 \cdot [crRNA]\)
- \(\frac{d[Cas13a]}{dt} = k_2 \cdot [Cas13a_{M}] - \beta \cdot [Cas13a] - k_2 \cdot [Cas13a] [crRNA]\)
- \(\frac{d[Cas13a_{crRNA}]}{dt} = k_3 \cdot [Cas13a][crRNA] - k_4 \cdot [Target][Cas13a_{crRNA}]\)
- \(\frac{d[Target]}{dt} = -k_4 \cdot [Target][Cas13a_{crRNA}] - \beta \cdot [Target]\)
- \(\frac{d[Cas13a_{crRNA/Target}]}{dt} = k_4 \cdot [Target][Cas13a_{crRNA}]\)
- \(\frac{d[RNA]}{dt} = -k_{col} \cdot \frac{[Cas13a_{crRNA/Target}][RNA]}{k_M+[RNA]}-\beta \cdot [RNA]\)
| Constant/Parameter | Value | Description |
|---|---|---|
| \(k_{col}\) | \(10 \frac{1}{min}\) | |
| \(k_M\) | \(500 nM\) | |
| \(k_1\) | Determined by speculation | Constitutive expression of \(Cas13a_M\), crRNA (coupled) |
| \(k_2\) | Determined by speculation | Transcription of \(Cas13a_M\) |
| \(k_3\) | \(1 \frac{1}{min}\) | Equivalent to \(k_{cr}\) in Munich 2017's model |
| \(k_4\) | \(0.001 \frac{1}{min}\) | Equivalent to \(k_{t}\) in Munich 2017's model |
| \(\beta\) | Determined by speculation | Degradation factor |
Exchanging Ideas
Thanks to the HD Resolution Team in NYC, USA, we collaborated to host an event to present our projects to each other. They learned about the EtBr Spot Protocol and the light operon and we learned about their goal to cure Huntington's Disease. It was a great experience!
