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| − | Mosfet
| + | Our model adopts the Gilliespie algorithm to randomly simulate from the microscopic perspective of molecules, so as to reflect the actual situation in the most real way. The specific practice is divided into the following steps: |
| − | 1． What is Mosfet?
| + | 1) For all reactions that change the amount and state of the effective substance in the system, such as the production of each protein, the interaction of each protein, and the modification of DNA by protein or small molecules. Each chemical reaction is analyzed separately. Conditions, rates, changes, etc. under different conditions are set for each reaction in combination with literature and experimental data. |
| − | The metal-oxide-semiconductor field-effect transistor (MOSFET, MOS-FET, or MOS FET) is a type of field-effect transistor (FET). It has an insulated gate, whose voltage determines the conductivity of the device. This capacity to change conductivity by varying the amount of applied voltage can be used for amplifying or switching electronic signals. In an enhancement MOSFET mode, voltage applied to the gate terminal increases the conductivity of the device. In depletion mode transistors, voltage applied at the gate reduces the conductivity.
| + | 2) Determine the amount of each substance at the initial moment of the system, the parameters of each reaction obtained from part I, and calculate each time interval of the system through conditional probability formula and multiplication rule. Also, calculate the time of the next occurrence of the system at each reaction time, so as to obtain the amount of different time points and different effective substances. |
| − | The MOSFET is by far the most common transistor in digital circuits,. Millions of transistors may be included in a memory chip or microprocessor. | + | |
| − | 2． Why are our cells also called Mosfet?
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| − | Inspired by electronic Mosfet, we designed our project, Monitoring and Operating System Founded on Engineered T cells,which is very similar to Mosfet in many ways. Our Mosfet can be divided into two systems, signal detection system and signal response system.
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| − | 1) Signal detection | + | |
| − | We use synthetic Notch (synNotch) as our signal detection receptor of the engineered T cell. When the recognition domain binds to its target antigen, it will release the effector domain (ED) and the ED is TEV protease.
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| − | 2) Signal processing
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| − | We want to convert the extracellular analog signal into an intracellular digital signal . We use Tet operator (TetO) to achieve this goal. This part contains TetO, TEV protease from the ED of synNotch and Tet repressor (TetR) from 2017 Oxford University iGEM project. We can initially implement the filtering function by using these elements but also has leakage because of TetO and TetR.
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| − | To solve the leakage problem, we introduced a recombinase into the system. The recombinase is capable of flipping the 5' end to the 3' end of the target sequence containing the specific sites, thereby realizing the zero-one switch precisely.
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| − | 3) Signal output
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| − | Finally, we achieved equal outputs by using promoters which have different strength and recombinases which have different effects . Because the promoter was located at downstream of TetO and upstream of recombinase, the function is self-evident (if you want to see more, you can click here).
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| − | 4) Reset
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| − | When the external signal disappears or falls below the threshold, our mosfet has a reset function, which is to restore the initial state. This function is achieved by a protein called recombination directionality factor (RDF).
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| − | 5) The relationship between electronic Mosfet and our Mosfet
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| − | From another perspective, the original Mosfet loaded into the circuit can do the same thing as our Mosfet: 1) constantly detect the voltage signal, 2) to process voltage signal(change the resistance),3) output the corresponding current, and 4) immediately restore the original state after the external changed voltage is restored. But the principles to achieve these are completely different,so the extension must be combined with the actual situation.
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Our model adopts the Gilliespie algorithm to randomly simulate from the microscopic perspective of molecules, so as to reflect the actual situation in the most real way. The specific practice is divided into the following steps:
1) For all reactions that change the amount and state of the effective substance in the system, such as the production of each protein, the interaction of each protein, and the modification of DNA by protein or small molecules. Each chemical reaction is analyzed separately. Conditions, rates, changes, etc. under different conditions are set for each reaction in combination with literature and experimental data.
2) Determine the amount of each substance at the initial moment of the system, the parameters of each reaction obtained from part I, and calculate each time interval of the system through conditional probability formula and multiplication rule. Also, calculate the time of the next occurrence of the system at each reaction time, so as to obtain the amount of different time points and different effective substances.
