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Team:Groningen/project
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| + | ==Brief description== | ||
The aim of our project is to design a genetic device working in E.coli able to count and memorize the occurrences of an input signal. This functionality is achieved by utilization of auto-inducing loops, that act as [https://2011.igem.org/Team:Groningen/project_circuit memory units], and an engineered riboregulator, acting as an [https://2011.igem.org/Team:Groningen/project_circuit AND gate]. Each increase of the counter will result in a different state of the system that will subsequently lead to a defined output signal. | The aim of our project is to design a genetic device working in E.coli able to count and memorize the occurrences of an input signal. This functionality is achieved by utilization of auto-inducing loops, that act as [https://2011.igem.org/Team:Groningen/project_circuit memory units], and an engineered riboregulator, acting as an [https://2011.igem.org/Team:Groningen/project_circuit AND gate]. Each increase of the counter will result in a different state of the system that will subsequently lead to a defined output signal. | ||
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The design of the device is modular. This means, that both input and output signals can be changed freely, without disturbing the functionality of the memory system. Also, the design allows implementation of any number of memory units, as the AND gate design enables to extend the system in a hassle-free way.Therefore possible applications of our system range from a memory system of a Turing complete machine, through bacteria used as biosensors measuring the number of occurrences of an event in a process, to bacteria that can perform multi-step bioconversions sequentially all by themselves and without the need of changing them to another strain. | The design of the device is modular. This means, that both input and output signals can be changed freely, without disturbing the functionality of the memory system. Also, the design allows implementation of any number of memory units, as the AND gate design enables to extend the system in a hassle-free way.Therefore possible applications of our system range from a memory system of a Turing complete machine, through bacteria used as biosensors measuring the number of occurrences of an event in a process, to bacteria that can perform multi-step bioconversions sequentially all by themselves and without the need of changing them to another strain. | ||
| - | Our research is strongly dependent on [https://2011.igem.org/Team:Groningen/modeling_overview | + | Our research is strongly dependent on [https://2011.igem.org/Team:Groningen/modeling_overview modelling support]. We have created a genetic algorithm that will enable us to predict and calculate parameters of the parts used in the design. The framework of the model will be made usable for other researchers via a cloud-based application. We have also generated a bi-stability model, that furthermore includes predictions about the stability of information stored in the bi-stable switches. |
Revision as of 11:25, 20 September 2011
Brief description
The aim of our project is to design a genetic device working in E.coli able to count and memorize the occurrences of an input signal. This functionality is achieved by utilization of auto-inducing loops, that act as memory units, and an engineered riboregulator, acting as an AND gate. Each increase of the counter will result in a different state of the system that will subsequently lead to a defined output signal.
The design of the device is modular. This means, that both input and output signals can be changed freely, without disturbing the functionality of the memory system. Also, the design allows implementation of any number of memory units, as the AND gate design enables to extend the system in a hassle-free way.Therefore possible applications of our system range from a memory system of a Turing complete machine, through bacteria used as biosensors measuring the number of occurrences of an event in a process, to bacteria that can perform multi-step bioconversions sequentially all by themselves and without the need of changing them to another strain.
Our research is strongly dependent on modelling support. We have created a genetic algorithm that will enable us to predict and calculate parameters of the parts used in the design. The framework of the model will be made usable for other researchers via a cloud-based application. We have also generated a bi-stability model, that furthermore includes predictions about the stability of information stored in the bi-stable switches.
