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Team:Peking S/project/nonb
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Our ‘chemical wire’ toolbox is not only applicable to Boolean logic gene networks, but also amenable to non-Boolean population dynamics, for instance, the microbial population density balancer mentioned above. | Our ‘chemical wire’ toolbox is not only applicable to Boolean logic gene networks, but also amenable to non-Boolean population dynamics, for instance, the microbial population density balancer mentioned above. | ||
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In such a dynamical network, we need to construct a comparator device that can integrate two environmental signals. We managed to fulfill the task with the simplest elements and types of interactions of gene regulation, i.e., the inducible promoters, mRNAs and regulatory small RNAs that silence them. We will demonstrate that simply by combining these three types of genetic components, a comparator module can be implemented and thus to work in population balancer system to verify the feasibility of our "chemical wire" toolkit for non-Boolean population dynamic. | In such a dynamical network, we need to construct a comparator device that can integrate two environmental signals. We managed to fulfill the task with the simplest elements and types of interactions of gene regulation, i.e., the inducible promoters, mRNAs and regulatory small RNAs that silence them. We will demonstrate that simply by combining these three types of genetic components, a comparator module can be implemented and thus to work in population balancer system to verify the feasibility of our "chemical wire" toolkit for non-Boolean population dynamic. | ||
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| + | Design | ||
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| + | Small noncoding RNAs (sRNAs) have been depicted in recent years to play central roles in gene regulation. The special features of small RNA mediated regulation are fast responsive, noise insensitive and threshold effect, which inspire us to construct an artificial comparator based on small RNA regulation. Before biological implementation in bench, during circuit design, series of requirements must be met. | ||
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| + | (1) Input 1 activates mRNA and sRNA 1. | ||
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| + | (2) Input 2 activates mRNA and sRNA 2. | ||
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| + | (3) Suppose there is more input 1, sRNA 1 will strongly repress mRNA 2 and vice versa. | ||
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| + | (4) Suppose there is less input 2, the repression going towards the other way should be weak enough. This will lead to the production of output 1, and little to no production of output 2. | ||
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| + | (5) The output of the device is either dominated by output 1 or output 2. By seeing which is the case, we can infer whether input 1 or input 2 was present in greater amounts. | ||
| + | (See Fig. 1) | ||
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[[File:Integrated 3.png]] | [[File:Integrated 3.png]] | ||
[[File:P1'_S1.png]] | [[File:P1'_S1.png]] | ||
Revision as of 19:50, 5 October 2011
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Non-Boolean Dynamics
Synthesizing a Modular Comparator Using Small Regulatory RNAs
Introduction
Our ‘chemical wire’ toolbox is not only applicable to Boolean logic gene networks, but also amenable to non-Boolean population dynamics, for instance, the microbial population density balancer mentioned above.
In such a dynamical network, we need to construct a comparator device that can integrate two environmental signals. We managed to fulfill the task with the simplest elements and types of interactions of gene regulation, i.e., the inducible promoters, mRNAs and regulatory small RNAs that silence them. We will demonstrate that simply by combining these three types of genetic components, a comparator module can be implemented and thus to work in population balancer system to verify the feasibility of our "chemical wire" toolkit for non-Boolean population dynamic.
Design
Small noncoding RNAs (sRNAs) have been depicted in recent years to play central roles in gene regulation. The special features of small RNA mediated regulation are fast responsive, noise insensitive and threshold effect, which inspire us to construct an artificial comparator based on small RNA regulation. Before biological implementation in bench, during circuit design, series of requirements must be met.
(1) Input 1 activates mRNA and sRNA 1.
(2) Input 2 activates mRNA and sRNA 2.
(3) Suppose there is more input 1, sRNA 1 will strongly repress mRNA 2 and vice versa.
(4) Suppose there is less input 2, the repression going towards the other way should be weak enough. This will lead to the production of output 1, and little to no production of output 2.
(5) The output of the device is either dominated by output 1 or output 2. By seeing which is the case, we can infer whether input 1 or input 2 was present in greater amounts. (See Fig. 1)
