Team:MIT/ResultsProcessing

From 2012.igem.org

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<h1>Not Gate modeling</h1>
<h1>Not Gate modeling</h1>
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<p> Simulations helped us to find the right trade off, as mentioned before, in the choice of relative concentration of A with to respect of B and input. The performance of the NOT Gate was analyzed using <a href = "http://lepton.research.microsoft.com/webdna/">Visual DSD</a>, a software developed to model the kinetics of DNA strand displacement. Download the code for this simulation <a href = "http://2012.igem.org/File:NOTgate_MIT2012.txt">here</a>. With this model, we were able to compute a transfer function for the NOT gate, which predicts the output levels produced in response to various input levels.
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<p><img src="http://2012.igem.org/wiki/images/6/65/NOT_gate_in_vitro_vs_simulation_small.png" width=600/>
<p><img src="http://2012.igem.org/wiki/images/6/65/NOT_gate_in_vitro_vs_simulation_small.png" width=600/>
<br> <i>Figure 2 - NOT GATE transfer function in vitro and by simulation using the software Visual DSD</i>
<br> <i>Figure 2 - NOT GATE transfer function in vitro and by simulation using the software Visual DSD</i>
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<p> Simulations helped us to find the right trade off, as mentioned before, in the choice of relative concentration of A with to respect of B and input.
 
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The performance of the NOT Gate was analyzed using <a href = "http://lepton.research.microsoft.com/webdna/">Visual DSD</a>, a software developed to model the kinetics of DNA strand displacement. Figure 2 shows the overlay of the simulated transfer function and the <i>in vitro</i> transfer function, subtracting the basal fluorescence. The graph demonstrates that the <i>in vitro</i> modeling accurately predicted the behaviour of the NOT gate. Rate constants for this simulation were based on the findings of the article "Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades, Lulu Qian  and Erik Winfree, <a href = "http://www.sciencemag.org/content/332/6034/1196/suppl/DC1">Science, 2011</a>".
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Figure 2 shows the overlay of the simulated transfer function and the <i>in vitro</i> transfer function, subtracting the basal fluorescence. The graph demonstrates that the <i>in vitro</i> modeling accurately predicted the behaviour of the NOT gate. Note the negative slope, characteristic of NOT logic. Rate constants for this simulation were based on the findings of the article "Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades, Lulu Qian  and Erik Winfree, <a href = "http://www.sciencemag.org/content/332/6034/1196/suppl/DC1">Science, 2011</a>".
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Revision as of 20:52, 2 October 2012

DEPRECATED. DO NOT USE OR EDIT. If a page uses this template, relink with MIT-results2.

Processing Overview

We demonstrated the first strand displacement-based NOT gate, which will allow for more varied logic to be implemented using all forms of strand displacement, whether in vitro or in vivo, with RNA or DNA.

We also introduced the hammerhead ribozyme, a powerful RNA-cutting tool, to iGEM and the parts registry. We intend to use the hammerhead ribozyme to manufacture RNA strand displacement gates in vivo.

Not Gate in vitro


Figure 1 - DNA molecules that constitute the NOT GATE

The original strand displacement paper demonstrated AND and OR gates, but did not include NOT gates. We designed, built, and successfully tested a strand displacement NOT gate in vitro, expanding the computational structures possible with strand displacement.

The design of our NOT gate is in Figure 1 above, where a letter with a '*' depicts a complementary domain to the one denoted by the letter alone. We arrived to this design after iterating through numerous other ideas, trying each time to reduce the number of molecules involved and their complexity.

To understand the behavior of this NOT gate, it can be useful to consider two extreme cases: no input and saturation-level input.

When the input is not present, molecule B can bind reversibly with A (by partially displacing a1) and reversibly with C (by partially displacing c2). When B binds with C, molecule D finishes the job and fully kicks c2 off of c. c2 then triggers the readout E by irreversibly displacing e2 from e1. Meanwhile, D also frees B from c1, making B catalytic and allowing it to react with more C molecules, amplifying the output. Therefore we will see high fluorescence.

When the input is present in high concentration, B binds to a2, partially displacing a2 from a1. The input then binds to a1, completing what B started by fully and irreversibly separating a2 and a1. This step was inspired by the mechanism of the cooperative hybridization (Cooperative Hybridization of Oligonucleotides,David Yu Zhang,JACS 2011). Since B is stuck with a2, it can no longer displace c2 from C, and the readout pathway described above cannot continue. Consequently e2 cannot be displaced from the readout. Therefore we will see no fluorescence.

The figure below shows experimental validation of our NOT gate design. As predicted, the concentration of the output strand decreases as the concentration of the input strand increases.


Experimental verification of the in vitro NOT gate. Output levels were measured through the displacement of a ROX-RQ strand, the e1 and e2 "readout" molecule in the header diagram. As c2 displaces e2 from e1, the RQ quencher on e2 separates from the ROX fluorescent molecule on e1, and the ROX fluoresces.

One important consideration in implementing the NOT gate is the relative concentration of A with respect to input and B. If the concentration of A is too low, the cooperative hybridization between A, B, and a high concentration of input can be slow. In that case, B is free to displace c2 from C, triggering the output although the input level is high. On the other hand, if the concentration of A is too high, even without the presence of input, B will continuously reversibly bind with A. Consequently, B is not available to displace c2 from C, and therefore we would not see a high level of output even when the level of input is low.

In addition to the relative concentration of the different components, another important point is the absolute concentration of them. This is mainly due to the equilibrium thermodynamics of cooperative hybridization. Since there are three reactants, but only two products, at low concentration the reactants are more favorable in the reaction, whereas at high concentration the products will be more favorable.

In light of these considerations, we tuned the NOT gate first by finding a set of concentrations that give the correct qualitative behavior, and then by fine-adjusting A for the right trade-off between the interaction of A, input, and B when the input is high, and the interaction of A and B when the input is low.

Not Gate modeling

Simulations helped us to find the right trade off, as mentioned before, in the choice of relative concentration of A with to respect of B and input. The performance of the NOT Gate was analyzed using Visual DSD, a software developed to model the kinetics of DNA strand displacement. Download the code for this simulation here. With this model, we were able to compute a transfer function for the NOT gate, which predicts the output levels produced in response to various input levels.


Figure 2 - NOT GATE transfer function in vitro and by simulation using the software Visual DSD

Figure 2 shows the overlay of the simulated transfer function and the in vitro transfer function, subtracting the basal fluorescence. The graph demonstrates that the in vitro modeling accurately predicted the behaviour of the NOT gate. Note the negative slope, characteristic of NOT logic. Rate constants for this simulation were based on the findings of the article "Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades, Lulu Qian and Erik Winfree, Science, 2011".

Hammerhead Ribozymes

In coming up with ways to make RNA strands in cells, and to affect cellular behavior with RNA strands, we introduced the hammerhead ribozyme to iGEM. The hammerhead ribozyme is a self-splicing ribozyme: upon being transcribed into RNA, it folds into a particular secondary structure and catalyzes its own cleavage.

This behavior is useful to us in two ways: First, hammerheads may allow us to transcribe gate complexes in vivo in a single transcript. The DNA coding for the gate would be made of the sequences of each strand, separated by hammerheads. When this DNA is transcribed into RNA, the strand sequences base-pair together, and the hammerhead folds and cleaves, separating the strands and resulting in a correctly-formed gate.

Image needed to describe gate formation.

Second, hammerheads may serve as an actuation mechanism compatible with RNA machinery. Someone else write this - I'm not sure I understand how it works.

Our hammerheads were based on those published in "Exogenous control of mammalian gene expression through modulation of RNA self-cleavage" by Yen, 2004. To demonstrate that our hammerhead cuts successfully in vivo, we appended the hammerhead sequence to the beginning or the end of a transcript for mKate, a fluorescent protein. We transfected these sequences, plus an unmodified mKate control, into HEK cells. ...To be continued, once we have conclusive data... and a mutated hammerhead control.

Image needed: circuit diagram of hammerhead-mKate


Key:Red: mKate, Blue: mKate-Hammerhead, Cyan: Hammerhead-mKate.
100,000 HEK293 cells were transfected with equimolar amounts of Hef1a:TagBFP, as a transfection marker, and one of the following: Hef1a:mKate, Hef1a:Hammmerhead-mKate, and Hef1a:mKate-Hammerhead. 48 hrs later, cells were harvested and analyzed by flow cytometry until 10,000 events were recorded. There is less red fluorescence in the hammerhead constructs compared to the Hef1a;mKate. This suggests that the hammerhead ribozymes could be cleaving the mRNA.