![](https://static.igem.org/mediawiki/2019/b/be/T--SEU--tm-wy1.jpg)
Demonstrate
Overview
This year, the contribution of our project mainly lies in a theory and its validation in molecular computing. Our project consists of three parts: a general computation theory in molecular computing, a software tool and the corresponding experimental validation of such parts.
This page presents the results of dry experiments (which prove our theory using in silico simulation) and wet experiments (in which materials are generated by our software tool).
Dry Experiments
This part presents results of our Model.
1.The simulation results of each calculation operation.
Addition:
The figure below shows the numerical simulation result of a set of reactions:\(A_1 \xrightarrow{k} O,\quad A_2 \xrightarrow{k_2} O, \quad A_3 \xrightarrow{k_3} O\) which perform addition calculation. The initial concentrations (input values) are 1, 2 and 3, respectively (dashed lines in the figure). The output result is the sum of such values (solid red line in the figure).
![](https://static.igem.org/mediawiki/2019/8/84/T--SEU--additionSim.png)
Subtraction:
The figure below shows the reaction \(A+B \xrightarrow{k} \phi\) which is a subtractor. Experiment settings in this figure is: \([A](0)=2, [B](0)=3\).
![](https://static.igem.org/mediawiki/2019/9/93/T--SEU--subtractionSim.png)
Multiplication:
The numerical results of reactions \(\alpha \xrightarrow{k_1} \phi, A+B+\alpha \xrightarrow{k_2} A+B+\alpha+C\) are shown in the figure below. Initial concentrations are \([A](0)=4, [B](0)=3\). The result shows that the final concentration of \(C\) reaches \(4 \times 3=12\).
![](https://static.igem.org/mediawiki/2019/b/bb/T--SEU--multiplicationSim.png)
2.We use such a model to construct a chemical neuron.
A pattern recognition example in computer simulation is shown here. The DNA-based neuron is trained to recognize a 'T' in a \(3 \times 3\) grid. The gery scales of the nine grids are provided and represented by the concentrations of nine species. Also, there are nine weights corresponding to the nine inputs. During training, when the image should be recognized as 'T', we provide a desired answer species which has high concentration. Otherwise the concentration is set to \(0\).
The figure below shows the training process of the neuron. We present the input image during each training (the upper grids) and the corresponding weights within the neuron (the lower grids). The results are shown at the bottom of the figure. Only after 10 times of training, the neuron can successfully recognize the target 'T'.
![](https://static.igem.org/mediawiki/2019/5/5c/T--SEU--pattern.png)
Wet Experiment
1.TPAGE imaging result of reactant complexes and reactants of addition reaction.
![](https://static.igem.org/mediawiki/2019/c/c1/T--SEU--additionPAGE.png)
The fluorescence intensity of the one-step reaction system:
The fluorescence intensity of the first step of the two-step reaction system:
The fluorescence intensity of the second step of the two-step reaction system:
In order to demonstrate the reaction really happened, we used 12% native PAGE to visualize the DNA fragments.
![](https://static.igem.org/mediawiki/2019/a/a9/T--SEU--additionPAGEDemonstrate.png)
And we could observe the formation of Oi (the target product of the first step of two-step group), two kinds of wastes, and the mixture of b and c (the target product of the whole experiment). We could see from the stripes of the first step that with the formation of Oi, which was about the same height of 1Gia (in this reaction Oi equals to 1Gia), another light stripe occurred below the 50 bp marker of dsDNA marker, which should be the waste of the first step of addition. The products, ssDNA b and ssDNA c share the same length of 40 bp, but were not very legible because signals of ssDNAs stained by GelRed were naturally much smaller than dsDNAs . However, we could easily recognize the stripes of two wastes of this reaction, which were all double-stranded DNAs.
References
[1] CRNsimulator.