Team:SEU/Demonstrate

Demonstrate

In this page, we provide simulation results along with experiment results. Simulations are based on a Mathematica numerical simulation package CRNsimulator [1]. Unitless concentrations and rate constants are utilized to simplify the model. For more details, please refer to our Model Page.

Dry Experiments

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).

Subtraction:

The figure below shows the reaction \(A+B \xrightarrow{k} \phi\) which is a subtractor. There are two tests shown in this figure: \([A_1](0)=3, [B_1](0)=2\) and \([A_2](0)=2, [B_2](0)=4\).

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 \(4 \times 3=12\).

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 \(0\).

Only after 10 times of training, the neuron can successfully recognize the target 'T'.

Wet Experiment

3.The DNA experment results of our calculation operations.

References

[1] CRNsimulator.