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<p>This project is based on one of our previously published article [1]. Artificial intelligence is one prevailing research field in recent years, but most of the implementations are on traditional silicon-based computers or chips. Is it possible to use biochemical materials to implement such systems? Our previous paper provides one possible method, but it is validated by only simulations. In this project, we aim to implement such a system in wet experiments. Also, to aid the design of such systems, we will develop a small software to automatically generate required DNA topological structures.</p>
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<p>In our system, the concentrations of some input DNA species will be regarded as the input to the neural network. Some mathematical calculations are performed in solutions (weighted summation, activation, etc.) and the output of the neural network is the concentration of some certain DNA strands, similarly.</p>
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<p>There are various possible applications of this technology. For example, as it utilizes only DNA, a type of bio-friendly material, with small modifications it may be integrated to other biosystems to create biochemistry robots.</p>
<i class="fa fa-trophy counter-icon" aria-hidden="true"></i><span class="count-number">32</span> Awards Won
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<p>DNA strands have been proved a powerful medium to perform computation. Previous researches [2], [3] have shown some interesting applicatoins of such materials, which implemented a "probabilistic switch" and a pattern recognition machine, respectively.</p>
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<p>In this project, we plan to utilize a similar approach to conduct our experiment, implement a neural network using biochemical materials and validate our previous theory.</p>
<p>[1]C. Fang, Z. Shen, Z. Zhang, X. You and C. Zhang, "Synthesizing a Neuron Using Chemical Reactions," 2018 IEEE International Workshop on Signal Processing Systems (SiPS), Cape Town, 2018, pp. 187-192.</p>
<p>[2]Wilhelm, Daniel, Jehoshua Bruck, and Lulu Qian. "Probabilistic switching circuits in DNA." Proceedings of the National Academy of Sciences 115.5 (2018): 903-908.</p>
<p>[3]Cherry, Kevin M., and Lulu Qian. "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks." Nature 559.7714 (2018): 370.</p>
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<i class="fa fa-desktop counter-icon" aria-hidden="true"></i><span class="count-number">12</span> Working Years
This project is based on one of our previously published article [1]. Artificial intelligence is one prevailing research field in recent years, but most of the implementations are on traditional silicon-based computers or chips. Is it possible to use biochemical materials to implement such systems? Our previous paper provides one possible method, but it is validated by only simulations. In this project, we aim to implement such a system in wet experiments. Also, to aid the design of such systems, we will develop a small software to automatically generate required DNA topological structures.
In our system, the concentrations of some input DNA species will be regarded as the input to the neural network. Some mathematical calculations are performed in solutions (weighted summation, activation, etc.) and the output of the neural network is the concentration of some certain DNA strands, similarly.
There are various possible applications of this technology. For example, as it utilizes only DNA, a type of bio-friendly material, with small modifications it may be integrated to other biosystems to create biochemistry robots.
Preliminaries
DNA strands have been proved a powerful medium to perform computation. Previous researches [2], [3] have shown some interesting applicatoins of such materials, which implemented a "probabilistic switch" and a pattern recognition machine, respectively.
In this project, we plan to utilize a similar approach to conduct our experiment, implement a neural network using biochemical materials and validate our previous theory.
References
[1]C. Fang, Z. Shen, Z. Zhang, X. You and C. Zhang, "Synthesizing a Neuron Using Chemical Reactions," 2018 IEEE International Workshop on Signal Processing Systems (SiPS), Cape Town, 2018, pp. 187-192.
[2]Wilhelm, Daniel, Jehoshua Bruck, and Lulu Qian. "Probabilistic switching circuits in DNA." Proceedings of the National Academy of Sciences 115.5 (2018): 903-908.
[3]Cherry, Kevin M., and Lulu Qian. "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks." Nature 559.7714 (2018): 370.
