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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>
<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>
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<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>
<p>Indiscriminate usage of antibiotics allows pathogenic bacteria to develop mechanisms that render these antibiotics ineffective.
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<!-- Image antibiotic resistance development-->
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Especially in the animal sector, antibiotics are still overused. Bacteria that develop antibiotic resistance there, can spread via food or direct contact, posing a threat to human health. It is estimated that by 2050, 10 million people will die annually due to antibiotic resistant bacteria (<a href='#References'>O’Neill, 2014).</a></p>
<center><i>Schematic of the development of antibiotic resistance and how it can spread to humans</i></center>
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<p>The goal of our project is to develop a tool that will enable farmers to test on-site if a cow suffering from a bacterial infection is infected with antibiotic resistant bacteria. Based on the output, they can adapt their antibiotics usage, resulting in a more targeted treatment.</p>
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<center><i>Schematic of our project</i></center>
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<p>Our project consists of three parts: a recently characterized variant of the CRISPR/Cas system (Cas13a) for fast and accurate detection; tardigrade proteins that increase the shelf-life of our device; and the coacervation method for visible read-out. Moreover, we aim to use cells as mini-factories through the use of vesicles, truly transforming bacteria in genetically engineered machines.</p>
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<blockquote>Our project tackles one of the biggest challenges our society faces in the coming years - we offer a durable device that contributes to the reduction of antibiotic resistance. Furthermore, our device is easily adaptable to detect any kind of DNA/RNA sequence, opening doors for the rapid diagnosis of many diseases.</blockquote>
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<span class='card-title'>References:</span>
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<li><a href="https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf">O'Neill J. Review on Antimicrobial Resistance Antimicrobial Resistance: Tackling a crisis for the health and wealth of nations. London: Review on Antimicrobial Resistance. 2014. </a></li>
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.
