Difference between revisions of "Team:SEU/Description"

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                                        <h2>Project Description</h2>
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                                        <h3>Motivation</h3>
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                                        <p><font size="4">Artificial intelligence is one prevailing research field in recent years due to its successful application in areas like medical care, computer vision and natural language processing. Most of artificial intelligence systems are implemented with silicon-based chips which have high area complexity and power cost when facing large systems. Is it possible to use bio-chemical materials to implement artificial intelligence systems more efficiently?</font></p>
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                                        <h3>Preliminaries</h3>
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                                        <p><font size="4">DNA strands have been proved a powerful medium to perform computation. Previous researches [1], [2] have shown some interesting applicatoins of such materials, which implemented a "probabilistic switch" and a pattern recognition machine, respectively. Computation systems in this field are usually based on DNA strand displacement reactions. [3] and [4] provide vivid demonstration of how such reactions proceed in molecular level.</font></p>
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                                        <h3>Our Work</h3>
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                                        <p></p>
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                                        <p><font size="4">In this project, we have employed DNA reactions to implement neural networks which are basic components in artificial intelligence systems. Specifically, using molecular concentrations as input and output signals, we propose and demonstrate computation models for weighted summation (including multiplication and addition) and activation functions (e.g, ReLU and Sigmoid) which are arithmetic operations in neural networks. Also, the classical backpropagation algorithm which is utilized in the training process can be similarly constructed (which has not been studied in molecular computing to our best knowledge). Based on these models, we synthesize basic neural networks with DNA strand displacement reactions.</font></p>
  
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                                        <p><font size="4">To help other researchers obtain the proposed DNA-based artificial neural networks, we develop a software tool to automatically generate DNA reaction models and relevant DNA sequences according to the required parameters of neural networks, which may help them with further bio-computer design and bio-robot design. </font></p>
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<p>This page is used by the judges to evaluate your team for the <a href="https://2019.igem.org/Judging/Medals">medal criterion</a> or <a href="https://2019.igem.org/Judging/Awards"> award listed below</a>. </p>
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<p> Delete this box in order to be evaluated for this medal criterion and/or award. See more information at <a href="https://2019.igem.org/Judging/Pages_for_Awards"> Instructions for Pages for awards</a>.</p>
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<h1>Project Inspiration and Description </h1>
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<p>Document how and why you chose your iGEM project on this page. Reference work outside or inside of iGEM that inspired your project, how you selected your project goal, and why you thought this project was a useful application of synthetic biology. Finally, provide a clear and concise description of what you plan on doing for your project.</p>
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<h3>What should this page contain?</h3>
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<h3>Inspiration</h3>
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<p>See how other teams have described and presented their projects: </p>
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<li><a href="https://2016.igem.org/Team:Imperial_College/Description">2016 Imperial College</a></li>
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<li><a href="https://2016.igem.org/Team:Wageningen_UR/Description">2016 Wageningen UR</a></li>
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<li><a href="https://2014.igem.org/Team:UC_Davis/Project_Overview"> 2014 UC Davis</a></li>
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<li><a href="https://2014.igem.org/Team:SYSU-Software/Overview">2014 SYSU Software</a></li>
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We encourage you to put up a lot of information and content on your wiki, but we also encourage you to include summaries as much as possible. If you think of the sections in your project description as the sections in a publication, you should try to be concise, accurate, and unambiguous in your achievements.
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                                        <p><font size="4">For more details, please browse our <b> <a href="https://2019.igem.org/Team:SEU/Contribution">Contribution Page.</a></b></font></p>
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                                        <h3>References</h3>
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                                        <p><font size="4">[1] Wilhelm, Daniel, Jehoshua Bruck, and Lulu Qian. "Probabilistic switching circuits in DNA." Proceedings of the National Academy of Sciences 115.5 (2018): 903-908.</font></p>
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                                        <p><font size="4">[2] Cherry, Kevin M., and Lulu Qian. "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks." Nature 559.7714 (2018): 370.</font></p>
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                                        <p></p>
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                                        <p><font size="4">[3] <a href="https://www.youtube.com/watch?v=zjC_0PW9A4c&feature=youtu.be&tdsourcetag=s_pctim_aiomsg">DNA Strand Displacement.</a></font></p>
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                                        <p></p>
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                                        <p><font size="4">[4] <a href="https://www.youtube.com/watch?v=42FCzoJt8Pg&feature=youtu.be">DNA Join Circuit.</a></font></p>
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Latest revision as of 06:41, 19 October 2019









Project Description

Motivation

Artificial intelligence is one prevailing research field in recent years due to its successful application in areas like medical care, computer vision and natural language processing. Most of artificial intelligence systems are implemented with silicon-based chips which have high area complexity and power cost when facing large systems. Is it possible to use bio-chemical materials to implement artificial intelligence systems more efficiently?

Preliminaries

DNA strands have been proved a powerful medium to perform computation. Previous researches [1], [2] have shown some interesting applicatoins of such materials, which implemented a "probabilistic switch" and a pattern recognition machine, respectively. Computation systems in this field are usually based on DNA strand displacement reactions. [3] and [4] provide vivid demonstration of how such reactions proceed in molecular level.

Our Work

In this project, we have employed DNA reactions to implement neural networks which are basic components in artificial intelligence systems. Specifically, using molecular concentrations as input and output signals, we propose and demonstrate computation models for weighted summation (including multiplication and addition) and activation functions (e.g, ReLU and Sigmoid) which are arithmetic operations in neural networks. Also, the classical backpropagation algorithm which is utilized in the training process can be similarly constructed (which has not been studied in molecular computing to our best knowledge). Based on these models, we synthesize basic neural networks with DNA strand displacement reactions.

To help other researchers obtain the proposed DNA-based artificial neural networks, we develop a software tool to automatically generate DNA reaction models and relevant DNA sequences according to the required parameters of neural networks, which may help them with further bio-computer design and bio-robot design.

For more details, please browse our Contribution Page.

References

[1] Wilhelm, Daniel, Jehoshua Bruck, and Lulu Qian. "Probabilistic switching circuits in DNA." Proceedings of the National Academy of Sciences 115.5 (2018): 903-908.

[2] Cherry, Kevin M., and Lulu Qian. "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks." Nature 559.7714 (2018): 370.

[3] DNA Strand Displacement.

[4] DNA Join Circuit.