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Scalable DNA computing via microfluidics integration

Scalable DNA computing via microfluidics integration
通过微流体集成进行可扩展的 DNA 计算
批准号:
579995-2022
负责人:
Chou, LeoLYT
金额:
$1.82万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
翻译
许多人类疾病都是通过体液(如唾液、尿液和血液)中生物分子浓度的变化来反映的。因此,测量这些变化的能力可以改善这些疾病的诊断和治疗。一个主要的挑战是现有的技术体积庞大,价格昂贵,使用起来很复杂。此外,从中产生的数据需要专业知识来分析和解释。这些制约因素限制了他们在当地诊所、农村地区和患者家中等护理点的广泛可及性。该项目的总体目标是通过开发“分子计算机”来提高可访问性。分子计算机使用一组工程生物分子来测量、分析和处理信息。分子计算机消耗生化能量,体积最小,可以直接与患者样本中的生物分子相互作用。这些优势提供了使生物分子分析更加便携和方便的潜力。为了实现这一目标,本提案的具体目标是将由DNA组成的分子计算机与微流体装置相结合。这种集成的优点是它将增强整体计算能力。提高计算能力对于教分子计算机分析更复杂的数据是必要的,例如多种生物分子的不同浓度如何与疾病状态相关联。成功的结果提供了设计下一代分子计算机的策略,这不仅将改善生物分子分析,而且还将改善数据加密和存储等其他应用。
英文摘要
Many human diseases are reflected by changes in the concentrations of biomolecules found in our bodily fluids, such as saliva, urine, and blood. Therefore, the ability to measure these changes can improve the diagnosis and treatment of these diseases. One major challenge is that existing technologies are bulky, expensive, and complex to use. In addition, the data generated from them require expertise to analyze and interpret. These constraints limit their broad accessibility to point-of-care such as local clinics, rural areas, and within patient homes. The overall goal of this project is to improve accessibility by developing "molecular computers". A molecular computer uses a set of engineered biomolecules to measure, analyze, and process information. Molecular computers consume biochemical power, have minimal size, and can directly interact with the biomolecules in patient samples. These advantages offer the potential to make biomolecular analysis more portable and accessible. Towards this goal, the specific objective of this proposal is to combine molecular computers made of DNA with a microfluidic device. The advantage of this integration is that it will enhance the overall computing power. Increasing the computing power is necessary to teach molecular computers to analyze more complex data, such as how different concentrations of multiple biomolecules correlate with disease states. Successful outcome offers a strategy to design next-generation molecular computers, which will improve not only biomolecular analysis, but other applications such as data encryption and storage.
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