Statistical Pattern Matching Between Random Heteropolymers and Multifunctional Disordered Surfaces; Implications for Viral Inhibition and Chromatography

无规杂聚物与多功能无序表面之间的统计模式匹配；

• 批准号: 9711340
• 负责人: Arup Chakraborty
• 金额: $ 22.79万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9711340&HistoricalAwards=false
• 关键词: Statistical; Pattern; Matching; Between; Random

Abstract Proposal No.: 9711340 Proposal Type: Investigator Inititated Principal Investigator: Arup K. Chakraborty Affiliation: University of California Berkeley. This grant is awarded through the Separations and Purification Program sub-element of the Interfacial, Transport and Separations Program of the Chemical and Transport Systems Division. The principal investigator is Dr. Arup Chakraborty of the University of California Berkeley. The project is directed towards the understanding of random heteropolymer adsorption on disordered surfaces and the rational design of pattern matching between heterofunctional polymers and adsorption sites. A combination of replica field theory and ensemble groth Monte-Carlo simulations will be developed to examine the issues of statistical pattern matching. Elucidating the phenomenon of statitical pattern matching is expected to impact technologies relevant to biomedical applications such as drug delivery and drug design, Protein separation processes, and polymer adsorptioon and adhesion to surfaces. A goal of the research is the de novo design of random heteropolymer sequence statistics(and hence chemistry) that will optimally inhibit the attachment of specific viruses to cells, and to guide the design of inexpensive chromatographic materials that can discriminate between different types of protein sequences.

摘要 方案编号： 9711340提案类型：研究者发起主要研究者：Arup K. Chakraborty Affiliation： 加州伯克利大学。 该补助金通过化学和运输系统司界面，运输和分离计划的分离和纯化计划子要素授予。 首席研究员是加州伯克利大学的Arup Chakraborty博士。 该项目旨在了解无序表面上的随机杂聚物吸附和杂官能聚合物与吸附位点之间的模式匹配的合理设计。 副本场理论和合奏增长蒙特-卡罗模拟的组合将被开发来研究统计模式匹配的问题。 阐明统计模式匹配现象有望影响与生物医学应用相关的技术，如药物递送和药物设计， 蛋白质分离过程，以及聚合物吸附和粘附到表面。 该研究的目标是从头设计随机杂聚物序列统计（以及因此的化学），这将最佳地抑制特定病毒与细胞的附着，并指导可以区分不同类型蛋白质序列的廉价色谱材料的设计。