Computational Design of Complex Multi-Scale Systems: Design of Synthetic Muscle with Shape Grammars and Agent-Based Search
Computational Design of Complex Multi-Scale Systems: Design of Synthetic Muscle with Shape Grammars and Agent-Based Search
批准号:
1160840
负责人:
Jonathan Cagan
金额:
$42.49万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-07-01 至 2015-06-30
中文摘要
该奖项的研究目标是探索复杂多尺度系统的计算设计,重点是在宏观水平上通过设计肌球蛋白(一种分子马达)和肌球蛋白-肌动蛋白网络在纳米水平上进行合成肌肉的分层设计。通过5个层次,肌球蛋白是多尺度宏观结构肌肉系统的主要力量成分。本研究使用了一种新的计算方法,将多层次、多尺度基于主体的搜索与肌凝蛋白的形状语法表示相结合,以配置具有所需宏观功能特性的新的肌凝蛋白-肌动蛋白丝设计和潜在的新生物材料(合成肌肉)。预计通过不同类型肌球蛋白在最低尺度上的组合,将出现独特的宏观肌肉特性,并且基于计算探索可能会发现新的蛋白质发现。如果成功,这项研究将使设计跨越从纳米到宏观的尺度,同时适应生物组装系统中由此产生的突发行为的复杂性。此外,使用计算机有效、快速地在这些尺度上搜索有意义的设计,将比目前通过湿实验室进行更大的探索,并有可能发现存在或可能在自然界中制造的新蛋白质。所创建的计算工具可能会导致具有重要生物医学特性的新型纳米材料-来自肌凝蛋白和肌动蛋白不同组合的非直观创新设计或新的蛋白质发现可能会产生。
英文摘要
The research objective of this award is to explore the computational design of complex multi-scale systems, with a focus on the hierarchical design of synthetic muscles at the macro level via design of myosin (a molecular motor) and myosin-actin networks at the nanoscale level. Through a 5-level hierarchy, myosin is the major force component in a multi-scale macro structural muscle system. This research uses a novel computational method that combines multi-level, multi-scale agent-based search with shape grammar representations of myosin to configure new myosin-actin filament designs and potential new biomaterials (synthetic muscle) with desired macro-level functional properties. It is anticipated that through combinations of different types of myosin at the lowest scale, unique macro muscle properties will emerge, and potentially new protein discoveries can be identified based on computational exploration. If successful, this research will enable design across scales from nano to macro while adapting to the complexity of the resulting emergent behavior in a biologically assembled system. As well, the use of computers to effectively and rapidly search across these scales for meaningful designs will enable greater exploration than is currently available through the wet lab, and the potential for discovery of new proteins that exist or could potentially be made in nature. The created computational tool may lead to new nanomaterials with important biomedical properties - non-intuitive innovative designs from different combinations of myosin and actin or new protein discoveries may result.
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