SBIR Phase I: Commercialization of Bio-Design Automation Software for Synthetic Biology

SBIR 第一阶段：合成生物学生物设计自动化软件的商业化

• 批准号: 1519998
• 负责人: Kevin LeShane
• 金额: $ 15万
• 依托单位: Lattice Automation, Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1519998&HistoricalAwards=false
• 关键词: SBIR; Phase; Commercialization; Bio; Design

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will be to develop algorithms and software to address basic and fundamental design automation tasks in synthetic biology. The goal is to fundamentally change the way that biological designs are conceived, designed, and physically created. The tools and techniques proposed introduce a new way of thinking about biological engineering that will transform the current biological procedures from an "artisanal" craft confined to small niche research projects to an engineering discipline based on modular parts, characterization data, requirements, rule-based design, optimized planning, and standardized automation, all capable of scaling up to support the complexity of future applications, and is the first to do so in an end-to-end manner. Further, the proposed work is a core enabler for the synthetic biology discipline that has the potential to assist the biomanufacturing industry turn resources and expertise into products.This SBIR Phase I project proposes to develop algorithms and software for the design, planning, assembly, and analysis of synthetic biological constructs. The proposed activities will bring three software tools closer to commercial deployment: Tools for genetic design, assembly planning, and rule mining. This proposal addresses the fundamental overall problem of designing, building, and optimizing a network of genes that produce a specified behavior in an organism. The proposed project will further develop and tailor software algorithms and tools for the combinatorial design of genetic networks using graph theory. It will extend algorithms and tools for planning the DNA assembly of large sets of designs using advanced dynamic programming algorithms, and it will integrate data mining approaches for learning design rules for optimizing genetic networks.

该小企业创新研究 (SBIR) 项目更广泛的影响/商业潜力将是开发算法和软件，以解决合成生物学中的基本设计自动化任务。 目标是从根本上改变生物设计的构思、设计和物理创造的方式。所提出的工具和技术引入了一种新的生物工程思维方式，将当前的生物程序从仅限于小型利基研究项目的“手工”工艺转变为基于模块化部件、表征数据、需求、基于规则的设计、优化规划和标准化自动化的工程学科，所有这些都能够扩展以支持未来应用的复杂性，并且是第一个以端到端方式做到这一点的。此外，拟议的工作是合成生物学学科的核心推动者，有潜力帮助生物制造行业将资源和专业知识转化为产品。该 SBIR 第一阶段项目建议开发用于合成生物结构的设计、规划、组装和分析的算法和软件。 拟议的活动将使三种软件工具更接近商业部署：基因设计、装配规划和规则挖掘工具。该提案解决了设计、构建和优化在生物体中产生特定行为的基因网络的基本总体问题。拟议的项目将进一步开发和定制软件算法和工具，用于使用图论进行遗传网络的组合设计。它将使用先进的动态编程算法扩展用于规划大型设计集 DNA 组装的算法和工具，并将集成数据挖掘方法来学习设计规则以优化遗传网络。