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

这个小企业创新研究（SBIR）项目的更广泛的影响/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和基础设计自动化任务。 我们的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。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局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、要求、基于规则的设计、优化规划和标准化自动化的工程学科，所有这些都能够扩大规模以支持未来应用的复杂性，并且是第一个以端到端的方式这样做的。此外，拟议的工作是合成生物学学科的核心推动者，有可能帮助生物制造业将资源和专业知识转化为products.This SBIR第一阶段项目提出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。 拟议的活动将使三个软件工具更接近商业部署：遗传设计工具、装配规划工具和规则挖掘工具。这个提议解决了设计、构建和优化基因网络的基本问题，这些基因网络在生物体中产生特定的行为。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规划算法扩展规划大型设计集的DNA组装的算法和工具，并将整合数据挖掘方法，以学习优化遗传网络的设计规则。

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.