EAGER: Algorithms for Synthetic Gene Library Design

简介/动机：合成生物学超越了传统的基因操作，构建了不是源于自然界的新的生物成分。虽然基因组序列和功能之间的知识差距很大，但仍然存在。为了加强对基因表达的理解，研究人员构建和评估了基因变异体的文库，这些基因变异体传统上由于合成成本和随机或有偏见的组成而大小有限。虽然已经获得了有价值的见解，但对重新设计的合成基因表达的精确控制仍然难以实现。拟议的算法研究涉及合成基因变体的组合设计，以帮助构建大规模的、有目的的文库。其目的是分析决定基因表达的最重要的序列特征，同时最小化实验成本和最大化编码环境的探索。拟议的设计、合成和湿实验室评估具有修改特性的报告基因变体将有助于确定它们对模式生物表达的定量影响，并验证我们的算法设计。智力优势：成功完成后，该项目将在计算和生命科学方面取得重大进展，通过以最低成本进行多种基因库组合设计的新算法结果，以及关于基因表达的基础发现。PIS希望他们的设计方法能够推动一系列新的发现，通过使高通量具有成本效益的实验来研究基因和途径的序列特征的影响，并通过将实验观察与长期存在的计算和理论模型进行比较来帮助获得重要的见解。广泛的影响：与该奖项一起开发的算法和软件将被用于设计下一代大规模合成结构实验，这将使遗传元素的优化重新设计从一个有机体转移到另一个有机体，适应完全不同的环境。主要影响最终将取决于使用这些技术完成的科学，例如提高产量的药物和化学合成微生物、快速生产的疫苗和转化二氧化碳的微藻。教育影响包括在面向研究生和本科生的讲座中积极推广计算生物学和合成生物学，以及通过网络可访问的知识库传播研究成果和数据。

Introduction/Motivation: Synthetic Biology moves beyond conventional genetic manipulation to construct novel biological components which do not originate in nature. There still exists though a big gap of knowledge between genomic sequence and function. To enhance understanding of gene expression, researchers have constructed and evaluated libraries of gene variants, which traditionally have limited size due to synthesis costs, and random or biased composition. Valuable insights have been gained, but precise control of expression in redesigned synthetic genes remains elusive.The proposed algorithmic research involves combinatorial design of synthetic gene variants to aid the construction of large scale, purposed libraries. The aim is to assay the most important sequence features which determine gene expression, while minimizing experimental cost and maximizing the exploration of the coding landscape. Proposed design, synthesis and wet-lab evaluation of reporter gene variants with modified characteristics will help determine their quantitative effect on expression in a model organism and validate our algorithmic designs.Intellectual merit: Upon successful completion, this project will make major advances in the computational and life sciences, through new algorithmic results in combinatorial design of diverse gene libraries with minimized cost, and fundamental discoveries regarding gene expression. PIs expect their design methodologies to fuel an array of new discoveries by enabling high throughput cost effective experiments to study the effects of sequence features of genes and pathways, and help gain important insights by comparing experimental observations with long standing computational and theoretical models.Broader Impact: The algorithms and software developed with this award will be used to design the next generation of large-scale synthetic construct experiments, which will enable optimized redesign of genetic elements to be transferred from one organism to another, adapting to an altogether different environment. The primary impact will ultimately rest with the science done using these techniques, such as drug and chemical synthesizing microorganisms with improved yields, rapidly produced vaccines, and CO2 transforming micro-algae. The educational impact includes active promotion of computational and synthetic biology in lectures towards graduate and undergraduate students, and dissemination of research findings and data through web accessible repositories.