ABI Innovation: Sequence Optimization for Synthetic Biology

斯托尼布鲁克大学获得一笔赠款，用于将设计基因序列的计算方法推广和扩展为一套连贯的工具，用于分子生物学的三个领域：1）优化基因表达的序列设计; 2）信号识别和定位; 3）自组装。该项目将提高我们对基因组中发生的几种序列现象的理解，例如密码子对偏倚，RNA二级结构和tRNA使用如何调节基因表达，以及如何使用这些结果开发基因设计软件以优化基因表达。（例如结合域、二级结构和mRNAi靶标）将大规模合成与基于组合组测试的想法的复杂设计相结合。该项目将提高这一程序的效率和通用性，并将开发新的同源性为基础的软件工具，以确定候选信号之前，采用组合搜索方法。新的基于阵列的寡核苷酸合成技术提供了对数万甚至数十万短定制序列的访问，但是如果寡核苷酸可以被设计成自组装成染色体大小的序列，则甚至更大的功率将成为可能。酵母中的纯化系统提供了进行此类实验的平台。该项目将开发用于设计大规模自组装序列的算法，以最好地利用新的基于阵列的合成技术，并测试它们在实践中的工作原理。计算科学和生命科学研究人员之间的这种合作将通过组合算法设计和离散优化的新成果以及基因表达，信号检测和自组装的基础发现来推动这两个学科的发展。该项目的更广泛影响将产生软件和实验工具，以推进分子生物学的广泛领域，包括疫苗的设计。该项目的软件和结果将在网站http://www.cs.stonybrook.edu/~skiena/dna上提供。

A grant is awarded to Stony Brook University to generalize and expand computational approaches for designing gene sequences into a coherent set of tools for three areas of molecular biology: 1) sequence design for optimized gene expression; 2) signal identification and location; and 3) self-assembly. This project will improve our understanding of several sequence phenomena that occur in genomes, such as codon-pair bias, how RNA secondary structure and tRNA usage modulates gene expression, and how to use these results to develop gene design software to optimize gene expression.A new approach to identify the locations of critical DNA or RNA sequence signals (e.g. binding domains, secondary structures, and mRNAi targets) couples large-scale synthesis with sophisticated designs based on ideas from combinatorial group testing. This project will improve the efficiency and generality of this procedure, and will develop new homology-based software tools to identify candidate signals prior to employing combinatorial search approaches. New array-based oligo synthesis technologies provide access to tens or even hundreds of thousands of short custom sequences, but even greater power will become enabled if oligonucleotides can be designed to self-assemble into chromosome-sized sequences. Recombination systems in yeast provide the platform to conduct such experiments. This project will develop algorithms for the design of large-scale, self-assembling sequences, to best exploit new array-based synthesis technologies, and test how they work in practice.This collaboration between computational and life sciences researchers will advance both disciplines, through new results in combinatorial algorithm design and discrete optimization as well as fundamental discoveries regarding gene expression, signal detection, and self-assembly. Broader impacts of this project will result in software and experimental tools to advance broad areas of molecular biology, including the design of vaccines. Software and results of this project will be available from the website http://www.cs.stonybrook.edu/~skiena/dna.