ABI Innovation: Sequence Optimization for Synthetic Biology

石溪大学被授予一项拨款，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；以及3)自组装。这个项目将提高我们对基因组中发生的几种序列现象的理解，如密码子对偏差，RNA二级结构和tRNA使用如何调控基因表达，以及如何利用这些结果开发基因设计软件来优化基因表达。一种新的方法来确定关键DNA或RNA序列信号的位置(例如结合结构域、二级结构和mRNAi靶标)，基于组合组测试的思想将大规模合成与复杂的设计结合在一起。该项目将提高这一程序的效率和通用性，并将开发新的基于同源性的软件工具，以在采用组合搜索方法之前识别候选信号。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个短的定制序列的访问，但如果寡核苷酸能够被设计成自组装成染色体大小的序列，那么将能够实现更大的能力。酵母中的重组系统为进行这样的实验提供了平台。这个项目将开发设计大规模自组装序列的算法，以最好地利用基于阵列的新合成技术，并测试它们在实践中的工作方式。通过组合算法设计和离散优化方面的新结果，以及关于基因表达、信号检测和自组装的基本发现，计算和生命科学研究人员之间的合作将促进这两个学科的发展。该项目的更广泛影响将导致软件和实验工具的出现，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从网站http://www.cs.stonybrook.edu/~skiena/dna.上获得

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.