ABI Innovation: Synthetic Seqeunce Designs for Real Biology

A grant has been awarded to Stony Brook University to unify, generalize, and expand algorithmic design techniques for synthetic biology, which have proven useful in pilot studies, into a coherent set of tools to address important experimental problems in microbiology. The project will result in algorithmic and software tools to couple imaginative sequence design with DNA synthesis in several areas. In particular, they will improve the robustness, efficiency, and generality of a new approach to identify the locations of critical DNA or RNA sequence signals. This work couples large-scale synthesis with sophisticated designs employing combinatorial group testing and balanced Gray codes. Software tools will be deployed to enable broad dissemination of the technology and improve a new technique exploiting the synthesis of carefully-designed sequences (employing ideas from combinatorics, namely de Bruijn sequences) to titrate transcription factors on a genome-wide scale. This will also address algorithmic research necessary to optimize this class of synthetic sequences, coupled with experimental work to evaluate the efficacy of these designs. Array-based oligo synthesis technologies provide access to thousands of low-cost, custom-designed sequence variants. The algorithms developed for the large-scale design of diverse coding sequences will allow researchers to exploit array-based synthesis technologies and assay their performance. Finally, the advent of synthetic genomics means that laboratory strains can be "refactored", i.e., redesigned to make them easier to experimentally manipulate. The project will build on restriction-site placement algorithms to produce a web-accessible genome factorization tool. This collaboration between computational and life sciences researchers advances both disciplines, through new algorithmic results in combinatorial algorithms and discrete optimization as well as fundamental discoveries regarding gene expression, transcription factor analysis, and sequence signal detection. The project will result in software and experimental tools to advance broad areas of molecular biology. Beyond the algorithmic contributions of this research, they will develop laboratory materials of general interest. Educational outcomes include mentoring of undergraduate research students. Software and results of this project will be available from the website http://www.cs.sunysb.edu/~skiena/dna.