期刊
NUCLEIC ACIDS RESEARCH
卷 38, 期 8, 页码 2607-2616出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkq165
关键词
-
资金
- National Science Foundation (NSF) [CCR-0225610, 0720882, 0647591, 0720841]
- Department of Defense National Defense Science and Engineering
- Amgen Scholars Program
- Center for Hybrid and Embedded Software Systems (CHESS) at University of California, Berkeley
- US Army Research Office (ARO) [W911NF-07-2-0019]
- US Air Force Office of Scientific Research (MURI) [FA9550-06-0312, FA9550-06-1-0244]
- Air Force Research Lab (AFRL)
- State of California Micro Program
- Agilent
- Bosch
- Lockheed Martin
- National Instruments
- Thales and Toyota
- Synthetic Biology Engineering Research Center (SynBERC)
- Direct For Computer & Info Scie & Enginr [0647591, 0720841] Funding Source: National Science Foundation
- Division Of Computer and Network Systems [0647591, 0720841] Funding Source: National Science Foundation
Generating a defined set of genetic constructs within a large combinatorial space provides a powerful method for engineering novel biological functions. However, the process of assembling more than a few specific DNA sequences can be costly, time consuming and error prone. Even if a correct theoretical construction scheme is developed manually, it is likely to be suboptimal by any number of cost metrics. Modular, robust and formal approaches are needed for exploring these vast design spaces. By automating the design of DNA fabrication schemes using computational algorithms, we can eliminate human error while reducing redundant operations, thus minimizing the time and cost required for conducting biological engineering experiments. Here, we provide algorithms that optimize the simultaneous assembly of a collection of related DNA sequences. We compare our algorithms to an exhaustive search on a small synthetic dataset and our results show that our algorithms can quickly find an optimal solution. Comparison with random search approaches on two real-world datasets show that our algorithms can also quickly find lower-cost solutions for large datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据