SGER: Design of Efficient Testing and Screening in Molecular Biology

SGER：分子生物学中高效测试和筛选的设计

• 批准号: 0548895
• 负责人: Taieb Znati
• 金额: $ 10万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0548895&HistoricalAwards=false
• 关键词: SGER; Design; Efficient; Testing; Screening

Intellectual Merit The proposed work will focus on the following research tasks: (i)development of near-optimal designs for DNA library screening based on new construc-tions; (ii) application of the newly developed constructions to find gene efficiently and toimprove contig sequencing; and (iii) extension of these newly developed constructions tocomplex models for eukaryotic DNA transcription and RNA translation.Successful completion of these research tasks will result in efficient and robust poolingdesigns to achieve efficient gene identification. This will enable new research directionsin several fields of biology and medicine.Broad ImpactThe multi-level educational component of this project will focus onincreasing computer science students' participation in bioinformatics. The PI will ag-gressively seek to recruit and involve talented graduate students in this research project,with a special focus on members of underrepresented communities. The educational andresearch experience students will gain from the involvement in this project will help pre-pare them for a successful career in academia and industry. The artifacts of this researchwill benefit the broader bioinformatics and advanced theory research communities. Fur-thermore, the developed techniques have potential applications in database management,medicine, biochemical testing, and biological networks.

本论文的主要研究内容包括：（1）基于新构建的DNA文库筛选的近最优设计;（2）应用新构建的DNA文库高效地寻找基因和改进重叠群测序;以及（iii）将这些新开发的结构扩展到真核DNA转录和RNA翻译的复杂模型。成功完成这些研究任务将导致高效和鲁棒的池设计，以实现高效的基因识别。这将使生物学和医学的几个领域的新的研究方向。广泛的影响这个项目的多层次教育部分将集中在增加计算机科学学生在生物信息学的参与。PI将积极寻求招募并让有才华的研究生参与这项研究项目，特别关注代表性不足的社区成员。学生将从参与本项目中获得的教育和研究经验将有助于他们在学术界和工业界取得成功。这项研究的成果将有利于更广泛的生物信息学和先进的理论研究社区。此外，所开发的技术在数据库管理、医学、生化测试和生物网络中具有潜在的应用。