BRIGE: Computational Identification of Gene Regulatory Networks in Microalgae

主要研究者：胡海燕提案编号：1125676该项目的研究和教育目标是：（1）提出一个计算框架，系统地研究微藻中的基因调控，以实现计算机模拟和生物工程应用;（2）教育大学生和公众了解微藻基因调控;（3）通过指导和推广，使妇女和女孩接触跨学科科学和工程。本研究的目的是建立一种新的计算方法，在微藻模式生物C.莱因哈德氏菌计划的工作包括：（1）在全基因组范围内鉴定C. reinhardtii通过创建新的策略来测量序列保守性;（2）通过新的机器学习算法鉴定DNA调控元件的候选者;和（3）鉴定C. reinhardtii通过频繁模式挖掘和统计建模。本项目的长期目标是开发统计和计算算法来模拟微藻的基因调控网络，并将基因调控信息整合到微藻的计算机模拟中，用于微藻工程。教育活动：教育目标是在多个层次上向学生介绍令人兴奋的生物信息学领域;传播从拟议研究中获得的知识，开展外联活动，吸引更多的女孩和妇女参与科学，扩大代表性不足群体的参与。计划开展的活动包括：研究生/本科生辅导，课程开发，并通过与UCF本科生研究和国家女孩合作项目办公室合作，对妇女和女孩进行外联/辅导。教育活动将与研究活动紧密结合。智力价值：了解微藻中基因是如何转录调控的是生物学和微藻工程中的一个重要问题。本研究的目的是通过计算识别微藻模式生物C中基因组水平的DNA调控元件，来提高我们对微藻基因调控的理解。莱因哈德氏菌目前还没有广泛适用的方法和系统的研究，以全面确定DNA调控元件和基因调控机制的C。莱因哈德氏菌通过创建新的计算算法，如无重复的方法，以确定整个C。reinhardtii基因组和计数Gibbs抽样方法重新鉴定DNA调控元件，该工作将能够系统地发现C. reinhardtii的基因组序列，为构建C. reinhardtii和其他微藻生物在不久的将来。从所提出的研究中获得的基因调控信息有希望促进微藻和微藻生物工程的后续研究中的集成的计算机模拟。申请人先前在大规模生物数据的数据集成和知识发现、机器学习和数据挖掘技术以及软件开发方面的工作，使其处于执行拟议研究的独特位置。更广泛的影响：拟议研究将对多个层面的教育产生重大影响。研究将通过研究生/本科生指导和课程开发纳入研究生和本科生教育。拟议研究所产生的知识将通过一个网站传播给研究界和公众，以提高科学认识。此外，对妇女和女孩的辅导和外联将创造一个积极的循环，吸引更多的妇女进入跨学科科学。

PI: Hu, HaiyanProposal Number: 1125676The research and education goals of the project are to: (1) propose a computational framework to systematically study gene regulation in microalgae towards in-silico modeling and bioengineering applications; (2) educate college students and general public about microalgae gene regulation; and (3) expose women and girls to interdisciplinary science and engineering through mentoring and outreach.Research Activities: The research objective is to create novel computational approach to perform genome-wide identification of DNA regulatory elements and their patterns in microalgal model organism C. reinhardtii. The planned activities include: (1) genome-wide identification of DNA regulatory regions in C. reinhardtii by creating new strategy to measure sequence conservation; (2) identification of candidates for DNA regulatory elements via novel machine learning algorithms; and (3) identification of interacting DNA regulatory elements in C. reinhardtii through frequent pattern mining and statistical modeling. The longer-term goal of this project is to develop statistical and computational algorithms to model gene regulatory network of microalgae, and to integrate gene regulation information into in-silico modeling of microalgae for microalgae engineering.Education Activities: The educational objectives are to introduce students at multiple levels to the exciting area of bioinformatics; disseminate the knowledge obtained from the proposed study and develop outreach activities to attract more girls and women into science and to broaden participation of underrepresented groups. The planned activities include: graduate/undergraduate mentoring, curriculum development, and outreaching/mentoring women and girls by collaborating with the UCF office of Undergraduate Research and National Girls Collaborative Project. The education activities will be tightly integrated with the research activities. A combination of metrics will be employed to evaluate the education activities.Intellectual Merit: Understanding how genes are transcriptionally regulated in microalgae is an important problem in both biology and microalgae engineering. The proposed work aims to advance our understanding of gene regulation in microalgae by computationally identifying DNA regulatory elements at the genome-scale in microalgae model organism C. reinhardtii. There is as yet no broadly applicable method and no systematic study to comprehensively identify DNA regulatory elements and characterize gene regulatory mechanisms in C. reinhardtii. By creating novel computational algorithms such as alignment-free methods to identify regulatory regions in the entire C. reinhardtii genome and enumerative Gibbs sampling approach to de novo identify DNA regulatory elements, the proposed work will be able to systematically discover DNA regulatory signals in C. reinhardtii, and will lay the ground for genomescale gene regulatory network construction in C. reinhardtii and other microalgal organisms in the near future. The gene regulatory information gained from the proposed research has the promise to facilitate integrative in-silico modeling of microalgae and microalgae bioengineering in the subsequent research. The prior work on data integration and knowledge discovery from large scale biological data, machine learning and data mining techniques, and software development put the applicant in a unique position to perform the proposed research.Broader Impacts: The proposed research will have great impact on education at multiple levels. The research will be incorporated into the graduate and undergraduate education by graduate/undergraduate mentoring and curriculum development. The knowledge resulted from the proposed research will be disseminated to the research community and the public to enhance scientific understanding through a website. In addition, mentoring and outreach for women and girls will create a positive cycle in attracting more women into interdisciplinary science.