Haiyan Hu, Ph.D. - US grants

PI: Hu, Haiyan
Proposal Number: 1125676

The 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.

This work will develop a computational framework to advance the understanding of epigenetic gene regulation. One of the most important tasks in modern molecular biology is to understand the role of epigenome in gene regulation and in phenotype development. With the current unprecedented availability of genome-scale epigenetic modification data, this project will develop computational algorithms and tools to model histone modification and discover patterns that are of interest to gene regulation. The discovered histone modification patterns will provide us deep insights into multiple epigenetic modification interaction and genome-epigenome interaction. The probabilistic model of gene expression regulation constructed from large-scale genomics and epigenomic data integration will greatly advance understanding of gene regulation at different levels. The objectives of this research are to: (1) create novel algorithms to model epigenetic modifications from high-throughput sequencing data; (2) create computational and statistical algorithms to mine epigenetic modification patterns; and (3) model gene regulation through genomic and epigenomic data integration.

Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changes in the underlying DNA sequence. This work will develop tools for modeling epigenetic changes. All of the proposed research will be converted into software tools that will be released as open-source and freely available software tools to the scientific community. All the associated source code will be freely available to the public through the project website. The proposed research will have great impact on education at all levels. The research will be incorporated into the graduate, undergraduate and K12 education, and will be disseminated to the research community, informal science education, and the public to enhance scientific understanding. The education and outreach component also includes developing outreach activities for women and girls in interdisciplinary science.

The project aims to develop novel computational methods and tools to study microRNA binding interactions and microRNAs' role in gene regulation. Small (~22 nucleotide), non-coding RNAs called microRNAs have been known to regulate genes involved in key aspects of animal development and physiology through binding-interactions with their mRNA targets. Since the first discovery of microRNAs in C. elegans in 1993, a large number of microRNAs have been discovered in metazoan, plants and viruses. Today, microRNAs are known to express ubiquitously in almost all cell types, evolutionarily conserved in most of metazoan and plant species, and potentially regulate more than 30% of mammalian gene products. Understanding of microRNAs' regulatory functions in the fundamental biological processes is thus essential towards gaining a global view of gene regulation, but still at its early stages despite the rapid advances in microRNA biology. The project to study microRNA gene regulation and phenotype development seeks to generate many computational algorithms, which will be converted into software tools. These tools will be subsequently released as open-source and freely available software packages to the scientific community. The research is expected to have great impact on education at all levels.

The research will be incorporated into graduate, undergraduate and K-12 education. The research will also be disseminated to the research community, informal science education, and the public to enhance scientific understanding through freely distributed computational tools and web dissemination. In addition, mentoring and outreach for women and girls is planned to help attract more women into interdisciplinary science.

RNA is emerging as an important part of gene regulatory mechanisms under various phenotypic conditions. With the current unprecedented availability of genome-scale RNA genomics and transcriptomics data, the project seeks to create a set of computational algorithms and statistical methods to model microRNA binding interactions and discover microRNA interaction patterns that will help elucidate many functional roles of microRNAs in gene regulation. The advanced probabilistic model of microRNA binding activities promises to greatly benefit mRNA target recognition under specific phenotypic conditions, lay the foundation for further study of inter-microRNA interactions, and provide insight into microRNAs' functional mechanisms in gene regulation and phenotype formulation. The research is expected to not only advance scientific understanding of microRNAs' role in global gene regulation and phenotype development, but also stimulate interest in developing and advancing efficient computational modeling and data integration methods in the informatics research field. The research information and products will be made available through the project website (http://hulab.ucf.edu/research/projects/miRNA/).

This project aims to develop computational methods and tools to discover how gene transcription initiation mechanisms vary, and their resulting functional consequences on gene transcriptional regulation. Gene transcriptional regulation refers to any process by which a cell regulates its genes expression. Properly regulated expression of genes is crucial for ensuring that biological processes are accurately carried out, for genes contributing to development, proliferation, programmed cell death (apoptosis), aging, and differentiation. Gene expression begins when mRNA molecules start to be synthesized, at the point on the gene where they initiate. To understand the regulation of gene expression, it is essential to discover the transcription initiation mechanisms under various conditions, and how these varied mechanisms lead to different outcomes, or phenotypes. High throughput sequencing of complete RNA sets synthesized in cells has produced large datasets, but matching large-scale computational studies, to understand phenotype-relevant transcription initiation mechanisms are still at its early stage. This project will study transcription initiation and gene regulation, with the goal of generating computational algorithms that capture the rules and conditions for selection of the transcription initiation site on a gene; the algorithms will then be converted into software tools. These tools will be released as open-source and freely available software packages to the scientific community and interested public. The research will be communicated in ways expected to have a great impact on education at many levels: it will be incorporated into lectures, labs, and research opportunities geared towards graduate, undergraduate and K-12 education. The research will also be disseminated to the research community and the public to enhance scientific understanding, through freely distributed computational tools and various modes of web dissemination. In addition, mentoring and outreach efforts targeted towards women and girls will help attract more women into engaging in research experiences in interdisciplinary science.

Understanding the underlying mechanisms and functional consequences of gene transcription initiation is important to understand gene regulation. The project seeks to create a set of computational algorithms and statistical methods to discover the associations between transcription initiation and gene regulation mechanisms towards advancing our understanding of gene transcriptional regulation. The advanced graph theory-based algorithms and probabilistic models of gene transcription initiation and regulation through large-scale high-throughput transcriptomics, genomics and epigenomics data integration have the promise to unveil various transcription initiation mechanisms and their functional roles in gene transcriptional regulation and phenotype formulation. The research is expected to not only advance scientific understanding of global gene regulation and phenotype development, but also stimulate interest in developing and advancing efficient computational modeling and data integration methods in the informatics research field. The research information and products will be made available through the project website (http://hulab.ucf.edu/research/projects/TransInitiation/).

Cell to cell Interactions are essential for the overall function of multicellular organisms. Recent studies have discovered that non-coding microRNAs (miRNAs) can be transferred from one cell to another cell to perform certain physiological functions. Understanding which miRNAs participate in this intercellular crosstalk and how they function under specific environmental conditions is essential to gaining a new perspective of intercellular communications. Biotechnology advancement has generated a large amount of transcriptomics data that motivate efficient computational strategies to understand miRNAs in cell-cell interactions, which is fundamental to modeling biological systems. This project investigation miRNAs in cell-cell interactions will generate methods and tools that will be freely available to the scientific community. The research will be incorporated into graduate, undergraduate and K-12 education, and will be disseminated to the research community and the public to enhance scientific understanding.

The project seeks to integrate large-scale transcriptomics data and create efficient computational algorithms to model miRNA activities relevant to cell-cell interactions under different conditions. The advanced statistical algorithms and probabilistic models of miRNA activities related to cell-cell interactions promise to unveil various roles of miRNAs in cell communications and phenotype formulation. The research is expected to not only advance scientific understanding of cell communications but also stimulate interest in developing and advancing efficient computational modeling and data integration methods in the informatics research field. Outputs of the project will be made available through the project website (http://hulab.ucf.edu/research/projects/cell2cell/).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.