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Novel Methods for Large Scale Presence only Data in Biological Systems Engineering

生物系统工程中大规模仅存在数据的新方法

基本信息

批准号：
9975868
负责人：
GARVESH RASKUTTI
金额：
$ 14.58万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9975868
关键词：
Address Algorithms Amino Acid Sequence Area Automation Biochemical Biological Biological Assay Biology Biotechnology Cells Classification Complex DNA DNA sequencing Data Data Set Databases Development Ecosystem Engineering Experimental Designs Explosion Gene Proteins Genetic Genetic Diseases Genome engineering Genotype Goals Laws Learning Libraries Medicine Methodology Methods Miniaturization Modeling Phenotype Physics Population Property Protein Engineering Proteins Sampling Side Source Statistical Algorithm Statistical Methods Structure Symptoms Uncertainty Variant Work Writing base biological systems design experimental study genetic disorder diagnosis high dimensionality high throughput screening high throughput technology improved insight large scale data next generation novel novel strategies phenotypic data physical model response theories therapeutic protein three dimensional structure tool

项目摘要

This proposal will develop a number of novel statistical tools for learning genotype-phenotype mappings from experimental data. Massive genotype-phenotype data sets can be generated by genetic diversification, followed by high-throughput screening/selection and next-generation DNA sequencing of functionally-distinct populations. The resulting data presents new and interesting statistical challenges including large numbers of examples, presence-only responses, and noisy/missing data. Presence-only responses arise because most high-throughput screening/selection methods isolate only functional examples (positive responses), while non-functional examples (negatives) are difficult or impossible to obtain. The resulting data sets contain the initial unlabelled variant library and positive examples. The modeling tools developed in this proposal apply to all levels of biological organization spanning from molecules to ecosystems. The novel statistical methods developed in this proposal will model the relationships between protein sequence, structure, and function, with the goal of gaining insight into biochemical mechanisms and designing new and useful proteins. This proposal will (i) develop new theory and tools to analyze the large quantities of protein sequence function data that are being generated by emerging high-throughput methods; (ii) address challenges associated with positive-unlabeled (PU) learning, extremely large data size, low- quality/missing data, and (iii) encoding side information from existing databases or physical models. Furthermore, applying the methods and algorithms developed in this work will generate novel scientific insights and engineered biological systems.

这一建议将开发一些新的统计工具，用于从实验数据中学习基因-表型映射。通过基因多样化可以产生大量的基因-表型数据集，然后是高通量筛选/选择和功能不同群体的下一代DNA测序。由此产生的数据提出了新的有趣的统计挑战，包括大量的示例、仅存在的响应以及噪声/丢失的数据。由于大多数高通量筛选/选择方法只分离功能示例(阳性响应)，而非功能示例(阴性)很难或不可能获得，因此出现了仅存在响应。所得到的数据集包含初始的未标记的变异库和正例。本提案中开发的建模工具适用于从分子到生态系统的所有级别的生物组织。在这项建议中开发的新的统计方法将对蛋白质序列、结构和功能之间的关系进行建模，目的是深入了解生化机制并设计新的有用的蛋白质。这项提议将(I)开发新的理论和工具来分析由新兴的高通量方法产生的大量蛋白质序列功能数据；(Ii)解决与正向无标记(PU)学习、极大的数据量、低质量/缺失数据以及 (3)对现有数据库或物理模型中的辅助信息进行编码。此外，应用在这项工作中开发的方法和算法将产生新的科学见解和工程生物系统。