Systems Biology Core

系统生物学核心

• 批准号: 10271647
• 负责人: William Evan Johnson
• 金额: $ 38.47万
• 依托单位: RBHS-NEW JERSEY MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10271647
• 关键词: Automobile Driving; Biochemical Pathway; Bioinformatics; Biological; Biological Process; Cell physiology; Cells; Clinical; Clinical Data; Clinical Investigator; Clinical Research; Computational Biology; Computational Technique; Computer Models; Data; Data Analyses; Data Set; Disease; Disease Progression; Drug Tolerance; Ensure; Etiology; Gene Expression Regulation; Generations; Genes; Genetic Transcription; Heterogeneity; Human; Immunologist; Individual; Investigation; Laboratory Study; Link; Machine Learning; Metabolic; Metabolism; Modeling; Modernization; Mus; Mycobacterium tuberculosis; Outcome; Pathway interactions; Phenotype; Physiology; Process; Property; Recommendation; Regulator Genes; Research; Research Design; Research Personnel; Research Project Grants; Resolution; Role; Sampling; Serum Proteins; Service delivery model; Services; Standardization; System; Systems Biology; Tuberculosis; Validation; Work; Yang; base; bioinformatics network; biological heterogeneity; biomarker signature; biomedical data science; causal variant; clinical phenotype; cohort; computerized data processing; design; disease phenotype; experimental analysis; experimental study; falls; insight; interest; knowledge base; lens; machine learning method; metabolomics; microbial; molecular phenotype; multimodality; network models; new technology; pandemic disease; pathogen; programs; protein profiling; simulation; single-cell RNA sequencing; statistical and machine learning; transcriptome sequencing; transmission process

SYSTEMS BIOLOGY CORE ABSTRACT This overall TBRU Program seeks to understand how both host and bacterial heterogeneity act together to promote TB clinical phenotypes such as transmission, disease progression, and drug tolerance. These two subjects (biological heterogeneity and clinical phenotypes) are emergent properties of intracellular networks and of multicellular interactions, respectively, rendering this overall topic challenging to study by conventional hypothesis-driven research approaches. Systems-level analyses are required in order to account for the biological complexity imposed by cellular network and multicellular interactions and advanced computational techniques are required to elucidate biological understanding from the increasingly large quantitative datasets generated by modern advanced experimental platforms. The Systems Biology Core is designed to meet both of these needs, providing advanced bioinformatics, biomedical data science, and network modeling analysis services to support each Project in this Proposal. This Core is led by Drs. Evan Johnson, Shuyi Ma, and Jason Yang, each with extensive subject-matter expertise in diverse computational and systems biology analytical approaches, and each of whom actively collaborates with other investigators from this TBRU on diverse tuberculosis research projects. The Systems Biology Core will aid in the standardized processing and analysis of data from each Project, generation of experimentally testable hypotheses for each Project, and integrative analyses of mechanisms connecting clinical phenotypes across Projects. Two key strengths of this Core that differentiate it from other computational cores and that enable this TBRU to uniquely study clinical biospecimens are: (i) the extensive expertise in using biomarker signatures such as PREDICT29 to detect incipient and subclinical TB disease, expanding the range of clinical Mtb strains and host cells that can be studied; and (ii) the extensive expertise in multiscale cellular network modeling and interpretable machine learning, expanding the breadth and precision of biological hypotheses that can be generated from each set of experimental data. Investigators in this TBRU have uniquely developed Mtb gene regulatory and metabolic network models, which will be used by this Systems Biology Core to form condition-specific models of host and Mtb cell physiology corresponding to experimental samples for each Project. These models will not only enable the Core to deconvolve the large experimental datasets generated in this Program, but will also enable the Core to directly predict causal gene regulatory and metabolic gene and pathway mechanisms that underlie each of the key clinical phenotypes studied from these clinical samples: TB transmission, disease progression and drug tolerance. These models and analyses will enable direct integration between Projects, allowing this TBRU to determine how these clinical phenotypes may be mechanistically linked. Together, this Core will synergize with each and with all Projects to mechanistically bridge host and pathogen heterogeneity with clinical outcomes.

系统生物学核心摘要 整个TBRU计划试图了解宿主和细菌异质性是如何共同作用的 促进结核病传播、疾病进展、耐药等临床表型。这两个 受试者(生物异质性和临床表型)是细胞内网络和 分别对多细胞相互作用进行了研究，使这一总体主题具有挑战性，难以通过常规方法进行研究 以假设为导向的研究方法。需要进行系统级别的分析，以说明 蜂窝网络和多细胞相互作用以及高级计算带来的生物复杂性 需要技术来从日益庞大的定量数据集中阐明生物学理解 由现代先进的实验平台产生。系统生物学核心旨在满足以下两个方面 这些需求，提供先进的生物信息学、生物医学数据科学和网络建模分析 支持本建议书中每个项目的服务。这一核心由埃文·约翰逊博士、马淑仪博士和杰森博士领导 每个人都在不同的计算和系统生物学分析方面拥有丰富的主题专业知识 方法，每个人都积极地与该TBRU的其他调查人员在不同的 结核病研究项目。系统生物学核心将协助标准化处理和分析 来自每个项目的数据，为每个项目生成可通过实验验证的假设，以及 跨项目连接临床表型的机制分析。此核心的两个关键优势是 使其有别于其他计算核心，并使该TBRU能够独特地研究临床生物谱系 是：(I)在使用生物标记物签名，如PREDICT29来检测早期和 亚临床结核病，扩大可研究的临床结核分枝杆菌菌株和宿主细胞的范围；以及 在多尺度蜂窝网络建模和可解释机器学习方面拥有丰富的专业知识，扩展了 可以从每组实验数据中产生的生物学假说的广度和精度。 该实验室的研究人员开发了独特的结核分枝杆菌基因调控和代谢网络模型， 将被该系统生物学核心用来形成宿主和结核分枝杆菌细胞生理学的特定条件模型 与每个项目的实验样本相对应。这些型号不仅将使Core能够 解除本计划中生成的大型实验数据集的卷积，但也将使Core能够直接 预测构成每个关键因素的因果基因、调节和代谢基因及途径机制 从这些临床样本中研究的临床表型：结核病传播、疾病进展和药物 宽容。这些模型和分析将实现项目之间的直接集成，从而使该TBRU能够 确定这些临床表型可能是如何机械联系的。共同努力，这一核心将与 每个项目都与所有项目一起，将宿主和病原体的异质性与临床结果机械地联系起来。