Systems Biology Core

系统生物学核心

• 批准号: 10493266
• 负责人: William Evan Johnson
• 金额: $ 35.78万
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10493266
• 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; knowledgebase; 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 该总体TBRU计划旨在了解宿主和细菌异质性如何共同起作用 促进结核病临床表型，例如传播，疾病进展和药物耐受性。这两个 受试者（生物异质性和临床表型）是细胞内网络的新兴特性， 分别进行多细胞相互作用，使这一总体主题挑战于传统的研究 假设驱动的研究方法。需要进行系统级分析以说明 细胞网络和多细胞相互作用和高级计算施加的生物复杂性 需要技术来阐明越来越大的定量数据集中的生物学理解 由现代高级实验平台生成。系统生物学核心旨在满足这两种 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 在此提案中支持每个项目的服务。该核心由Drs领导。埃文·约翰逊（Evan Johnson），苏伊（Shuyi MA）和杰森（Jason） Yang，每个都有广泛的主题和系统生物学分析方面的广泛主题专业知识 方法，每个人都积极与该TBRU的其他研究人员合作就多样化 结核病研究项目。系统生物学核心将有助于标准化的处理和分析 来自每个项目的数据，每个项目的实验可检验的假设以及综合性 对跨项目连接临床表型的机制的分析。这个核心的两个关键优势 将其与其他计算核心区分开，这使该TBRU能够唯一研究临床生物测量 是：（i）使用生物标志物特征（例如Predict29）检测初期和 亚临床结核病，扩大了可以研究的临床MTB菌株和宿主细胞的范围； （ii） 多尺度蜂窝网络建模和可解释的机器学习方面的广泛专业知识，扩展 可以从每组实验数据产生的生物学假设的广度和精度。 该TBRU中的研究人员已独特地开发了MTB基因调节和代谢网络模型，这些模型 该系统生物学核心将使用该系统核心形成宿主和MTB细胞生理的条件特异性模型 对应于每个项目的实验样本。这些模型不仅会使核心能够 解析此程序中生成的大型实验数据集，但也将使核心能够直接 预测因果基因调节基因和代谢基因以及途径机制，这些机制是每个关键的基础 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。这些模型和分析将使项目之间的直接集成，从而使该TBRU能够 确定这些临床表型如何机械上的联系。在一起，这个核心将与 每个项目都可以机械地桥接宿主和病原体异质性，并具有临床结果。