Systems Biology Core

系统生物学核心

• 批准号: 10665023
• 负责人: William Evan Johnson
• 金额: $ 35.36万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 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/10665023
• 关键词: Automobile Driving; Biochemical Pathway; Bioinformatics; Biological; Biological Process; Cell physiology; Cells; Clinical; Clinical Data; Clinical Investigator; Clinical Research; Collaborations; 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; Molecular; 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; base; biological heterogeneity; biomarker signature; biomedical data science; causal variant; clinical phenotype; cohort; computerized data processing; data integration; design; disease phenotype; experimental study; falls; insight; interest; knowledgebase; lens; machine learning method; metabolomics; microbial; multimodality; network models; new technology; pandemic response; pathogen; programs; protein profiling; simulation; single-cell RNA sequencing; statistical and machine learning; synergism; 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 这个整体TBRU计划旨在了解宿主和细菌异质性如何共同作用， 促进结核病的临床表型，如传播、疾病进展和耐药性。这两 受试者（生物异质性和临床表型）是细胞内网络的新兴特性， 的多细胞相互作用，分别，使这一整体课题具有挑战性的研究，通过传统的 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以及先进的计算技术带来的生物复杂性 需要技术来阐明从越来越大的定量数据集的生物学理解 由现代先进的实验平台产生。系统生物学核心旨在满足 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 为支持本建议书中的每个项目提供服务。该核心由Evan约翰逊博士、Shuyi Ma博士和Jason博士领导 杨，每个人都在不同的计算和系统生物学分析领域拥有广泛的专业知识 方法，他们每个人都积极与来自这个TBRU的其他研究人员合作， 结核病研究项目。系统生物学核心将有助于标准化处理和分析 每个项目的数据，为每个项目生成实验可检验的假设， 跨项目连接临床表型的机制分析。该核心的两个关键优势， 将其与其他计算核心区分开来，并使该TBRU能够独特地研究临床生物标本 （i）在使用生物标志物特征（如PREDICT 29）检测早期和晚期癌症方面的广泛专业知识， 亚临床TB疾病，扩大了可以研究的临床Mtb菌株和宿主细胞的范围;和（ii） 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 生物学假设的广度和精确度，可以从每组实验数据中产生。 该TBRU的研究人员独特地开发了Mtb基因调控和代谢网络模型， 将被这个系统生物学核心用来形成宿主和结核菌细胞生理学的条件特异性模型 对应于每个项目的实验样品。这些模型不仅使核心能够 反卷积该计划中生成的大型实验数据集，但也将使核心能够直接 预测因果基因调控和代谢基因和途径机制，每一个关键的基础 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实现项目之间的直接集成，使TBRU能够 确定这些临床表型是如何在机制上联系起来的。总之，这一核心将与 每一个项目和所有项目都在机械上将宿主和病原体异质性与临床结果联系起来。