Development of data driven and AI empowered systems biology to study human diseases

数据驱动和人工智能的发展使系统生物学能够研究人类疾病

• 批准号: 10714763
• 负责人: Chi Zhang
• 金额: $ 39.23万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714763
• 关键词: Address; Biochemical; Biological; Biological Markers; Biological Process; Biology; Biomedical Research; Cell physiology; Cells; Clinical; Communities; Complex; Computing Methodologies; Data; Data Analyses; Dependence; Development; Differential Equation; Disease; Disease Management; Disease Progression; Early Diagnosis; Endowment; Foundations; Genetic Annotation; Genetic Variation; Goals; Heterogeneity; Infrastructure; Knowledge; Literature; Mathematics; Metabolic; Metabolic Pathway; Metabolism; Methodology; Methods; Modeling; Molecular; Multiomic Data; Natural Language Processing; Pathology; Phenotype; Prevention; Recommendation; Research; Role; Sampling; Signal Pathway; Structure; System; Systems Biology; Tissues; Transcriptional Regulation; Validation; Variant; advanced system; computational suite; computer framework; computerized tools; drug repurposing; dynamic system; empowerment; epigenetic regulation; human disease; information organization; new therapeutic target; novel; nutrition; precision medicine; theories; transcriptomics

Project Summary Systems biology models provide an effective way to study the functional impact of biological process within complex disease system. Despite a plethora of knowledge on the differential equation-based systems biology model have gained, there are still major gaps in raising dynamic models within the context of human diseases. Essentially, the parameters involved in the non-linear dependencies are largely unknown under disease conditions and the systems biology models are always within a reductionist paradigm, which can hardly characterize the complicated disease system. The large amount of single-cell, spatial or tissue multi-omics data obtained from disease tissue has been proven to be endowed with the potential to deliver information on a cell functioning state and its underlying phenotypic switches. Hence, advanced systems biology models and computational tools are in pressing need to empower reliable characterization of biological processes and their functional roles in disease by using multi-omics data. Our preliminary data include (1) a new computational method to approximate systems biology model using transcriptomics data, and (2) computational principles to approximate dynamic system by using omics data, which form the methodology and theoretical foundations of this project. In this MIRA project, I proposed to develop a suite of novel computational methods, systems biology models and quantitative metrics to bring the following unmet capabilities: (1) A computational framework to establish dynamic models using omics data, which will enable the following analyses to study a complex disease system: (i) assessing sample-wise activity of biological processes; (ii) perturbation analysis to evaluate the impacts of biological features or model structures to the system, which could serve as new drug targets, and (iii) evaluating how the system evolve through disease progression; (2) A natural language processing-based extraction of biological functions and relations to automatically establish context specific knowledge of system structure and components from scientific literature datal; and (3) computational principles and theories of the identifiability and mathematical representation of dynamic systems in omics data. By implementing these methods into multi-omics data analysis, we plan to address the following outstanding biological questions: (i) identification of molecular features with high impact to metabolic variations in different diseases, (ii) the role of metabolism in fueling epigenetic regulation, (iii) transcriptional regulation of metabolism and other biological processes, (iv) functional annotation of genetic variations, and (v) assessment of biochemical variations. We will also develop novel knowledge representation and transfer of metabolic and other variations in pan-disease analysis to aid in better understanding of the basic disease pathology and promote the precision medicine research, including prediction and validation of new biomarkers, nutrition recommendation, and drug repurposing. Successful execution of the proposed research will provide a suite of computational capabilities to quantify and study general biological processes that could be broadly utilized by the biomedical research community.

项目概要 系统生物学模型提供了一种有效的方法来研究生物过程的功能影响 复杂的疾病系统。尽管对基于微分方程的系统生物学有大量的了解 模型已经取得了进展，但在人类疾病背景下建立动态模型仍然存在重大差距。 本质上，涉及非线性依赖性的参数在疾病状态下很大程度上是未知的 条件和系统生物学模型始终处于还原论范式内，这很难 描述复杂疾病系统的特征。大量的单细胞、空间或组织多组学数据 从疾病组织中获得的物质已被证明具有传递细胞信息的潜力 功能状态及其潜在的表型转换。因此，先进的系统生物学模型和 迫切需要计算工具来可靠地表征生物过程及其 使用多组学数据研究疾病中的功能作用。我们的初步数据包括（1）一个新的计算 使用转录组数据近似系统生物学模型的方法，以及（2）计算原理 利用组学数据近似动态系统，形成了方法论和理论基础 这个项目。在这个 MIRA 项目中，我提议开发一套新颖的计算方法，系统生物学 模型和定量指标带来以下未满足的功能：（1）计算框架 使用组学数据建立动态模型，这将使​​以下分析能够研究复杂的疾病 系统：（i）评估生物过程的样本活动； (ii) 扰动分析来评估 生物特征或模型结构对系统的影响，可以作为新的药物靶标，以及（iii） 评估系统如何随着疾病进展而演变； (2)基于自然语言处理 提取生物功能和关系以自动建立系统的上下文特定知识 科学文献数据的结构和组成部分； (3) 计算原理和理论 组学数据中动态系统的可识别性和数学表示。通过实施这些 方法进入多组学数据分析，我们计划解决以下突出的生物学问题：（i） 鉴定对不同疾病的代谢变化具有高度影响的分子特征，(ii) 代谢促进表观遗传调控，（iii）代谢和其他生物的转录调控 过程，（iv）遗传变异的功能注释，以及（v）生化变异的评估。我们将 还开发新的知识表示以及泛疾病代谢和其他变异的转移 分析有助于更好地了解基础疾病病理，促进精准医疗 研究，包括新生物标志物的预测和验证、营养建议和药物再利用。 拟议研究的成功执行将提供一套计算能力来量化和 研究可以被生物医学研究界广泛利用的一般生物过程。