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

项目摘要 系统生物学模型为研究生物过程的功能影响提供了一种有效的途径 复杂的疾病系统。尽管关于基于微分方程式的系统生物学有太多的知识 尽管模型已经取得进展，但在人类疾病的背景下提出动态模型方面仍存在重大差距。 本质上，在疾病情况下，非线性依赖关系中涉及的参数在很大程度上是未知的 条件和系统生物学模型总是在简化论的范式内，这几乎不可能 描述复杂的疾病系统。大量的单细胞、空间或组织多组学数据 从疾病组织中提取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开关。因此，先进的系统生物学模型和 迫切需要计算工具来使生物过程和它们的 利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 利用转录学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力学系统，这构成了研究的方法论和理论基础 这个项目。在这个Mira项目中，我提议开发一套新的计算方法，系统生物学 模型和量化指标带来了以下未满足的能力：(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：(1)评估生物过程的抽样活动；(2)扰动分析，以评估 生物学特征或模型结构对系统的影响，可作为新的药物靶点；以及(Iii) 评估系统如何在疾病进展中进化；(2)基于自然语言处理的 提取生物功能和关系以自动建立系统的上下文特定知识 来自科学文献数据的结构和组成部分；以及(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 方法在多组学数据分析中，我们计划解决以下突出的生物学问题：(I) 识别对不同疾病的代谢变化有很大影响的分子特征，(Ii) 新陈代谢促进表观遗传调节，(Iii)新陈代谢和其他生物的转录调节 (4)遗传变异的功能注释，以及(5)生化变异的评估。我们会 还开发了泛疾病中代谢和其他变量的新知识表示和转移 分析有助于更好地了解基础疾病病理，促进精准医学 研究，包括新生物标志物的预测和验证、营养推荐和药物再利用。 拟议研究的成功执行将提供一套计算能力来量化和 研究可被生物医学研究界广泛利用的一般生物学过程。

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.