A Predictive Modeling Framework to Dissect the Dynamic Immunometabolic Responses to Pathogenic infection and the Kinetic Reprogramming of Metabolism in Cancer Cell System

细胞代谢正在成为控制免疫反应及其对机体影响的关键因素。 病原体。此外，最近的研究指出异常代谢在控制 任何形式癌症的遗传和表观遗传细胞现象。因此，研究动态代谢 病原体感染和时间“反应组学”（定义为反应的组合）导致免疫细胞发生变化 机制、调节和动力学参数）以及肿瘤细胞的相关脆弱性存在巨大的 开发新治疗方法的潜力。虽然现有的免疫细胞多尺度模型尝试 为了弥合多个尺度（即分子到器官水平）之间的差距，现有的方法都无法 同时通过建立适当的、预测性的“全面”模型来做到这一点。此外，是否或在何种程度上 宿主免疫系统中发生的代谢变化仍不清楚。对于癌细胞，一些关键的 挑战包括定义系统级细胞代谢表型和跟踪时间变化 在反应组学中，这对于将细胞代谢恢复到更健康的状态至关重要。在此，PI Saha 提出 开发并迭代改进系统级、全面、综合的代谢模型 框架：i）剖析与致病性相关的免疫代谢反应的动态变化 感染，ii) 研究与代谢重编程相关的时间反应组学的变化 在特定的癌细胞中。拟议的研究计划将利用计算的独特组合 Saha 实验室的建模技巧和丰富的研究经验对于表征代谢至关重要 与任何疾病相关的现象。他的研究团队最近开发了第一个计算 易于处理且准确的建模框架，用于跟踪细胞代谢的时间动态，并且 建立了一种新方法来估计细胞中涉及的每个代谢反应的反应组学 当“组学”数据集不完整或缺失时系统，从而开发预测动力学模型 框架。因此，所提出的建模框架可以潜在地研究代谢动力学 与与病原体或时间反应组相互作用的细胞簇（例如免疫细胞）相关 特定细胞（例如癌细胞）。作为第一步，萨哈将研究特定区域的动态代谢变化 SARS-Cov-2 和金黄色葡萄球菌感染后的免疫细胞类型（即巨噬细胞）和颞叶 胰腺导管腺癌 (PDAC) 细胞代谢的重编程和反应组学并测试 假设这些变化的程度会导致疾病症状的严重程度。总体而言， 提出的框架以及相关的“预测组”数据库（包含关键的预测 基因/蛋白质/反应发挥关键作用）将提供更广泛的科学界，包括分子 对这一作用有基本了解的生物学家、计算生物学家、临床医生和转化科学家 代谢在决定疾病严重程度方面的作用，也是研究其他疾病的有用模板。

Cellular metabolism is emerging as a critical factor to control the immune responses and their impact on the pathogens. In addition, recent studies pinpoint a more prominent role of the aberrant metabolism in controlling both genetic and epigenetic cellular phenomena of any form of cancer. Thus, investigating the dynamic metabolic shift in immune cells upon pathogenic infection and temporal ‘reactomics’ (defined as a combination of reaction mechanisms, regulations, and kinetic parameters) and associated vulnerabilities of tumor cells holds immense potential to develop novel therapeutic approaches. While the existing multi-scale modeling of immune cells tries to bridge the gap between multiple scales (i.e., molecular to organ-level), none of the existing approaches can simultaneously do that by building a proper, predictive ‘full-scale’ model. Furthermore, whether or to what extent metabolic shifts occur in the host’s immune system is still not known. In case of cancer cell, some of the critical challenges include defining the systems-level cellular metabolic phenotype and tracking the temporal changes in reactomics which are critical for reverting the cell metabolism to more healthy state. Herein, PI Saha proposes to develop and iteratively improve a systems-level, comprehensive, and integrative metabolic modeling framework: i) to dissect the dynamic shifts in the immunometabolic responses associated with pathogenic Infection, and ii) investigate the changes in temporal reactomics associated with the metabolic reprogramming in a specific cancer cell. The proposed research program will leverage the unique combination of computational modeling skills and rich research experience in Saha’s laboratory that are crucial for characterizing the metabolic phenomena associated with any disease. His research team recently developed the first computationally tractable and accurate modeling framework to track the temporal dynamics of cellular metabolism and also established a new method to estimate the reactomics of each of the metabolic reactions involved in a cellular system when ‘omics’ datasets are incomplete or missing and, thereby, develop a predictive kinetic modeling framework. Thus, the proposed modeling framework can potentially investigate the metabolic dynamics associated with a cluster of cells (e.g., immune cells) interacting with a pathogen or the temporal reactomics of a specific cell (e.g., cancer cell). As a first step, Saha will investigate the dynamic metabolic shifts in a specific type of immune cell (i.e., macrophage) upon SARS-Cov-2 and Staphylococcus aureus infection and the temporal reprogramming and reactomics of pancreatic ductal adenocarcinoma (PDAC) cell metabolism and test the hypothesis that if the degree to these changes gives rise to the severity of the disease symptoms. Overall, the proposed framework as well as the associated ‘predictome’ database (containing the predictions of key genes/proteins/reactions playing critical roles) will provide the broader scientific community including molecular biologists, computational biologists, clinicians, and translational scientists with a basic understanding of the role of metabolism in dictating disease severity and also a useful template to investigate other diseases.