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

细胞新陈代谢正在成为控制免疫反应的关键因素及其对 病原体。此外，最近的研究指出，异常代谢在控制中的作用更为突出。 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 免疫细胞在病原性感染和临时性“反应”(定义为反应的组合)时的转移 机制、规则和动力学参数)和与肿瘤细胞相关的脆弱性 开发新的治疗方法的潜力。而现有的免疫细胞多尺度模型试图 为了弥合多个尺度之间的差距(即从分子到器官水平)，现有的方法中没有一种方法可以 同时，通过建立一个适当的、可预测的“全规模”模型来实现这一点。此外，无论是在多大程度上， 宿主免疫系统中发生的新陈代谢变化尚不清楚。在癌细胞的情况下，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 在使细胞新陈代谢恢复到更健康状态的关键反应中。在此，皮萨哈提议 开发和迭代改进系统级的、全面的、综合的代谢模型 框架：i)剖析与致病相关的免疫代谢反应的动态变化 感染，以及ii)调查与代谢重编程相关的颞叶反应的变化 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 萨哈实验室的建模技能和丰富的研究经验，对表征新陈代谢至关重要 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机化的 易于处理和准确的建模框架，用于跟踪细胞新陈代谢的时间动力学，还 建立了一种新的方法来估计细胞中涉及的每个代谢反应的反应物 数据集不完整或缺失时的系统，并由此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 与病原体相互作用的一群细胞(例如，免疫细胞)或与病原体的颞叶反应有关的 一种特定的细胞(如癌细胞)。作为第一步，SAHA将研究特定环境中的动态代谢变化 SARS-CoV-2和金黄色葡萄球菌感染的免疫细胞类型(即巨噬细胞)及其时间 胰腺导管腺癌(PDAC)细胞代谢的重编程和反应性研究 假设如果这些变化的程度引起了疾病症状的严重程度。总体而言， 拟议的框架以及相关的‘预测组’数据库(包含对关键字的预测 发挥关键作用的基因/蛋白质/反应)将为包括分子在内的更广泛的科学界提供 对角色有基本了解的生物学家、计算生物学家、临床医生和翻译科学家 新陈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.