Core 2: Ecological Core

摘要-生态核心 空间“组学”的最新进展导致人们对研究和预测突发事件的兴趣增加 聚集行为和肿瘤与其微环境之间的相互作用。肿瘤是动态的 快速发展和经历生态转变;因此，对肿瘤进展的理解， 随着时间的推移，研究生态进化的相互作用将有利于对治疗的反应。调查 肿瘤免疫微环境中的空间和时空相互作用需要计算 方法来确定和调查的多样性生态模式，出现凭借这些复杂的 动力学生态分析核心将迎合空间的生成，调查和应用。 生态模型和技术，以支持两个非小细胞肺癌（NSCLC）重点项目， 这个提议鉴于空间和/或时间上的生态变化（Δ-生态学）是一个共同的中心主题， 通过这两个项目，核心准备支持和定制的工具，以满足每个项目的要求。的 核心将具备管理、可视化、存储、共享和执行定量分析的能力 高维度和高分辨率的临床和实验组织学图像。我们还将建立 重要的计算方法和发展框架，以提高我们的理解和 使用图像预测紧急肿瘤行为。总之，核心将开发和应用空间 生态模型与数学模型相结合，从数学建模的核心来理解 肿瘤异质性的多样性和维持的进化生态学，以及这是如何 用于指导肺癌的新治疗策略。

Summary – Ecological Core Recent advances in spatial ‘omics’ have led to an increased interest in studying and predicting emergent aggregate behavior and interactions between the tumor and its microenvironment. Tumors are dynamic, rapidly evolving and undergoing ecological shifts; therefore, the understanding of tumor progression and response to therapy will benefit from studying eco-evolutionary interactions over time. Investigation of the spatial and spatiotemporal interactions within the tumor immune microenvironment requires computational approaches to identify and investigate the diverse ecological patterns that emerge by virtue of these complex dynamics. The Ecological Analysis Core will cater to the generation, investigation, and application of the spatial ecological models and techniques to support the two non-small cell lung cancer (NSCLC)-focused Projects of the proposal. Given that ecological change over space and/or time (the Δ-Ecology) is a central theme shared by both Projects, the Core is poised to support and tailor the tools to the requirements of each Project. The Core will have the capability to the ability to manage, visualize, store, share, and perform quantitative analyses of the high-dimensional and high-resolution clinical and experimental histology images. We will also build important computational approaches and developmental frameworks to improve our understanding and prediction of emergent tumor behavior using the images. In summary, the Core will develop and apply spatial ecological models in concert with mathematical models from the Mathematical Modeling Core to understand the evolutionary ecology underlying the diversity and maintenance of tumor heterogeneity, and how this can be leveraged to guide new therapeutic strategies for lung cancer.