CAREER: Prediction of multiscale emergent dynamics in decentralized cell populations

PI: Bagheri， neda提案号：1653315提议的工作将发展计算方法来预测细胞群体的动态。存在于细胞内和细胞间的信号将被数学建模，以预测细胞、组织和肿瘤微环境中的出现，特别强调乳腺癌肿瘤。此外，STEM概念将通过儿童向年轻和不同的受众介绍。在美国的教科书中，有一些实践练习，突出了女性和未被充分代表的少数民族对这一领域的贡献。技术的进步提供了对单个细胞信号传导和功能的深刻见解；它们的限制限制了这些细胞如何在微环境中合作以产生稳健的涌现细胞群体动态的研究。计算方法可以用来填补知识上的空白，但生物复杂性需要越来越复杂的框架，我们的领域尚未开发出一个完全集成的、多尺度的、多类别的异质模型，可以适应无数的环境来预测细胞群体的出现。该项目提供了这样一个框架，通过基于预测代理的模型，从个体自主细胞决策中产生大规模动态。我们的模型包括细胞内和细胞间信号、细胞类型（健康细胞和癌细胞）和状态（如增殖、静止、迁移等）的异质性、营养物质的代谢以及物理方向和约束。这将是第一个将这些生化和物理反应整合在一个单一框架中的模型之一，以预测微环境中的出现。鉴于其可概括和灵活的框架，来自所有学科的人都可以在涌现中找到熟悉感，为讨论和研究提供了宝贵的跨学科机会。这种模型的可访问性也将使关于复杂性和涌现的先进原则能够被编织到教育材料中。除了课程开发之外，STEM概念还将通过儿童向年轻和不同的受众介绍。美国的教科书（带有实践练习）强调了女性和未被充分代表的少数民族对该领域的贡献。这项工作将涉及来自STEM和非STEM领域的学生的合作，以促进青年教学的最佳实践。通过使STEM主题更熟悉，更少程序化，下一代学生将对计算机科学，机器学习，复杂性和生物学有基本的了解。该CAREER提案支持多学科研究机会，以促进对复杂生物系统的理解，并促进将相关发现整合到可访问的故事和演示中，分发给广大受众。

PI: Bagheri, NedaProposal No: 1653315The proposed work will develop computational approaches to predict dynamics of cell populations. Signaling present within a cell and from cell-to-cell will be mathematically modeled to predict emergence in cellular, tissue and tumor microenvironments, with special emphasis on breast cancerous tumors. Additionally, STEM concepts will be introduced to young and diverse audiences through children?s textbooks with hands-on exercises that highlight the contribution of women and underrepresented minorities to the field.Advances in technology offer remarkable insights into individual cell signaling and function; their constraints limit investigation of how these cells cooperate within the microenvironment to produce robust emergent cell population dynamics. Computational approaches can be used to fill gaps in knowledge but biological complexity demands increasingly sophisticated frameworks, and our field has yet to develop a fully integrated, multi-scale, multiclass heterogeneous model that can be adapted to countless contexts to predict emergence of cell populations. This project offers such a framework where large-scale dynamics arise from individual autonomous cell decisions through a predictive agent based model. Our model includes intra-and intercellular signaling, heterogeneity of cell types (healthy and cancer cells) and states (e.g., proliferative, quiescent, migratory, and others), metabolism of nutrients, and physical orientation and constraints. It will be one of the first models to integrate these biochemical and physical responses in a single framework to predict emergence in the microenvironment. Given its generalizable and flexible framework, people from all disciplines can find familiarity in emergence, providing invaluable cross-disciplinary opportunities for discussion and research. The accessibility of such a model will also enable advanced principles on complexity and emergence to be woven into educational material. In addition to curriculum development, STEM concepts will be introduced to young and diverse audiences through children?s textbooks (with hands-on exercises) that highlight the contribution of women and underrepresented minorities to the field. This effort will involve the collaboration of students from STEM and non-STEM fields to advance best practices of teaching and learning for youth. By making STEM topics more familiar and less procedural, the next generation of students will be guided with a basic understanding of computer science, machine learning, complexity, and biology. This CAREER proposal supports multi-disciplinary research opportunities to catalyze understanding of complex biological systems and facilitate integration of related findings into accessible stories and demonstrations distributed to broad audiences.