CAREER: Prediction of multiscale emergent dynamics in decentralized cell populations

职业：预测分散细胞群中的多尺度新兴动态

• 批准号: 1653315
• 负责人: Neda Bagheri
• 金额: $ 50万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1653315&HistoricalAwards=false
• 关键词: CAREER; Prediction; multiscale; emergent; dynamics

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.

PI：Bagheri，NedaProposal编号：1653315拟议的工作将开发计算方法来预测细胞群体的动态。存在于细胞内和从细胞到细胞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概念将通过儿童介绍给年轻和多样化的观众？技术的进步为单个细胞信号传导和功能提供了显着的见解;它们的限制限制了对这些细胞如何在微环境中合作以产生强大的新兴细胞群体动力学的研究。计算方法可以用来填补知识的空白，但生物学的复杂性需要越来越复杂的框架，我们的领域还没有开发出一个完全集成的，多尺度的，多类的异质模型，可以适应无数的情况下预测细胞群的出现。该项目提供了这样一个框架，其中大规模的动力学通过基于预测代理的模型从个体自主细胞决策中产生。我们的模型包括细胞内和细胞间信号传导、细胞类型（健康细胞和癌细胞）和状态（例如，增殖性、静止性、迁移性等）、营养物质的代谢以及物理取向和限制。它将是第一个将这些生物化学和物理反应整合在一个框架中的模型之一，以预测微环境中的出现。鉴于其可推广和灵活的框架，来自所有学科的人都可以在涌现中找到熟悉的东西，为讨论和研究提供了宝贵的跨学科机会。这种模式的可获得性还将使关于复杂性和涌现性的先进原则能够融入教育材料。除了课程开发，STEM概念将通过儿童介绍给年轻和多样化的受众？在教科书中（附有实践练习）强调妇女和代表性不足的少数民族对该领域的贡献。这项工作将涉及STEM和非STEM领域的学生合作，以推进青年教学和学习的最佳做法。通过使STEM主题更熟悉，更少程序化，下一代学生将被引导对计算机科学，机器学习，复杂性和生物学的基本理解。这个职业生涯建议支持多学科的研究机会，以促进对复杂生物系统的理解，并促进相关发现整合到可访问的故事和演示分发给广大观众。