CAREER: Learning and teaching how to program morphogenesis with synthetic genetic circuits, a case study for elongation of mammalian cell spheroids

职业：学习和教授如何利用合成遗传电路对形态发生进行编程，这是哺乳动物细胞球体伸长的案例研究

• 批准号: 2145528
• 负责人: Leonardo Morsut
• 金额: $ 70.85万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145528&HistoricalAwards=false
• 关键词: CAREER; Learning; teaching; how; program

Growing organs and multicellular machines in the lab is at the leading edge of regenerative medicine. However, controlling the shape of lab-grown materials is elusive. Creating a computational framework that can help design control networks to drive groups of cells to self-assemble into elongated structures is the primary objective of this project. Supporting curriculum and teacher development is another objective. Presenting workshops and training teachers in synthetic biology will help cultivate interest in STEM fields among under-represented high school students from local neighborhoods.The primary objective is to rationally design and autonomously manufacture axially elongated, three-dimensional tissues, organs and biobots. The central hypothesis is that artificial genetic circuits, based on synthetic Notch (synNotch) contact-dependent cell-cell signaling, could be designed and optimized in silico to control axial elongation of in vitro grown multicellular structures, when combined with control of adhesion and proliferation. A validated platform for computational modeling based on an augmented Cellular Potts model will be used to develop genetic programs. These programs will be implemented in fibroblasts. Quantitative fluorescent imaging will indicate their capacity to sustain elongation. The validated, predictive computational platform developed during this project will be available for use on a broader range of shapes and structures. Identification of genetic drivers of axial elongation will increase our understanding of normal developmental processes and provide insights into network malfunctions that may play a role in the pathophysiology of certain birth defects as well as advancing control of in vitro-grown tissues for applications in regenerative medicine and soft robotics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在实验室中培养器官和多细胞机器是再生医学的前沿。然而，控制实验室生长材料的形状是难以捉摸的。该项目的主要目标是创建一个计算框架，帮助设计控制网络，以驱动细胞群自组装成细长结构。支持课程和教师发展是另一个目标。 举办合成生物学研讨会和培训教师将有助于培养当地社区代表性不足的高中生对STEM领域的兴趣。主要目标是合理设计和自主制造轴向细长的三维组织，器官和生物机器人。 核心假设是，人工遗传电路，基于合成的Notch（synNotch）接触依赖性细胞-细胞信号传导，可以设计和优化在计算机上控制轴向伸长体外生长的多细胞结构，当结合控制的粘附和增殖。一个经过验证的平台，计算建模的基础上增强细胞波茨模型将被用来开发遗传程序。这些程序将在成纤维细胞中实施。定量荧光成像将指示它们维持伸长的能力。该项目开发的经过验证的预测计算平台将可用于更广泛的形状和结构。轴向伸长的遗传驱动因素的鉴定将增加我们对正常发育过程的理解，并提供对可能在某些出生缺陷的病理生理学中发挥作用的网络故障的见解，以及推进体外发育的控制。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用的支持，基金会的学术价值和更广泛的影响审查标准。