Learning the Rules of Collective Cell Migration

学习集体细胞迁移的规则

• 批准号: 2601485
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601485
• 关键词: Learning; Rules; Collective; Cell; Migration

Collective cell migration is important for a plethora of biological processes, including cancer metastasis, organogenesis, and wound healing. However, the biological mechanisms underpinning collective cell movement are still poorly understood. It is, therefore, desirable to better understand the mechanisms of collective migration, such that therapies for diseases resulting from misdirected or uncontrolled migration can be prevented. In this project, in vivo cell migration data from chick cranial neural crest will be used in tandem with high throughput in vitro epithelial cell wound healing data to probe the biological mechanisms underpinning collective cell migration. Efforts will be focussed on the formulation and simulation of agent-based mathematical models of migration, the outputs of which can be compared with available data to elucidate the mechanisms responsible for in vivo observations and constrain the associated biological parameters. These models can subsequently be coarse-grained to form continuum equivalents that are amenable to detailed mathematical analysis. A particular focus will be placed on the prevailing theory of leader-follower dynamics in the cranial neural crest due to the vast body of experimental evidence supporting this hypothesis and the findings of prior mathematical models adopting this framework. This work will aim to develop the findings of previous models by considering additional interaction mechanisms observed in vivo that are poorly understood, such as extracellular matrix degradation, spatially non-uniform growth of the migratory domain, and cell confinement from factors expressed within the neural crest environment. Work concerning the neural crest will be complemented by parameter estimation and model validation utilising data from experimentally tractable wound healing assays to produce models of collective migration in which parameters are known to a high degree of accuracy, further increasing the predictive capacity of models.

集体细胞迁移对于许多生物过程是重要的，包括癌症转移、器官发生和伤口愈合。然而，支持集体细胞运动的生物学机制仍然知之甚少。因此，需要更好地了解集体移徙的机制，以便可以预防因错误或不受控制的移徙而导致的疾病的治疗。在这个项目中，从鸡颅神经嵴的体内细胞迁移数据将被用于串联高通量的体外上皮细胞伤口愈合数据，以探讨集体细胞迁移的生物学机制。努力将集中在制定和模拟的代理为基础的数学模型的迁移，其输出可以与现有的数据进行比较，以阐明负责在体内观察和约束相关的生物参数的机制。这些模型随后可以被粗粒度化，以形成适合于详细数学分析的连续等效物。一个特别的重点将放在领导者-追随者的动态在颅神经嵴由于大量的实验证据支持这一假设和先前的数学模型，采用这一框架的调查结果的流行理论。这项工作的目的是通过考虑在体内观察到的其他相互作用机制，如细胞外基质降解，空间不均匀的迁移结构域的生长，以及神经嵴环境中表达的因子的细胞限制，来发展以前模型的研究结果。有关神经嵴的工作将得到补充的参数估计和模型验证，利用实验上易于处理的伤口愈合试验的数据，以产生集体迁移的模型，其中参数是已知的高度准确性，进一步提高模型的预测能力。