Robust Analysis of Signal Transduction Underlying Cellular Variability in Stem Cells

干细胞细胞变异性信号转导的稳健分析

• 批准号: G1002092/1
• 负责人: Sarah Filippi
• 金额: $ 39.46万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G1002092%2F1
• 关键词: Robust; Analysis; Signal; Transduction; Underlying

At the molecular level no cell is like any other. Differences between genetically identical cells can in fact be quite pronounced, and these differences can have profound biological and biomedical implications. Stem cells divide and differentiate in response to physiological and environmental cues but exhibit considerable differences in how they do so; equally, tumour cells respond differently to drug treatment or radiation therapy. The mechanisms driving such variability among to all intents and purposes seemingly identical cells are only poorly understood. In order to understand these fundamental processes - which also have numerous biomedical and pharmacological implications - we have to overcome experimental and mathematical modelling challenges. Here we develop the necessary experimental and theoretical frameworks, which allow us to probe single cells, measure the abundances of key signalling molecules, and infer mathematical mechanistic models for signal transduction. We will then analyze these mathematical models in order to understand better how the structure of the signal transduction network influences cell-to-cell variability. Most importantly, however, we will develop novel strategies which will allow us to guide cellular decision making processes and determine patterns of differences between cells. If we manage to reduce or otherwise affect such variability then we gain a novel way to guide the behaviour of cells.

在分子水平上，没有一个细胞是和其他细胞一样的。事实上，基因相同的细胞之间的差异可能非常明显，这些差异可能具有深刻的生物学和生物医学意义。干细胞分裂和分化响应生理和环境线索，但表现出相当大的差异，他们这样做;同样，肿瘤细胞对药物治疗或放射治疗的反应不同。在所有意图和目的看似相同的细胞中，驱动这种变异性的机制知之甚少。为了理解这些基本过程-也有许多生物医学和药理学的影响-我们必须克服实验和数学建模的挑战。在这里，我们开发了必要的实验和理论框架，使我们能够探测单细胞，测量关键信号分子的丰度，并推断信号转导的数学机制模型。然后，我们将分析这些数学模型，以便更好地理解信号转导网络的结构如何影响细胞间的变异性。然而，最重要的是，我们将开发新的策略，使我们能够指导细胞决策过程并确定细胞之间的差异模式。如果我们设法减少或以其他方式影响这种变异性，那么我们就获得了一种指导细胞行为的新方法。