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中文摘要
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描述(由申请人提供):细胞非常擅长检测和跟踪感兴趣的化学物质的浅梯度。微生物沿着梯度寻找食物或配偶,类似的过程也存在于神经系统的轴突引导、免疫细胞向入侵者的归巢、修复细胞向受伤部位的爬行以及受孕期间精子向卵子的引导。梯度跟踪也有助于癌症的转移,因此了解细胞如何跟踪浅化学梯度具有医学相关性和根本兴趣。当通过细胞表面受体检测到化学物质时,细胞要么向信号源移动,要么向信号源生长。在许多情况下,扩散物质的梯度很浅,导致小细胞直径上的浓度差异很小。单个受体-配体相互作用的随机性使梯度检测变得更加困难,这导致分子噪声可以掩盖微小的空间梯度信号。尽管有噪音,但细胞能够有效追踪非常浅的梯度的机制尚不清楚。在本提案中,我们使用独特的可处理酵母模型系统来研究这些机制。在交配过程中,酵母细胞极化并生长出一个信息素梯度,以寻找异性伴侣并与之融合。我们建议结合尖端显微镜、遗传学和计算模型来了解酵母细胞是如何跟踪信息素梯度的。
英文摘要
DESCRIPTION (provided by applicant): Cells are extraordinarily adept at detecting and tracking shallow gradients of chemicals of interest. Micro-organisms track gradients to find food or mates, and similar processes underlie axon guidance in the nervous system, homing of immune cells towards invaders, crawling of repair cells towards wound sites, and guidance of sperm towards the egg during conception. Gradient tracking also contributes to metastasis in cancer, so understanding how cells track shallow chemical gradients is of medical relevance as well as fundamental interest. Upon detecting chemicals through cell-surface receptors, cells either move or grow towards the source of the signal. In many cases, the gradients of diffusible substances are shallow, resulting in minuscule concentration differences across the diameter of small cells. Gradient detection is made even more difficult by the randomness of individual receptor-ligand interactions, which leads to molecular noise that can mask the tiny spatial gradient signal. The mechanisms that allow cells to efficiently track even very shallow gradients despite noise are poorly understood. In this proposal, we use the uniquely tractable yeast model system to investigate these mechanisms. During mating, yeast cells polarize and grow up a gradient of pheromone to find and fuse with opposite-sex partners. We propose to use a combination of cutting-edge microscopy, genetics, and computational modeling to understand how it is that yeast cells track pheromone gradients.
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Predoctoral Training Program in Bioinformatics and Computational Biology
Predoctoral Training Program in Bioinformatics and Computational Biology
Predoctoral Training Program in Bioinformatics and Computational Biology
Predictive Modeling of the EGFR-MAPK pathway for Triple Negative Breast Cancer Patients
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