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中文摘要
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描述(由申请人提供):细胞非常善于检测和跟踪感兴趣的化学物质的浅梯度。微生物追踪梯度以寻找食物或配偶,类似的过程构成神经系统中轴突引导的基础,免疫细胞向入侵者归巢,修复细胞向伤口部位爬行,以及在受孕期间引导精子向卵子。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意义和根本利益。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,可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得梯度检测更加困难,这导致分子噪声可以掩盖微小的空间梯度信号。尽管有噪声,但允许细胞有效跟踪非常浅的梯度的机制仍然知之甚少。在这个建议中,我们使用独特的易处理的酵母模型系统来研究这些机制。在交配过程中,酵母细胞会分泌一种梯度的信息素,以寻找并与异性伴侣融合。我们建议使用尖端显微镜,遗传学和计算建模的组合来了解酵母细胞如何跟踪信息素梯度。
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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