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中文摘要
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描述(由申请人提供):我的主要研究目标是基于进化理论的方法来破译发育中的转录控制。发育生物学中一个长期存在的问题是确定多细胞生物发育过程中非常强大的事件网络的遗传基础。线虫C. elegans在这方面提供了一个方便的模型,展示了细胞自主和细胞-细胞通信的发展模式。虽然这种生物的高度均匀的细胞谱系已经得到了很好的研究,揭示了许多主要的调节因子,但在整个发育过程中,对参与基因的限制知之甚少。我们假设,将野生秀丽隐杆线虫分离株与现存的隐杆线虫物种进行比较,将在分子细节上揭示这一过程的进化可塑性。我们的方法包括在一个基因的基础上,比较两个进化时间框架的影响:通过比较野生分离株的微观进化和通过比较不同的隐杆线虫物种的宏观进化。如果在线虫之间检测到足够的变异,比较转录组学数据有望通过确定每个基因的每个时间点的进化模式(纯化,阳性和中性)来解决可用的时间过程数据。利用反向遗传学和转基因菌株对一组在基因表达上具有有趣的保守性和差异性的基因进行了实验验证。
英文摘要
DESCRIPTION (provided by applicant): My primary research objective is to decipher transcriptional control in development based upon an evolutionary theoretic approach. A long-standing problem in developmental biology is to determine the genetic basis for the remarkably robust network of events that specify the development of a multicellular organism. The nematode C. elegans makes for a convenient model in this effort, exhibiting both cell-autonomous and cell-cell communication modes of development. While the highly uniform cell lineage of this organism has been elegantly studied, revealing a number of master regulators, little is known about the constraints that act upon the participating genes throughout development. We hypothesize that a comparison among wild C. elegans isolates and with extant Caenorhabditis species would reveal the evolutionary plasticity of the process in molecular detail. Our approach involves comparing, on a gene by gene basis, the effect of two evolutionary time frames: micro-evolution through a comparison of wild isolates and macro-evolution through a comparison with divergent Caenorhabditis species. Provided that sufficient variation is detected among the nematodes, the comparative transcriptomic data promises to resolve the available time course data by identifying the mode of evolution (purifying, positive, and neutral) for each time point of each gene. These predicted modes are experimentally tested by the use of reverse genetics and transgenic strains for a set of genes with interesting conservation and divergences in gene expression.
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Computational framework for analyzing and annotating single bacterium RNA-Seq data
Computational framework for analyzing and annotating single bacterium RNA-Seq data
Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1
Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1
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