Mapping and modeling transcription factor networks

项目摘要 细胞选择转录程序，部分是通过将信息从分子传感器传递到 调节DNA结合转录因子(TF)活性的信号级联。中的更改 转铁蛋白活性导致特定基因转录速率的变化，这通常是 对新信息的回应。负责这一过程的分子机制可以被认为是 电池的控制电路。 我的研究计划专注于开发计算和分子方法，使其 可以绘制出细胞的控制电路，观察它们对新信息的反应，以及 最终以有助于人类健康和福祉的方式对它们进行重新布线。酿酒酵母-- Visiae(酵母)是发展这些方法的理想生物，因为它相对简单。 Nome，为实验系统生物学设计的大量菌株集合，以及 全面的数据集，用于测试和优化新方法。这里提出的研究重点是 但我们的方法将立即适用于真菌病原体，并最终 适用于模式生物和人类。 我们计划的第一个目标是优化确定哪些基因受 每个分队。我们将使用这些方法来生成TF-目标网络的地图，该地图将显著地 无论是在准确性还是完整性上，都超越了今天所知的。就像代谢反应图一样， 这将是解决许多科学问题的宝贵资源。 我们的第二个目标是开发方法来推断任何样本中所有转铁蛋白的活性水平 通过分析细胞的转录本。这项工作的一个产品将是易于使用的软件， 将使其他科学家能够在任何一组酵母转录图谱中识别转铁蛋白活性的变化。 我们的第三个目标是开发识别蛋白质的方法，以调节每种蛋白质的活性。 Tf.这将使我们有可能解释我们在刺激时观察到的转铁蛋白活性的变化，例如 药物或营养素被提供给细胞，并设计实验来检验这些解释。 实现这些目标将使我们有可能接近我们的最终目标-发展一个全- 可以预测对遗传和环境扰动的转录反应的定量模型。 作为成功的具体基准，这个模型应该准确地预测对整个 酵母转录组当TF的组合同时被干扰时(缺失或过度表达- 按下)在我们没有扰动数据的生长条件下。我们可以做到这一点- 只有稳定的Mira资金才能实现明智的长期目标。

PROJECT ABSTRACT Cells choose transcriptional programs, in part, by passing information from molecular sensors to signaling cascades that modulate the activity of DNA-binding transcription factors (TFs). Changes in TF activity lead to changes in the transcription rates of specific genes, which are often the first steps in responding to new information. The molecular machinery responsible for this can be thought of as the cell's control circuits. My research program focuses on developing computational and molecular methods that make it possible to map out a cell's control circuits, to watch them as they respond to new information, and ultimately to rewire them in ways that contribute to human health and well-being. Saccharomyces cere- visiae (yeast) is the ideal organism for developing these methods because of its relatively simple ge- nome, extensive collections of strains engineered for experimental systems biology, and comprehensive datasets for testing and optimizing new methods. The research proposed here focuses exclusively on yeast, but our methods will be immediately applicable to fungal pathogens and ultimately adaptable for model organisms and humans. The first objective of our plan is to optimize methods for determining which genes are regulated by each TF. We will use these methods to produce a map of the TF-target network that goes significantly beyond what is known today, both in accuracy and completeness. Like the map of metabolic reactions, this will be a valuable resource for addressing many scientific questions. Our second objective is to develope methods for inferring the activity levels of all TFs in any sample of cells by analyzing their transcriptomes. One product of this work will be easy-to-use software that will enable other scientists to identify changes in TF activity in any set of yeast transcriptional profiles. Our third objective is to develop methods for identifying proteins that regulate the activities of each TF. This will make it possible to explain the changes in TF activity we observe when stimuli, such as drugs or nutrients, are provided to cells, and to design experiments that test those explanations. Achieving these objectives will make it possible to approach our ultimate goal – to develop a quan- titative model that can predict the transcriptional response to genetic and environmental perturbations. As a concrete benchmark for success, this model should accurately predict the effect on the entire yeast transcriptome when combinations of TFs are simultaneously perturbed (deleted or overex- pressed) under growth conditions for which we have no perturbation data. We can achieve this ambi- tious, long term goal only with stable MIRA funding.