Signature analysis tools for the life sciences

细胞已经进化出一套复杂的、相互关联的过程，这些过程执行着各种各样的功能。当细胞受到环境挑战或遗传缺陷的压力时，它会启动特定的过程，试图绕过这个问题。过程本质上是以特殊方式相互作用以完成任务的基因或基因产物的清单。在生物和生物医学研究中，需要确定特定的分子途径或过程在靶细胞中是否活跃。在实验室中甚至对于一个过程来说，仍然很难做到这一点，而且用目前的技术不可能同时直接测量所有路径和过程的状态。使用高通量技术，如微阵列和下一代测序，为研究人员提供了一种可以识别细胞内所有过程的实验，但它是以间接的方式做到这一点的。例如，微阵列可以用来同时测量细胞中所有基因的表达水平。如果一个特定的过程在细胞中是活跃的，这个过程很可能最终会改变某些基因的表达。通过使用计算机和统计技术，可以搜索这些档案以寻找这一过程的“签名”的证据。也就是说，它将识别当该过程被激活时发生变化的那些基因。这项提议旨在开发生物信息学/生物统计方法，以解决与识别签名有关的许多技术困难。我们的目标是生产软件，帮助生物学家识别他们的数据中哪些过程是活跃的。在最近的过去，系统生物学研究已经毫无疑问地证实，细胞以广泛、全面的方式对挑战做出反应，同时调节许多过程以求生存。我们识别签名的方法允许我们形成所谓的签名网络。这些网络提供了不同过程如何相互联系和共同调制的线索。一种长期的应用可能是帮助临床医生可靠地确定那些具有一系列激发过程的肿瘤，这些过程对肿瘤起到保护作用(从而潜在地省去了患者痛苦和昂贵的化疗)。

Cells have evolved a set of complex, inter-connected processes that perform a wide variety of functions. When a cell is stressed by an environmental challenge or genetic defect, it activates specific processes that attempt to circumvent the problem. Processes are essentially a list of genes or gene products that interact in special ways to accomplish their task. In biological and biomedical research, there is a need to identify if a specific molecular pathway or process is active within a target cell. It is still very hard to do this in the laboratory for even a single process, and it is impossible with current technologies to directly measure the status of all pathways and processes simultaneously. The use of high-throughput technologies such as microarrays and next generation sequencing has provided researchers with a type of experiment that allows all processes within the cell to be identified but it does so in an indirect way. For example, microarrays can be used to measure the expression levels of all genes in the cell simultaneously. If a specific process is active in the cell, it is highly likely that this process will eventually change the expression of some genes. With the use of computers and statistical techniques, these profiles can be searched for evidence of this "signature" of the process. That is, it will identify those genes that are changed when the process is activated. This proposal is aimed at developing bioinformatics/biostatistics methodology that address the many technical difficulties associated with identifying signatures. Our goal is to produce software that helps biologists identify which process are active in their data. In the recent past, systems biology research has established beyond a doubt that cells react to challenges in broad, comprehensive ways, modulating many processes simultaneously in order to survive. Our approach for identifying signatures allows us to form so-called signature networks. These networks provide clues as to how different processes are interconnected and co-modulated. One long-term application may be to assist clinicians in reliably determining those tumors that have a set of processes firing that play a protective role against tumors (thus potentially sparing the patient painful and expensive chemotherapy).