Molecular and genetic network analysis in yeast

通过实验确定细胞成分之间的相互作用，如生化反应、蛋白质-蛋白质和基因的相互作用，可以绘制出细胞的图谱。相互作用网络图定义了基因功能，并形成了我们的生物过程模型的基础。这些知识最终将使我们能够从基因型预测表型，了解疾病的遗传基础，并为工业应用设计新的生物系统。最近生物技术的进步和对数百种生物体的完全基因组测序的可用性，导致了相互作用的蛋白质、基因和分子网络的基因组尺度制图。例如，使用酵母双杂交、微阵列染色质免疫沉淀和免疫沉淀耦合质谱等技术，已经绘制了超过80,000种酵母的蛋白质-蛋白质、蛋白质- dna和遗传相互作用图谱，其中加拿大研究小组做出了重大贡献。此外，分子分析方法，如通过微阵列测量RNA水平或通过质谱测定蛋白质，提供了对细胞途径成分动态水平的洞察。这些关于蜂窝网络的广泛信息需要强大的计算工具的发展，以促进人类大脑直接能力之外的解释。我的目标是通过开发新的生物网络分析方法来解决这个问题，以发现新的蛋白质复合物，总结生物过程之间的依赖或缓冲关系，并找到在细胞特定生命周期状态下活跃的途径。作为生物学家，网络分析方法将帮助我们在多个细节层面有效地导航细胞图谱，并利用它来做出新的发现。

Experimental determination of interactions between cellular components, such as biochemical reactions, protein-protein and genetic interactions, charts a map of the cell. The interaction network map defines gene function and forms the basis for our models of biological processes. This knowledge will eventually enable us to predict phenotype from genotype, understand the genetic basis of disease and engineer new biological systems for industrial applications.Recent biotechnology advances and the availability of completely sequenced genomes for hundreds of organisms has led to genome-scale mapping of networks of interacting proteins, genes and molecules. For instance, over 80,000 protein-protein, protein-DNA and genetic interactions for the yeast Saccharomyces cerevisiae have been mapped using techniques such as yeast two-hybrid, chromatin immunoprecipitation on microarrays and immunoprecipitation coupled to mass spectrometry, with a significant contribution by Canadian research groups. Further, molecular profiling methods, such as RNA level measurement by microarray or protein quantitation by mass spectrometry, provide insight into the dynamic levels of cellular pathway components.This extensive information about the cellular network demands the development of powerful computational tools to facilitate interpretation outside of the direct capacity of the human brain. I aim to address this by developing novel biological network analysis methods to discover new protein complexes, summarize dependency or buffering relationships between biological processes and find pathways that are active at specific life cycle states of the cell. Network analysis methods will help us, as biologists, effectively navigate the cell map at multiple levels of detail and use it to make new discoveries.