Combinatorial and graph theoretical approach to systems biology and mol. evo.

系统生物学和分子生物学的组合和图论方法。

• 批准号: 7969252
• 负责人: Teresa Przytycka
• 金额: $ 90.3万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7969252
• 关键词: Address; Binding; Binding Sites; Biochemical Pathway; Biological; Biological Models; Cell physiology; Codon Nucleotides; Complement; Data; Dependency; Development; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic Polymorphism; Genome; Genotype; Goals; Graph; Human; Malaria; Mathematics; Methods; Modeling; Molecular Biology; Molecular Evolution; Mutation; Neighborhoods; Parasites; Pathway interactions; Phenotype; Plasmodium falciparum; Property; Proteins; Psychological Techniques; Publications; Quantitative Trait Loci; Research; Shapes; Signal Transduction; Structure; Systems Biology; Techniques; Tertiary Protein Structure; Theoretical model; Translations; Trees; Variant; Work; base; biological systems; combinatorial; genome sequencing; heuristics; insight; pressure; programs; protein protein interaction; theories; tool

Building on our previous work on coevlution of interacting proteins (5) we studied power and limitation of the mirror-tree method to predict protein interaction. We and others have observed that the evolutionary distances of interacting proteins often display a higher level of similarity than those of noninteracting proteins. It has been difficult, however, to identify the direct cause of the observed similarities between evolutionary trees. One possible explanation is the existence of compensatory mutations between partners' binding sites to maintain proper binding. This explanation, though, has been recently challenged, and it has been suggested that the signal of correlated evolution uncovered by the mirrortree method is unrelated to any correlated evolution between binding sites. In (5),we examined the contribution of binding sites to the correlation between evolutionary trees of interacting domains. We showed that binding neighborhoods of interacting proteins have, on average, higher coevolutionary signal compared with the regions outside binding sites; however, when the binding neighborhood is removed, the remaining domain sequence still contains some coevolutionary signal. I also continued study of evolutionary pressure exerted on genome sequences, focusing on the optimization of codon usage. The question that we asked is whether codon usage is optimized towards avoiding frameshifting errors in translation. I have also expanded the scope of the systems biology research done in my group. In addition to studying properties of protein interaction networks and regulatory networks (3) we began to develop new apporaches to phenotype-genotype associations. For example, in publication (1) we developed a new method for analysis of expression quantitative trait loci (eQTL). Such analysis significantly contributes to the determination of gene regulation programs. To address some of the known challenges, of analysis of associations of gene expression levels and their underlying sequence polymorphisms, we developed the Graph based eQTL Decomposition method (GeD) that allowed us to model genotype and expression data using the so called eQTL association graph. Through graph-based heuristics, GeD identifies dense subgraphs in the eQTL association graph. By identifying eQTL association cliques that expose the hidden structure of genotype and expression data, GeD effectively filters out most locus-gene pairs that are unlikely to have significant linkage. We applied GeD to the eQTL data for Plasmodium falciparum, the human malaria parasite, and demonstrated that GeD reveals the structure of the relationship between all loci and all genes on a whole genome level. Furthermore, GeD allowed us to uncover additional eQTLs with lower FDR, providing an important complement to traditional eQTL analysis methods. We are also working on new methods to associate genotype variation with pathway level phenotypes.

基于我们先前关于相互作用蛋白质共进化的工作（5），我们研究了镜像树方法预测蛋白质相互作用的能力和局限性。我们和其他人已经观察到，相互作用蛋白质的进化距离通常比非相互作用蛋白质显示出更高水平的相似性。然而，很难确定进化树之间观察到的相似性的直接原因。一种可能的解释是伴侣结合位点之间存在补偿突变，以维持适当的结合。然而，这种解释最近受到了挑战，有人认为镜像树方法发现的相关进化信号与结合位点之间的任何相关进化无关。在（5）中，我们研究了结合位点对相互作用结构域进化树之间相关性的贡献。我们发现，相互作用的蛋白质的结合邻域，平均而言，更高的共同进化的信号相比，结合位点以外的区域，然而，当结合邻域被删除，剩余的结构域序列仍然包含一些共同进化的信号。 我还继续研究基因组序列的进化压力，重点是密码子使用的优化。 我们提出的问题是，密码子的使用是否被优化以避免翻译中的移码错误。 我还扩大了我所在小组的系统生物学研究范围。除了研究蛋白质相互作用网络和调控网络的性质外，我们开始开发表型-基因型关联的新方法。例如，在出版物（1）中，我们开发了一种分析表达数量性状基因座（eQTL）的新方法。这种分析显著有助于基因调控程序的确定。 为了解决基因表达水平及其潜在序列多态性的关联分析的一些已知挑战，我们开发了基于图的eQTL分解方法（GeD），该方法允许我们使用所谓的eQTL关联图来建模基因型和表达数据。通过基于图的聚类分析，GeD识别eQTL关联图中的密集子图。通过识别暴露基因型和表达数据的隐藏结构的eQTL关联集团，GeD有效地过滤掉大多数不太可能具有显著连锁的基因座-基因对。我们将GeD应用于恶性疟原虫（人类疟疾寄生虫）的eQTL数据，并证明GeD揭示了全基因组水平上所有位点和所有基因之间关系的结构。此外，GeD使我们能够发现具有较低FDR的其他eQTL，为传统eQTL分析方法提供了重要补充。我们还在研究将基因型变异与途径水平表型联系起来的新方法。