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.

在我们先前关于相互作用蛋白质共同探讨的工作（5）的基础上，我们研究了镜树方法的功率和限制以预测蛋白质相互作用。我们和其他人观察到，相互作用蛋白的进化距离通常比非互动蛋白的进化距离表现出更高的相似性。但是，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性突变以保持适当的结合。但是，这种解释最近受到了挑战，并且有人提出，镜面方法发现的相关进化的信号与结合位点之间的任何相关演变无关。在（5）中，我们研究了结合位点对相互作用域进化树之间相关性的贡献。我们表明，与外部结合位点相比，相互作用蛋白的结合邻域平均具有更高的协同进化信号。但是，当去除结合邻域时，其余域序列仍然包含一些协同进化信号。 我还继续研究在基因组序列上施加的进化压力，重点是密码子使用的优化。 我们询问的问题是，是否优化了密码子的使用量，以避免翻译中的错误错误。 我还扩大了在小组中进行的系统生物学研究的范围。除了研究蛋白质相互作用网络和调节网络的性质（3）外，我们还开始开发出新的与表型基因型关联相关。例如，在出版物（1）中，我们开发了一种新方法来分析表达定量性状基因座（EQTL）。这种分析显着有助于确定基因调节程序。 为了解决一些已知的挑战，分析基因表达水平及其潜在序列多态性的关联，我们开发了基于图的EQTL分解方法（GED），该方法使我们能够使用所谓的EQTL Association图对基因型和表达数据进行建模。通过基于图的启发式方法，GED在EQTL关联图中识别了密集的子图。通过识别揭示基因型和表达数据隐藏结构的EQTL关联集团，GED有效地滤除了大多数基因座对，这些基因座对不太可能具有显着的联系。我们将GED应用于EQTL数据，以用于恶性疟原虫，即人类疟原虫，并证明GED揭示了整个基因组水平上所有基因座与所有基因之间关系的结构。此外，GED使我们能够以较低的FDR发现其他EQTL，从而为传统的EQTL分析方法提供了重要的补充。我们还正在研究将基因型变异与途径水平表型相关联的新方法。