A Network Mdoel to Investigate Robustness of Gene Expressions

Correlation networks are ideal to describe the relationship between the expression profiles of genes. Gene expression is a characteristic exhibited by a particular gene. Our body has thousands of genes; each of them expresses differently, and each one of them has a particular function associated with them. When genes corresponding to a particular part of the body becomes non-functional, i.e., not expressed, then the function corresponding to that part of the body does not happen, thereby causing impairment or mutations. Co-regulation is a method involved in clustering analysis to find genes that perform similar functions. We want to identify genes that are co-regulated or expressed in concert to be able to identify defective cellular programs. By understanding this co-regulation, different ways for the healthy development of a cell can be identified and even changes leading to disease can be detected. However, this concept is not yet fully applied due to reasons such as a lack of benchmarking studies that support the global acceptance of these networks, the volume of data available, and the presence of coincidental noise or extra inconsequential relationships. In my project, I propose to explore the robustness of the gene expressions by comparing structural similarities of commonly developed networks using big data infrastructures. Further, I will work on forming a theory about the structure of correlation networks which supports their conceptual usability in biomedical big data. The proposed research will also provide an ideal software pipeline which can supply valid, reproducible and reliable correlation networks

On Mining Biological Signals Using Correlation Networks

Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from microarray or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions; for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are coregulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters via multiple clustering approaches and cross-validation of regulatory elements in these clusters via motif finding software. The results show that the majority (81- 100%) of genes in any given cluster will share at least one predicted transcription factor binding site. With this in mind, new regulatory relationships can be proposed using known transcription factors and their binding sites by integrating regulatory information and the network model itself

Identifying aging-related genes in mouse hippocampus using gateway nodes

BACKGROUND: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes. RESULTS: By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus. CONCLUSIONS: This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network