Addressing Challenges in a Graph-Based Analysis of High-Throughput Biological Data

Graph-based methods used in the analysis of DNA microarray technology can be powerful tools in the elucidation of biological relationships. As these methods are developed and applied to various types of data, challenges arise that test the limits of current algorithms. These challenges arise in all phases of data analysis: data normalization, modeling biological networks, and interpreting results. Spectral graph theory methods are investigated as means of threshold selection, a key step in constructing graphical models of biological data. Also important in constructing graphs is the selection of an appropriate gene-gene similarity metric, and an overview of similarity profiles for some biological data sets is present, along with a similarity thresholding method based upon structural properties of random graphs. The identification of altered relationships between two or more conditions is a goal of many microarray gene expression studies. Clique-based methods can identify sets of coexpressed genes within each group, but additional computational methods are required to uncover the differential relationships and sets of genes changing together between groups. Differential filters are reviewed to highlight those changing interactions and sets of changing genes. The effect of various normalization methods on these differential results is also studied. Finally, how methods commonly used in the analysis of gene expression data can be used to investigate relationships in noisy and incomplete historical ecosystem data is explored

Computational Analysis of Mass Spectrometry Data Using Novel Combinatorial Methods

The analysis of proteome profiles offers a new approach to understanding how cellular machinery functions and responds under certain conditions. By combining two-dimensional electrophoresis with mass spectrometry (MS), a snapshot of the cell's protein expression status and quantitative proteome profiling can be provided. As the cell's proteome becomes defined in normal and altered states, possible utilization of MS proteome profiling as a diagnostic tool becomes a reality. The ability of Matrix Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) to generate a spectrum with thousands of data points, necessitate the development of sophisticated analytical algorithms. In this paper, we describe how MALDI-MS can be used in monitoring proteomic profile in patients before and after treatment using a non- invasive sampling method. Because data analysis in this process possesses a challenge, we present a novel mathematical approach for analyzing data produced by MALDI MS, and discuss current applications of mass spectrometry in clinical medicine as well as challenges faced during procedures and experimentation. As a case study, we analyze protein expression patterns in premenopausal versus postmenopausal women. We also provide a proteomic profiling of premenopausal women versus postmenopausal women treated with estrogen as a hormone replacement therapy. * Corresponding authors