One of the challenges of using mass spectrometry for metabolomic analyses of samples consisting of thousands of compounds is that of peak identification and alignment. This paper addresses the issue of aligning mass spectral data from different samples in order to determine average component m/z peak values. The alignment scheme developed takes the instrument m/z measurement error into consideration in order to heuristically align two or more samples using a technique comparable to automated visual inspection and alignment. The results obtained using mass spectral profiles of replicate human urine samples suggest that this heuristic alignment approach is more efficient than other approaches using hierarchical clustering algorithms. The output consists of an average m/z and intensity value for the spectral components together with the number of matches from the different samples. One of the major advantages of using this alignment strategy is that it eliminates the boundary problem that occurs when using predetermined fixed bins to identify and combine peaks for averaging and the efficient runtime allows large datasets to be processed quickly.

Using microarrays, researchers are able to obtain a genome wide snapshot of a biological system under a given experimental context. Fortunately, a significant amount of gene regulation data is publicly available through various databases. We present a system that uses extra knowledge in published gene regulation relationships to examine findings in a microarray experiment and to aid biologists in generating hypotheses. Two algorithms are developed to highlight consistencies as well as inconsistencies between the data. We demonstrate that consistent as well as inconsistent subnetworks found in this manner are important in the discovery of active pathways and novel findings.

Microarray experiments produce expression patterns for thousands of genes at once. On the other hand, biomedical literature contains large amounts of gene regulation relationship information accumulated over the years. One obvious requirement is an automated way of comparing microarray data with the collection of known gene regulation relationships. Such an automated comparison is imperative because it can help biologists rapidly understand the context of a given microarray experiment. In addition, the consistency measure can be used to either validate or refute the hypothesis being tested using the microarray experiment. In this paper we present a systematic way of examining the consistency between a given set of microarray data and known gene regulation relationships. We first introduce a simple gene regulation network model with two separate algorithms designed to isolate a maximally consistent network. Subsequently, we extend the model to take into account multiple regulating factors for a single gene while highlighting both consistencies and inconsistencies. We illustrate the effectiveness of our approach with two practical examples, one that picks the peroxisome proliferator-activated receptor (PPAR) pathway as highly consistent from multiple pathways of Kyoto encyclopedia of genes and genomes (KEGG), and another that isolates key regulatory relationships involving nfkb1 and others known for macrophage's counter response to inflammation.

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Using microarray technology for genetic analysis in biological experiments requires computationally intensive tools to interpret results. The main objective here is to develop a "meta-analysis" tool that enables researchers to "spray" microarray data over a network of relevant gene regulation relationships, extracted from a database of published gene regulatory pathway models. The consistency of the data from a microarray experiment is evaluated to determine if it agrees or contradicts with previous findings. The database is limited to "activate" and "inhibit" gene regulatory relationships at this point and a heuristic graph based approach is developed for consistency checking. Predictions are made for the regulation of genes that were not a part of the microarray experiment, but are related to the experiment through regulatory relationships. This meta-analysis will not only highlight consistent findings but also pinpoint genes that were missed in earlier experiments and should be considered in subsequent analysis.