Data Retrieval and Visualization for Setting Research Priorities in Biomedical Research

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Over the past two decades, particularly after the completion of the human genome project, biomedical research has produced a huge amount of data. With the expansion of information technology, investigators have gained basic competency with integrating different resource data sets into unions. The basic principle of this integration is to use the co-occurrence of the same or similar (orthologous) elements in different data sets as links between those data sets. Increasingly more experiment-based databases have been established, which facilitates this integration of data sets. During this blooming period of biomedical research, high-throughput experimental data is fuelling systems biology research. In the pre-genomic era, researchers were only capable of conducting experiments with a single gene or a single protein at a time, which could not provide a global perspective on the molecular interactions that bridge the gap between external signal and internal response. Within the past two decades, several high-throughput technologies have been developed to address this difficulty. Expression microarrays detect the relative abundance of gene transcripts by comparing two or more biological conditions, and have become a common tool for screening thousands of genes for expression changes in response to a perturbation, or to track transcriptional changes in developmental processes. As a way of visualizing and interpreting the flood of data in recent years, the creation of biological networks from data became a prevalent target in biomedical research recently, including the construction of protein-protein interaction networks (PPN), gene regulatory networks (GRN), and metabolic and signaling networks and pathways, as well as disease-related or cell function-related networks. The integrative strategy of combining different data sets is a natural way of setting up networks. Also, based on the data obtained from high-throughput experiments, networks may be created by modeling the internal relationships of these data. Several popular analytical approaches are being utilized to model networks (Gebert, et. al., 2007; de Jong, 2002). Boolean networks describe each element as a variable with the value 0 or 1 to represent the state of the element as ‘off’ or ‘on’, respectively. Modeling networks by means of Boolean network became popular in the wake of a groundbreaking study by Kauffman. Kauffman employed Boolean networks to model the global properties of large-scale regulatory systems, which is called Kauffman’s NK Boolean networks. An NK automaton is an autonomous random network of N Boolean logic elements with each element having K inputs and one output, all taking binary (0 or 1) values. If K is large, like K=N, the network