Liquid Association with Application in Gene Expression

UC Case No. 2002-054

Background

Microarrays are used as a high throughput biochemistry technique for generating data about the quantity of mRNA expressed by each gene in cells of an organism under a set of conditions. The data that is generated, commonly known as a gene expression profile, can be used to infer global cellular activities that would be hard to describe otherwise. However, a problem that arises is that the data tends to be large and complex, making it difficult to analyze and interpret. One way to distill information from microarray data is through the use of correlation. Correlation is a traditional way of summarizing the relationship between any two variables in a system after the collection of empirical data. The term "liquid association" is used to conceptualize the internal evolution of co-expression patterns for a pair of genes in response to constant changes in the cellular state variables. But in extremely complex systems, correlation is hard to observe due to the many variables interacting with each other. There is a need for a bioinformatics tool to help with genomic research by analyzing and interpreting the large number of data involving multiple variables.

Innovation

Scientists at UCLA have developed a novel bioinformatics system and method that identifies a network of novel ternary relationships between variables in a complex data system. The method conducts a genome-wide search and identifies the most critical cellular players that may affect the co-expression pattern for genes that may participate in more than one pathway. The tool leads to better understanding about the cellular genetic network. Coupling the gene expression data and drug responsiveness data, this tool can help the search for new drugs or treatments in cancers and other disease studies.

Applications

• Distill large amount of data generated by genomic research
• Identify a network of relationships between variables in a complex data system and critical cellular players that may affect the co-expression pattern for genes that may participate in more than one pathway
• Predict the responsiveness of a drug based on the gene profile of the patient

Advantages

• The tool may be applied in any large microarray study
• First tool that evaluates "change in correlation"
• First high-throughput bioinformatics tool to explore and exploit the dynamic, as oppose to the static, aspect of gene expression in cells

Eliminates the need to specify the cellular state before applying this method