Research Issues in Genomic Signal Processing

Published in: IEEE Signal Processing Magazine, Vol. 22 No. 6, 46-68, 2005

Abstract

In an earlier article in IEEE Signal Processing Magazine, we discussed general issues regarding genomic signal processing (GSP) as it pertains to diagnosis and therapy [1]. In this article, we discuss the key research issues for GSP. It is important to recognize that “genomic signal processing” is not a name for genomic bioinformatics nor for the application of signal processing methods in genomics. GSP concerns the processing of genomic signals; it may be defined as the analysis, processing, and use of genomic signals to gain biological knowledge and the translation of that knowledge into systems-based applications. We note that research issues pertaining to GSP fit within the overall challenges confronting research in the area of multimodal biomedical systems [2].
We shall not review the basic biological concepts covered in our previous article except to recall two points. First, since cellular control results from multivariate activity among cohorts of genes and their products, it is not possible to separate the analyses of DNA, RNA, and protein in the DNA-to-RNA-to-protein information flow. Nevertheless, the immense interaction between levels ensures that a significant amount of the system information is available in each of the levels, with the current focus on RNA owing to measurement considerations, in particular, gene-expression microarrays. Second, two major goals of functional genomics are: 1) to use genomic signals to classify disease on a molecular level and 2) to screen for genes that determine specific cellular phenotypes and model their activity in such a way that normal and abnormal behavior can be differentiated. These goals correspond to diagnosing the presence or type of disease and developing therapies based on the disruption or mitigation of aberrant gene function contributing to the pathology of a disease. Developing diagnostic tools at the RNA level involves designing expression-based classifiers based on genes whose product abundances indicate key differences in cell state. Developing therapeutic tools involves synthesizing nonlinear dynamical networks, analyzing these networks to characterize gene regulation, and designing intervention strategies to modify dynamical behavior.