ABSTRACT
I. INTRODUCTION Recently, there has been significant interest in the ability to generate proteomic data as a measure for cell physiology and responses. There should be no question that such data are invaluable to the study of any particular biological problem or system. The current interest in proteomics may signal a shifting paradigm in biology. During the past decade, a special emphasis has been placed on the need to generate DNA sequence information as a key to solving various biological problems and to understanding biological systems. More recently, new tools have emerged which permit the measurement of mRNA expression on a genomewide scale. These new tools are being extensively applied to a wide variety of interesting systems and provide a wealth of information about cell physiology and gene expression. However, the lack of good correlation between mRNA expression and corresponding protein expression has recently been highlighted [1], and these observations have shifted the focus of many groups toward proteomics studies. It has been argued that although mRNA expression is important, protein expression measurements are critical to understanding biological systems [2]. We agree that
the measurement of protein abundance is critical to an understanding of a biological system; however, we further believe that measurements at all levels of biological information need to be made to develop a deep understanding of how a system (e.g., a gene network) functions [3,4]. That is, information about DNA sequences, mRNA expression, and protein expression and activities, as well as information about the physicochemical properties underlying the cellular processes should be integrated as much as possible. This integration will require new platforms for handling these often disparate datasets as well as new computational tools that can combine knowledge from each of these levels of information into a coherent understanding of the system as a whole. This paradigm shift toward a whole-cell perspective, rather than a genome, transcriptome or proteome-centered perspective, is driven by technology development and heralds the emergence of a systems approach to biology.
