ROBOTICS, AUTOMATION, AND STATISTICAL LEARNING FOR PROTEOMICS

During the era of the Human Genome Project (1), the emphasis was on sequencing and annotating individual genes. At that time, the number of estimated human genes was thought to be 100 thousand genes. Yet, as the human genome project draws to a close (2), recent work has decreased the estimate to between 20-25 thousand not far from the number of genes in a simple worm (i.e. C. elegans). Thus, the complex engineering of a human must be from other areas such as the interactions of the gene's products, or proteins. Given this, the field of proteomics has quickly been drawn to center stage. While biologists seek to study proteins, methods have been rather primitive until recently. A sudden surge of engineering and other technical talent has led this field and associated research to grow dramatically in the last couple of years. In this chapter, the topic of proteomics is introduced to an engineering/technical audience with an emphasis on the robotics and intelligent systems technologies used in this field. These include issues in protein extraction, separation, and identification. The associated analysis algorithms and statistical learning methods are also discussed. Two case studies regarding the above topics are then explored. Lastly, the future direction of the field and its challenges are delineated. Clinical applications of proteomics such as cancer diagnosis and drug discovery are expounded upon as relevant.