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. Keyword: Proteomics, Robotics, Automation, Statistical Learning, Mass spectrometry Gil Alterovitz, Ehsan Afkhami, and Marco Ramoni 218 I The Promise of Proteomics Proteins are essentially the small machines that allow an organism to function. “Proteomics,” a term introduced in the early 1990s, is a field concerned with determining the structure, expression, localization, interactions and cellular roles of all proteins within a particular organism or subset of one [3]. Yet, until recently, it was only possible to explore proteins and their function one at a time. Indeed, the key to proteomics is its intrinsic focus on parallelization and computational techniques to study myriad proteins at the same time. Proteomics is set to have a profound impact on clinical diagnosis and drug discovery. In fact, most drugs target and inhibit the functions of specific proteins. Proteomics has come a long way since the mid-1990s when protein networks were largely studied using 2-D gel electrophoresis (discussed later) [3]. Clinical proteomics is concerned with identifying protein networks and the intracellular interactions between proteins as applied to clinical aims [4]. The functioning of the human cell can be likened to the operation of a factory, as proteins are machines that process/deliver products and messages to other proteins via biochemical interactions. These messaging pathways or routes are essential for cellular function. As such, their malfunction can also be the cause or consequence of a disease process [4]. It is this notion that stimulated the application of proteomic technologies to: oncology [5], neurology [6], toxicology [7], immunology [8], and many other areas [9-11]. Later in the chapter, mass spectrometry methods and their proteomics applications will be outlined. With robust and high throughput features, these tools have enabled the resolution of thousands of proteins and peptide species in bodily fluids ranging from blood [12] to urine [13, 14]. Such technologies have advanced research in early cancer diagnosis as well as in Human Immunodeficiency Virus (HIV) inhibiting drugs [4, 15]. Proteomics can and does leverage some of the engineering and statistical methodology developed for functional genomics approaches [16]. However, challenges have arisen in this new field and customized solutions such as fabrication of chips for parallelization of experiments [17-24], robotics [25-31], and machine learning techniques for intelligent decision analysis [32-34] need to be engineered. Other challenges are completely new and proteome specific. For example, post-transitional modifications of proteins can be vital for cell function. In such cases, one to one correspondence does not exist between each protein and its encoding gene. This is significantly different from the relatively static nature of DNA. Since the post-translational modifications occur after the protein is created (based on the genetic blueprint), such modifications cannot be seen via traditional genomics approaches. The Human Genome Project has demonstrated that speed, cost, and precision are the underlying factors in any large scale biological endeavor and that technological hurdles can be overcome with novel engineering approaches. Higher throughput and sensitivity are requirements of technologies aiming to capture quality snapshots of cellular activity. It is with this aim that academia and industry are pushing ahead in the automating processes such as robotic sample preparation [35], alternative readouts for protein interactions [36-38], and microfluidics [39]. Current instrumentation is far from optimal, however, partly because manufacturers have not yet had the necessary lead time to build systems perfectly tailored to protein analysis [40]. Robotics, Automation, and Statistical Learning for Proteomics 219 In addition to sensitivity and throughput considerations, there are many data analysis challenges inherent in representation and interpretation of experimental results. Methods aimed at meeting these problems are largely grouped under bioinformatics, a multidisciplinary discipline, absorbing methods in computer science, signal processing, statistical inference, and other engineering-related fields. Algorithms such as the Basic Local Alignment Search Tool (BLAST) [41] have been developed for automated protein identification. Yet, more intelligent decision making algorithms are needed to improve detection of post-transitional modifications in MS spectra, peptide mass fingerprinting, and electrophoresis image analysis. I