Automation in Proteomics and Genomics: An Engineering Case-Based Approach

In the last decade DNA sequencing costs have decreased over a magnitude, largely because of increasing throughput by incremental advances in tools, technologies and process improvements. Further cost reductions in this and in related proteomics technologies are expected as a result of the development of new high-throughput techniques and the computational machinery needed to analyze data generated.

Automation in Proteomics & Genomics: An Engineering Case-Based Approach describes the automation technology currently in the areas of analysis, design, and integration, as well as providing basic biology concepts behind proteomics and genomics. The book also discusses the current technological limitations that can be viewed as an emerging market rather than a research bottleneck. Topics covered include:

• molecular biology fundamentals: from ‘blueprint’ (DNA) to ‘task list’ (RNA) to ‘molecular machine’ (protein); proteomics methods and technologies; modelling protein networks and interactions
• analysis via automation: DNA sequencing; microarrays and other parallelization technologies; protein characterization and identification; protein interaction and gene regulatory networks
• design via automation: DNA synthesis; RNA by design; building protein libraries; synthetic networks
• integration: multiple modalities; computational and experimental methods; trends in automation for genomics and proteomics
• new enabling technologies and future applications

Automation in Proteomics & Genomics: An Engineering Case-Based Approach is an essential guide to the current capabilities and challenges of high-throughput analysis of genes and proteins for bioinformaticians, engineers, chemists, and biologists interested in developing a cross-discipline problem-solving based approach to systems biology.

Gil Alterovitz

Dr. Alterovitz received his PhD in Electrical and biomedical Engineering at MIT through the Harvard/MIT Division of Health Sciences and Technology. He is a biomedical informatics fellow with the Harvard/MIT Division of Health Sciences and Technology (HST), Children's Hospital Informatics Program, and the Harvard Medical School-Partners Center for Genetics and Genomics. He is currently heading a new class that he initiated at Harvard University, bio.95hfa: 'Proteomics and Cellular Network Engineering'. He has served on the Harvard/MIT Division of Health Sciences and technology MD Curriculum and the Harvard/MIT Division of Health Sciences and Technology PhD Admission committees. He was a US Fulbright to Canada (University of Toronto) in 1998-1999. Dr Alterovitz has an S.M. Degree from the Massachusetts Institute of Technology (MIT) in Electrical Engineering and Computer Science, where he was a NDSEG Fellow. His B.S. is in Electrical and Computer Engineering from Carnegie Mellon University.

Dr Alterovitz has worked at Motorola (where he won the Motorola Intellectual Property Award), at IBM, and as a consultant for several national clients. As an invited contributor, he wrote the 'Proteomics' section for the Wiley Encyclopedia of Biomedical Engineering. Dr Alterovitz has appeared or has been cited for achievements in several national media outlets, including three separate editions of USA Today and National Public Radio. He was also featured in the Boston Globe. In 2001, he was selected as one of approximately 20 international delegates to the Canada 25 forum (to discuss healthcare/technology) covered by CBC radio, a national V special and Canada's Maclean's.

RoSeann Benson

For eleven years, Ms Benson was a chemical engineer, and participated in all phases of automation implementation projects in a variety of industries: nuclear, semiconductor, aluminum, specialty chemical and environmental. Her engineering experience ran the project gamut, from inception to completion, and included framing the technical problems correctly; designing bench tests to establish system specifications; setting and calculating design parameters; selecting,purchasing and installing equipment; and utilizing general equipment to meet particular applications. As part of thee engineering projects, Ms Benson acquired extensive experience with documentation. One paper, 'Shock Deionization of the K and L Basins', presented the research and outcome of a successful project where indefinite scientific principle were defined more accurately within a particular context. She won an American Institute of Chemical Engineers' (AIChE) National 'Outstanding Paper' award for the paper that she wrote and presented at an AIChE meeting. Ms Benson's formal credentials include a Clarkson University Chemical Engineering bachelor's Certificate in Administration and Management.

Ms Benson writes nonfiction outside of the engineering field, and is a contributor to three Cadogan Guides. Her first book, 101 Puppy-Buying Tips has recently been published by LifeTips.com. In addition, she serves on the City of Beverly's Library Board of Trustees.

Marco F. Ramoni

Marco F. Ramoni is Assistant Professor of Pediatrics and Medicine at Harvard Medical School and Assistant Professor of Health Sciences and Technology at the Harvard University and the Massachusetts Institute of Technology Division of Health Sciences and Technology. He is also Associate Director of Bioinformatics at the Harvard Partners center for Genetics and Genomics and the Director of the National Library of Medicine Training Fellowship in Biomedical Informatics 'Biomedical Informatics' at the Harvard-MIT Division of Health Sciences and technology, core faculty of the course Genomic Medicine at Harvard Medical School and a member of the curriculum committee of the Cellular and Molecular Medicine track of the Medical Physics and Medical Engineering graduate program at Harvard Medical School and a member of the curriculum committee of the cellular and Molecular Medicine track of the Medical Physics and Medical Engineering graduate program at Harvard-MIT Division of Health Sciences and Technology. He is cofounder of Bayesware LLC, a software company developing machine-learning programs based on Bayesian methods. He received a PhD in Biomedical Engineering and a BA in Philosophy (Epistemology) from the University of Pavia (Italy), completed his postdoctoral training at McGill University, Montreal (Canada). He has held academic and visiting positions at the University of Massachusetts, the University of London (United Kingdom), the Knowledge Media Institute (United Kingdom) and the University of Geneva (Switzerland). He is author of over 90 publications in genetics, biomedical informatics, statistics and artificial intelligence.