Bringing engineering to biology,11 I developed five instruments in the 1970s through the early 1990s—the automated DNA and protein sequencers, the automated DNA and peptide synthesizers and the ink-jet array technology for synthesizing large numbers of oligonucleotides (in arrays).
I founded Applied Biosystems to commercialize the first four instruments developed at Caltech; Agilent commercialized the ink-jet technology. These instruments allowed the automation and integration of relevant chemistries so that information could be generated more rapidly and reproducibly.
For example, the ink-jet printer led to the idea of automation, integration of chemistries, parallization of measurements, minaturization, and high-throughput synthesis of DNA. These features are fundamental tenets for many of the emerging technologies of today (mediated, in part, through microfluidics and nanotechnology). These instruments provided a powerful infrastructure for the rapidly emerging disciplines of molecular biology, genomics, and proteomics.
My laboratory also developed a series of powerful strategies for attacking diverse biological problems, e.g., oligonucleotide ligase strategy for genetic mapping, the BAC-end sequencing strategy for genome-sequence assembly, the STS strategy for physical mapping, etc.
Human Genome Project
I was invited to the first meeting of the Human Genome Project in the spring of 1985.12-16 I was fascinated with the genome project because it was the only avenue toward producing the first complete parts list of human genes (and, by inference, proteins). It represented a necessary component of systems approaches that I was pursuing.
I was an early advocate for the genome project when most biologists and NIH were opposed (1985–1990). I also directed one of the 16 U.S. genome sequencing centers—we did portions of human chromosomes 14 and 15—and co-founded one of the first genomics companies (Darwin Molecular).
The genome project, as most realize, transformed many aspects of biology and medicine (Table 2). In developing the automated DNA sequencer, I needed to bring together four different disciplines: molecular biology, chemistry, engineering, and computer science. In doing so I came to realize the power of cross-disciplinary biology.
Initial efforts at developing an automated DNA sequencer (1978–1981) failed because the single biologist working on this problem had little knowledge of engineering or chemistry. After three years with very little success, I assembled a team composed of a chemist (Lloyd Smith), an engineer/chemist (Mike Hunkapillar), a biologist turned computer scientist (Tim Hunkapillar), and myself.
Within a few weeks we had conceptualized the four-color chemistry approach to DNA sequencing and three years later had a prototype automated DNA sequencer.
In thinking about this experience and that of my own lab, which pioneered a variety of technologies, I came to the conclusion that there should be a new type of cross-disciplinary biology department where biologists, chemists, computer scientists, engineers, mathematicians, and physicists are assembled to attack hard biological problems through developing the technologies and analytical tools necessary to solve them.
The imperative is that the needs of frontier biology should dictate which technologies are developed and these, in turn, would specify the nature of the analytical tools required (biology drives technology drives analytical tool development).
With the help of Bill Gates, I moved in 1992 from Caltech to the University of Washington to establish the first cross-disciplinary biology department—molecular biotechnology. We recruited cross-disciplinary scientists—and over the next eight years achieved a number of successes.
Ruedi Aebersold and John Yates developed some of the first fundamental techniques in the emerging field of proteomics. Ger van den Engh pioneered a multiparamenter, high-speed cell sorter. My group developed the ink-jet DNA synthesizer for DNA arrays, and Phil Green developed the key assembly and quality-assessment software for the Human Genome Project. Also, we had two of the 16 human genome sequencing centers.
This success was a remarkable testament to the power of cross-disciplinary biology. I had planned to use the department of molecular biotechnology as a cross-disciplinary foundation for building a systems biology institute. Unfortunately, the bureaucracy of a state university hindered the development of some of the fundamental new requirements for creating a systems biology institute. I resigned from the university in 2000 to co-found the independent Institute for Systems Biology.
Creating the Institute
Along with Alan Aderem and Ruedi Aebersold, I created the Institute for Systems Biology (ISB) in Seattle in 2000. This institute brought together a group of like-minded scientists intent on inventing the field of systems biology, which takes a holistic rather than an atomistic view of analyzing biological systems.17
One takes the information of the biological system of interest and from that data formulates a model that may be descriptive, graphical, or mathematical, depending on the amount of available information. Then hypotheses are formulated to test this model.
The hypotheses are tested experimentally by either genetic and/or environmental perturbations of the system. New data is gathered and reintegrated back into the model with appropriate model changes. This iterative process is repeated until theory and experimental data are brought into conjunction with one another (Figure). The models should be predictive.
This process is both hypothesis driven and hypothesis generating. Systems approaches require that data, where possible, be global or comprehensive so that all informational changes can be recorded and that measurements be made on the dynamics of the system, both temporal and spatial, and that data is integrated across the multiscale and hierarchical range of biological information (DNA, RNA, protein, interactions, networks, cells, organs, individuals, populations, and ecologies).
This integration requirement is necessary to account both for the digital information derived from the genome and the environmental signals emerging from outside the genome that operate on every level of the information hierarchy.
Systems approaches require a cross-disciplinary environment that relies on comprehensive and diverse technologies and a significant computational infrastructure. Systems approaches are one of the central tools for dealing with biological complexity.18