Immune System Research
I encountered the limits of a reductive approach based on Occam’s razor as a graduate student at Caltech in the early 1960s, studying the complexities of the mouse and human immune systems.1-3
I was initially interested in how B cells generate the diversity of antibodies required to defend a vertebrate organism again viruses, bacteria, and, perhaps, even cancer. I extended these studies to include T-cell receptors and molecules of the major histocompatibility locus over the first 30 or so years of my career.4-6
Increasingly, I came to appreciate the incredible complexity of the immune system. Indeed, we came to understand many of the details of the molecular basis of antibody and T-cell receptor diversity.
However, the deeper mysteries of the immune response itself, both adaptive and innate, immunological tolerance, and autoimmunity did not yield their mechanisms to simple molecular, cellular, and biochemical approaches. I gradually came to realize that one needed to take a more holistic or systems approach to studying these complexities.
Max Delbruck, also at Caltech in the 1970s, argued that these immune mechanisms could only be revealed by a more global systems approach—similar to his attempts to understand the complexities of the fungus phycomycetes. I came to realize that new tools and novel strategies were necessary to be able to deal in a more comprehensive and quantitative manner with the complexities of biology in general and immunity specifically.
So how could one go about creating the holistic system strategies and measurement (or visualization) tools for generating global or comprehensive datasets? These thoughts led to my participating in a series of paradigm changes that paved the way for dealing with biological complexity.
Paradigm Changes in Biology
Thomas Kuhn’s The Structure of Scientific Revolutions7 described how paradigm changes arise by observations that do not fit preconceived dogma and how as these observations become accepted, they catalyze new explanations or paradigm changes in science.
Kuhn also made the point that most scientists are extremely conservative and reluctant to accept reformulations of the current dogmas that lead to paradigm changes (a failure of our educational system is that we do not teach most scientists how to think outside the box). Creating paradigm changes is exciting and often appreciated only in retrospect.
But how does one go about catalyzing change?
Bill Dreyer, my Ph.D. mentor at Caltech, left me with two dictums that provide insights into this question:8
- Always work at the leading-edge of biology. It is far more interesting and exciting and provides the opportunity to discover something truly new.
- If you really want to change a discipline, invent a new tool for generating more data and/or new data types in relevant areas of data space.
Early in my career, I realized that biology is an informational science, and new technologies should aid in deciphering biological information. This informational view is central to deciphering biological complexity.
When I arrived at Caltech as an assistant professor in 1970, I divided my lab into two areas: molecular immunology (leading-edge biology) and the development of technologies to more effectively decipher biological information. Combining technology development and leading-edge biology enabled me to participate in a significant manner in five paradigm changes that have created a powerful framework and infrastructure for dealing with biological complexity:9-10
- bringing engineering to biology to create automated and high-throughput technologies for gathering biological information;
- participating in the initiation and implementation of the Human Genome Project that democratized all genes (made them readily accessible to all biologists), created the parts list that enabled systems biology, and transformed many areas of biology;
- creating the first cross-disciplinary biology department to demonstrate the power of using biology to drive the development of relevant technologies and analytic tools in the context of an infrastructure with cross-disciplinary scientists speaking one another’s languages and working together in teams;
- creating the first systems biology institute that brought transformational systems approaches to complex problems of biology and medicine; and
- pioneering the emergence of proactive systems or P4 (predictive, preventive, personalized, and participatory) medicine that will decipher the complexities of disease by gathering and analyzing enormous amounts of data for each individual patient.
The first three of these changes were a necessary infrastructural framework for the emergence of systems biology and systems medicine. Collectively, these initiatives established powerful new approaches to dealing both with the complexities of biology and disease.