Training the 21st Century Immunologist
Immunology, along with other fields of biology, is undergoing a revolution. Here we discuss the challenges and opportunities presented by considering the dynamical systems properties of the immune system, and harnessing the power of data-rich technologies. We present specific recommendations for changing graduate programs to incorporate training that will enable students to actively participate in the analyses of complex data and their biological system, and urge that we move from viewing quantitative and computational biology as interdisciplinary, to recognizing these as intrinsic to the discipline of immunology going forward.
Biology is the study of highly complex dynamical systems. Indeed, at any scale – from eco-systems to populations, to organisms, to organs, to cells, molecular networks and macro-molecules – a hallmark of biological systems is the dynamical interplay of numerous components. It is remarkable how the tools of molecular biology and biochemistry have rendered this complexity tractable. Specifically, with the culmination of ‘omic technologies, the molecular and cellular parts lists of cells are known, quantifiable, and increasingly readily available in electronic databases. This remarkable success at the same time signifies that biology has irreversibly changed to a data rich science.
This transformation, described more fully elsewhere (e.g., ), has changed what constitutes the skillset of a biologist. Up to recently, assay development often constituted a central aspect of training, as the ability to generate useful data was often limited by technical hurdles. With a growing number of cellular and molecular reagents, as well as highly sophisticated assays in kit form available from vendors, the ability to analyze data creatively and critically so as to obtain real insight becomes a distinguishing skill. Indeed, practices within our group reflect this change: whereas assay kits were banned in the initial years to ensure that students were trained in the skills of optimizing assays, our focus has shifted to requiring students to never take software output at face value, but to be able to customize data analysis methods. In other words, what distinguishes PhD biologists in the 21st century more so than previously, is innovation not in the generation of data, but in data analysis and interpretation.
(See complete article here: http://www.sciencedirect.com/science/article/pii/S1471490615000770)
Volume 36, Issue 5, May 2015, Pages 283–285