I recently attended a workshop on Quantitative Immunology held under the auspices of the Kavli Institute of Theoretical Physics at the University of California, Santa Barbara (UCSB). The 30 or so participants came for a variety of backgrounds : the majority from physics, a fair number of classical experimental immunologists and a handful of applied mathematicians. The meeting, which lasted three weeks, was structured round seminars given by the participants, but these provided the focus for lots of interaction, discussion and debate. In refreshing contrast to most conventional immunology conferences, the emphasis was very much on ideas rather than overwhelming displays of data. I would like to consider briefly what themes emerged from the meeting, and how these may help define the scientific space of Quantitative Immunology.
Evolution, in many guises, was a nexus joining the realms of physics and biology. Mutation/selection, in the evolutionary sense was invoked at a molecular level to shape antibody and T cell repertoires, at a cellular level to create the naïve and memory compartments of adaptive immunity populations, and at a systems level to form entire signalling networks. The ability to apply well developed stochastic models proved an enticing approach for physicists to enter the world of immunology. Quantities borrowed directly from physics (information content, entropy) , or from ecology (e.g. diversity) were frequently used although the meaning of these terms in the immunological context would have benefited from more precise explanation.
The meeting was full of interesting approaches and I certainly learnt a lot about how physicists might approach the complicated world of classical immunology. But I was struck by how often the emphasis on the “quantitative” was absent. There were of course exceptions. The careful measurements of IL2 consumption/diffusion Oleg Krichevsky; the probabilistic models of TCR generation (Thierry Mora and Aleksandra Walczak) , the kinetic proof reading models of TCR transduction (Paul Francois) , and the simple but elegant birth/death models of repertoire generation (Rob de Boer) were examples (just a few of my favourites and not meant to be an exclusive list!) of modelling which generated precise quantitative predictions amenable to experimental measurement and verification/falsification.
But often, quantitative model parametrisation seemed almost an afterthought, and quantitative prediction not really the goal. Instead, models were used as conceptual tools to describe and hence gain intuition into very complex biological systems. This type of modelling can surely be helpful. But it is a long way from the precise equations of physics, based on a handful of fundamental “constants” and whose power lies precisely in their ability to precisely predict quantitative behaviour of a physical system to the limits of measurable accuracy available.
The impression I came away with is that bringing immunology into the quantitative era is still a project in its infancy. Of course, data of a more-or-less quantitative nature is being produced at an exponentially increasing rate : single cell data, flow cytometric data, genomics data, transcriptomics data. But we lack a conceptual framework which will somehow capture this data in mathematical formulae, which can be predictive, accurate and offer intuitively appealing interpretations of immunological complexity.