Immunology is empowered by lots of data, with deep sequencing, cell population imaging, proteomics, high-throughput immune assays, or metabolomics as sources. In the past one would draw a scheme with perhaps 20 players on the blackboard, with arrows indicating which influences which, ‘up’ or ‘down’. Now there is many more (>75 000) molecular and cellular players, and it matters how much they go up or down, and when, and where. With the advent of deep data, our traditional way of thinking falters: It does no justice to the new opportunities for identifying the immunology in action. We may need something like ‘deep thinking’.
But what is deep thinking? The human brain is not fit for thinking rationally about thousands of molecules at the same time. But it can invoke a little help from its friends. It is wise to befriend computers handling virtually unlimited numbers of data. They can analyze the data in many, many different ways, finding complex combinations of the most complex patterns. Such data analysis has long been limited by focusing on a single level of cellular organization, e.g. the transcriptome, rather than on the dynamics of the entire, hierarchical network at stake. It has not been ‘deep’ data analysis, penetrating down through the various levels of cell action.
Computers can also empower mathematical models that simulate immunological reality. For a long time already, mathematical modelling has played a big role in biology. It has helped appreciate the role of self-organization in developmental biology, synchronization of cellular oscillations, fragilities in the performance of metabolic and signal transduction pathways, and emergence of immunologically most relevant cell populations. That modelling however, has not been ‘deep’ modelling. Most of those mathematical models focused on concept rather than realism, and were small rather than ‘deep’.
With systems biology and functional genomics new types of both data analysis and modelling is developing, i.e. ones that try to integrate ‘all’ the relevant data into, thereby ‘deep’ (large), analytical and mathematical models of reality. Married to the deep data, these models enhance understanding of reality rather than potentiality. They help test hypotheses in hard rather than soft ways. They upgrade the human mind, assisted by computers and systems biology, from traditional to ‘deep’ thinking.
This ‘deep thinking’ is not easy. It constitutes a challenge. First, one has to befriend computers and systems biology. And then one then has to converse with them patiently, so as to ensure that computer programs make sense immunologically.