Model systems reductionist approach

Much scientific advance comes from a reductionist approach take a complex system apart into its components, understand these, and then build a useful model of the more complex system. This frequently works quite well. For example, if someone gave you a clock and asked you to discover how it works, you would probably get started by taking it apart, understanding the constituent parts, and learning how they work together by a process of reassembly. Indeed, one can pretty much predict the properties of a clock based on this reductionist approach. 

The reductionist approach to science has been extremely successful for several centuries. Its goal was the postulation of a hypothesis, later of a model, and ultimately of a theory, from experimental data (induction) or the postulation of a model with subsequent experimental verification (deduction). Parallel to the availability of a hyper-exponentially growing amount of data, a complementary approach is taking shape, termed a data-driven or systems approach. 

The data-driven approach does not render the model-driven approach obsolete or superfluous. In fact, data-driven approaches have to rely, and will do so for the foreseeable future, on model-driven advances in single fields of science (Aebersold, 2000). In addition, without knowledge gained with reductionist approaches interpretation of data with the systems approach is not possible. Novel tools such as pathway analysis and pattern recognition, and novel kinds of research questions such as the investigation of hypotheses based on the interaction of several system components, become possible with the systems approach. 

While reductionism in science may be frequently debated in the halls of HPS departments, the fact remains that faith in the validity of reductionist approach pervades, indeed is not questioned within, the scientific research community at large. Two interesting elements arise when considering rejection of computational simulations of their subject matter by chemists, then. As discussed previously, the rejection may arise because computational methods cannot simulate something so complex as a real chemical system. This can be seen as a rejection of reductionism we cannot understand the whole system by understanding the basic physical laws underlying it when the system is so complex. On the other hand, in the number of approximations that computational models must make in order to be able to model even a helium atom, they defy absolute reductionism. In this case, their rejection is in favour of reductionism. 

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