Systems biology

Abstract

The unequal race between technological and theoretical innovation in Computer Science has a mirror image in Systems Biology where the balance is even more biased toward the experimental side. The aim of Systems Biology is to produce a specification of natural systems, when artificial systems are usually already equipped with one 1 . As a consequence, Systems Biology is almost exclusively an experimental science where most of the innovation effort is geared toward the conception of devices able to extract more facts from biological systems. It has resulted in a modern trend of Systems Biology where high-throughput experiments are now producing big data and a massive inflation of publication volume (Figure 1). The lack of comprehensive integration of biological knowledge is hidden by the success stories of synthetic design using bio material: the initial aim of understanding how the basic components of the cell conspire in a complex and changing environment is modestly replaced by the science of hijacking cellular and genetic components in order to implement intelligently designed tasks. In turn these bio-devices enable biologists to set up more experiments and accumulate more data. This life cycle between technological innovation and data accumulation is encouraged by high impact journals in biology, which tend to give more credit to technological breakthroughs than (disputable) progress in understanding the ways of the cell.