Systems biology is seen as a highly productive approach to solving complex biological problems such as self-organization. Although definitions vary, most agree that systems biology is the application of mathematical and theoretical approaches to understand how the interaction of metabolites, proteins, RNA, genes and cells can lead to often counterintuitive and unexpected systems-level behaviour. Early examples of systems biology include Turing’s reaction-diffusion theory of morphogenesis in which spatial patterns emerge from simple molecular rules (Fig. 1), and the voltage-dependent opening of ion channels leading to an all-or-nothing change in the electrical response of nerve cells.
A central concept in systems theory is that positive feedback — mediated by chemical, electrical or mechanical signals — can lead to instabilities and switching, and in turn to spatial and/or temporal patterns or oscillations. Recent models have been developed for the cell cycle, bacterial chemotaxis2, cell differentiation in response to growth factors, and the beating of the heart and of flagella — all self-organized systems3.
Systems biology has also come to mean the modelling of systems at the whole-organism level, informed by new ‘omics’ technologies. It is now possible to measure protein levels in a small number of cells using mass spectrometry, to quantify gene expression using microarrays and next-generation sequencing, and to use genome-wide RNA-interference screens to accelerate the discovery of genes involved in cellular processes such as membrane trafficking and motility4,5.
The huge data sets generated mean that effective analysis lags behind; but important discoveries show that general principles will be uncovered. For example, within networks of transcription factors, recurring elements (or motifs) occur more often than random, indicating an underlying, self-organizing structure6.
Improvements in sample preparation for electron cryomicroscopy, combined with the rapid increase in the number of solved protein structures, has enabled the construction of atom-scale models of the complex organelles, such as the axoneme7, and the leading edge of a crawling cells. This holds promise for modelling an entire cell at the atomic level — remarkable considering that a bacterium contains 1012 atoms, not counting water.
Thirty years ago, Sulston and Horvitz painstakingly reconstructed the entire cellular development of a nematode worm; what previously took a decade can now be visualized in real time and applied to other species, thanks to new microscope techniques and image-processing algorithms8.