Editorial: Accelerating systems biology

Over the years, the long-standing questions concerning disease evolution, plausible cellular behavior and the hidden properties of organisms have become more and more pressing. They sparked a movement of experts from an array of scientific areas and stimulated them to work cooperatively with the ambitious aim of deciphering the language of nature. From the onset, the field of genomics has been built on many important discoveries beginning with the DNA double helix structure in the 1950s, reverse transcriptase in the late 1960s, recombinant DNA and restriction enzymes in the 1970s, and finally, the polymerase chain reaction discovery in the early 1980s. For some time the polymerase chain reaction was particularly revolutionary because it was the only method used to determine the base sequence of DNA. Wishing to understand how key components ‘converse’ in time, space and across multiple organizational levels became one of the driving factors that led scientists to sequence large eukaryotic genomes, such as that of human, and to the discovery of more than 20 000 different genes and of nearly a million proteins within the cell. The transition years between the 1990s and first decade of the 2000s were the time of the collection of such results that were later mathematically formalized and summarized in artificial wired diagrams. Soon after, the idea that the full understanding could be possible by a holistic rather than by an atomistic approach became slowly appealing. Investigation on the assembly of the cell’s elementary parts to form complex structures represented the main activity of a promising new research field named ‘Systems Biology’. Progress in this area required interdisciplinary breakthroughs and close links between wet and dry experimentations. Due to an enormous eager interest, synthetic formalizations (or models) grew rapidly in number and size, models that later could be stored in resource databases easily accessible to anyone of interest. ‘BioModels.Net’ is one of such data resources. It is a database of models ‘curated’ by Le Novère and co-workers. In their paper, they offer an overview of some peculiar characteristics of the data bank in light of the computerized web services they provide to ease the user impact. In spite of this, models became unmanageable by manual inspection because of their size and compelled biologists to reduce drastically the kind and quality of the achievable analysis procedures. Those whose job was that of modeling living systems very soon acknowledged how badly the effectiveness of such procedures is undermined by the complexity of the resulting models, which, in fact, are a reflection of the complexity of nature itself. If computer-driven experimentation had not been introduced almost alongside, any outcome would have been incomplete. Many automatic analysis procedures were made available to the scientific community, such as steady state, bifurcation, robustness analyses, model checking, simulation, etc. With regard to this, ‘CaliBayes’ and ‘BASIS’ represent the efforts made by Wilkinson and co-authors to collect and integrate some tools for calibration, simulation and storage of biological simulation models. Alongside them, Uhrmacher’s group has also proposed a valuable modular framework, ‘James II’. It works to ease the process of designing wetlab experiments through advanced routines and algorithms. Both the goals match: they aim at providing new insights for new models and to feed back the so-called ‘hypothesisdriven science cycle’. Dry laboratories proliferated rapidly. To better describe their biological dynamics of interest from precise perspectives, each defined a new appropriate language. As a first result, this gave rise to an explosion of new software tools along with an overall ‘Babel of voices’, since each one of them talked a different dialect. The need for a common language was becoming a ‘must’. Standard languages (like SBML and CellML) were created with the primary aim of making cooperation among software tools possible. They were able to bridge the gap between wet and dry scientists by broadening the range of the available computational methods. They definitely reached their goal. BRIEFINGS IN BIOINFORMATICS. VOL 11. NO 3. 267^269 doi:10.1093/bib/bbq012