It is computation time for bacteriology

Biology is an experimental science. In contrast to other natural sciences, physics, and chemistry, it has a very minor computational component. In order to support this statement with some data, I looked up 100 papers in the area of molecular and cellular biology published in 2008 in the journal Science and found that only 3 of them were purely computational. Another 15 were experimental papers with a significant computational component that was employed either to drive the experiment or to extend and interpret its results. Glancing through the Journal of Bacteriology (JB), one can see that the percentage of computational papers in this journal is even smaller. There are several reasons. First, this situation reflects the status quo, where experimental disciplines, such as genetics and biochemistry, play a key role in any scientific inquiry yielding worthwhile insights into the biology of the cell; hence the “if it ain’t broke, don’t fix it” (by bringing in some computational stuff) sentiment. Second, in contrast to the experimental approach, with its century-long history of success, its culture of careful controls, and its army of highly trained professionals, the computational approach is new, its controls (if any) are ill-defined, and it is carried out by a few “aliens” whose culture seems murky to experimentalists. Finally, there might be an increasing concern that traditional in vivo, in situ, and in vitro approaches will soon be replaced with this foreign in silico approach. Although the worries are legitimate (even if some are clearly exaggerated), there is still something going on with biology that biologists can no longer afford to ignore or to be afraid of. This “something” appears to comprise a fast-growing paradox in biology. It is clear that biology is experiencing an assault by advanced technologies, mathematics, and computing that have already led to such neologisms as “genome biology,” “systems biology,” “integrative biology,” “synthetic biology” and countless types of “-omics.” All this novelty is based solely on amazing progress in genome sequencing. We now face a state of affairs where more (in fact, much more) biological data is amenable to computational than experimental analysis. Thousands of sequenced genomes and many more species sampled by environmental sequencing provide materials for computational research. Almost four thousand genomes of various organisms are now available for data mining—see the GOLD database at www.genomesonline.org/ (6). The Human Microbiome Project, a new major National Institutes of Health initiative (www.genome.gov/27528386), will add 1,000 microbial genomes to the collection within this year. Metagenomics is another important player in the drastic increase in the amount of sequenced DNA and the number of genes and proteins in public databases. The Sorcerer II Global Ocean Sampling expedition more than doubled the size of the nucleotide database in 2007 (14). The Human Microbiome Project will also add its large metagenomic portfolio to the cohort of environmental DNA sequencing ventures. Fantastic developments in the new sequencing approaches and technologies, such as whole-ge