Bioinformatics may be defined as the sciences of algorithmically obtaining understanding of living systems from large amounts of experimental data. Much of the early bioinformatics focused on the interpretation of DNA sequences, through searches for homologies and patterns. More recent bioinformatics extends to the recognition of patterns in mRNA expression profiles. Soon we will see bioinformatics help interpret proteomic profiles. Cellular bioinformatics is the lesser developed branch of bioinformatics that focuses on the understanding of the functioning living cell. As such it has to integrate DNA, mRNA, protein and metabolic data. Because of the complexity of the problem, it also needs to invoke mathematical modeling. Here mathematics enters not only as a data analysis and ordering tool, but also as a method to model the actual functioning of the living system.

A recent application of cellular bioinformatics developed a general method to help identify the mode of action of genes with silent phenotypes. From a metabolic control analysis argument it had been deduced that genes that are silent in terms of fluxes (such as growth rate), have an increased likelihood of loud in terms of changes in metabolite concentrations. Therefore by looking at the metabolome one should be able to understand more about at least a number of the genes with silent phenotypes by the more traditional growth assays. This was demonstrated for genes engaged in glycolysis and energy metabolism in yeast. Glycolytic intermediate levels were measured and it was shown that genes with similar functions exhibited the same co-responses. In a second, more blind approach, a metabolic fingerprint was made of wild type and mutant cells by NMR, and this fingerprint was deconvoluted by Principal Component Analysis and Discriminatory Function Analysis. The effects was that genes of similar functions clustered together. By analyzing mutants in 'silent genes' in terms of such patterns it should be possible to group them together with known genes and this should provide clues for the function of the unknown genes.

The branch of cellular bioinformatics that focuses on understanding on the basis of all the know experimental data is also called computational biochemistry.

Computational biochemistry: click for explanation and papers. 

Theoretical biochemistry: click for explanation and papers. 

Quantitative biochemistry: click for explanation and papers.