The area of transcriptomics analysis is among the more established in computational biology, having evolved in both technology and experimental design. Transcriptomics has a strong impetus to develop sophisticated computational methods due to the large amounts of available whole-genome datasets for many species and because of powerful applications in regulatory network reconstruction as well as elucidation and modeling of cellular transcriptional responses. While gene expression microarray data can be noisy and comparisons across experiments challenging, there are a number of sophisticated methods that aid in arriving at statistically and biologically significant conclusions. As such, computational transcriptomics analysis can provide guidance for analysis of results from newer experimental technologies. More recently, search methods have been developed to identify modules of genes, which exhibit coherent expression patterns in only a subset of experimental conditions. The latest advances in these methods allow to integrate multiple data types anddatasets, both experimental and computational, within a single statistical framework accounting for data confidence and relevance to specific biological questions. Such frameworks provide a unified environment for the exploration of specific biological hypothesis and for the discovery of coherent data patterns along with the evidence supporting them.

The overview of changes in protein levels or states in response to a growth condition, stress, mutation or metabolic engineering is invaluable in understanding the physiology of a microbial system. The lipid profile of the cell is similarly a valuable diagnostic of the cellular response and health, especially in context of survival in a fluctuating environment. To obtain comprehensive cellular models, post-transcriptional cell wide surveys at the levels of proteins and lipids are required. Both these fields have been greatly bolstered by the development of high throughput methods using mass spectrometry. Multiple strategies now exist for the identification of proteins, and numerous workflows to quantify protein abundance have also been developed. Cellular profiling such as these allows us to assess the potential of a microbial system for environmental applications such as bioremediation and bio-energy.

Presentation prepared for the May 12, 2008 Alternative Fuels and Vehicles Conference that describes DOE's current hydrogen fuel cell technology validation projects.