**3. Role of transcriptomics**

such as gene and protein functions, interactions, metabolic and regulatory pathways. Bioin‐ formatics analysis will facilitate and quicken the analysis of cellular process to understand the cellular mechanism to treat and control microbial cells as factories. The next decade will belong to understanding molecular mechanism and cellular manipulation using the integra‐

Bioremediation is an option that utilizes microbes to remove many contaminants from the environment by a diversity of enzymatic processes. The major positive shades of bioremedia‐ tion are comparatively low-cost and techniques based on low-technology (Robb et al, 1995) which generally have a high public acceptance and can often be carried out on site. However, it will not always be suitable as the range of contaminants on which it is effective is limited, the time scales involved are relatively long, and the residual contaminant levels achievable may not always be appropriate. Varying degrees of success bioremediation has been used at a number of sites worldwide (Ajay et al, 2009). Here, we attempted to assist by providing information how the bioremediation is linked with cutting edge sciences like genomics, transcriptomics, proteomics, interactomics and bioinformatics (Fleming et al. 1993., Schena et

Examining the presence and expression of the key genes involved in bioremediation can yield more information on microbial processes than analysis of 16S rRNA sequences (Rogers and McClure. 2003). In general, there is a positive correlation between the relative abundance of the genes involved in bioremediation and the potential for contaminant degradation (Rogers

However, the genes for bioremediation can be present but not expressed. Therefore, there has been an increased emphasis on quantifying the levels of mRNA for key bioremediation genes. Often, increased mRNA concentrations can be, at least qualitatively, associated with higher rates of contaminant degradation (Schneegurt and Kulpa. 1998). For example, the concentra‐ tions of mRNA for *nahA* a gene involved in aerobic degradation of naphthalene were positively correlated with rates of naphthalene degradation in hydrocarbon-contaminated soil (Fleming et al. 1993).The reduction of soluble ionic mercury, Hg(II),to volatile Hg(0), is one mechanism for removing mercury from water; the concentration of mRNA for *merA* a gene involved in Hg(II) reduction was highest in mercury-contaminated waters with the highest rates of Hg (II) reduction (Nazaret et al. 1994). However, the concentration of *merA* was not always propor‐ tional to the rate of Hg (II) reduction (Nazaret et al. 1994., Jeffrey et al. 1996), illustrating that factors other than gene transcription can control the rates of bioremediation processes.

Highly sensitive methods that can detect mRNA for key bioremediation genes in single cells are now available (Bakermans and Madsen. 2002). This technique, coupled with 16S rRNA probing of the same environmental samples, could provide data on which phylogenetic groups of organisms are expressing the genes of interest. Analysis of the mRNA concentrations for genes other than those directly involved in bioremediation might yield additional insights into

al. 1998., Sikkema et al. 1995., Kuhner et al. 2005., Ellis et al. 2000).

**2. Genetic analysis of genes involved**

and McClure. 2003., Schneegurt and Kulpa. 1998).

tion of bioinformatics.

376 Applied Bioremediation - Active and Passive Approaches

The subset of genes transcribed in any given organism is called the transcriptome, which is a dynamic link between the genome, the proteome and the cellular phenotype. The regulation of gene expression is one of the key processes for adapting to changes in environmental conditions and thus for survival. Transcriptomics describes this process in a genome wide range. DNA microarrays are an extremely powerful platform in transcriptomics that enable determination of the mRNA expression level of practically every gene of an organism (Schena et al. 1998., Golyshin et al. 2003., Diaz. 2004) The most challenging issue in microarray experiments is elucidation of data (Dharmadi and Gonzalez. 2004). Often, hundreds of genes may be up- and/or down-regulated in a particular stress condition. In this context, several statistical issues become tremendously complex, including accounting for random and systematic errors and performing poor analysis.
