The scope of my research includes bioinformatics reconstruction of key metabolic and associated regulatory pathways/networks in reference genomes from the human microbiota using comparative genomics approaches. I have made significant contributions to the field of computational genomics and bioinformatics—specifically, in the area of comparative genomics techniques designed to define components of the regulatory and metabolic pathways in bacteria. I have pioneered the development of workflows and databases for transcriptional regulatory network inference and metabolic reconstruction and have applied them for a number of bacterial genomes, target metabolic subsystems, and transcriptional regulons. In particular, I am leading the development of the RegPredict platform and the RegPrecise database for the inferring and reconstruction of transcriptional regulatory networks in diverse taxonomic groups of bacteria. Using a comparative genomics approach, my group identified candidate binding sites for a large number of transcription factors in bacterial genomes. This approach was previously utilized by us to predict novel regulons and describe regulatory networks in >10 groups of microorganisms including Bacillus, Shewanella, Thermotoga, Staphylococcus, Streptococcus, Lactobacillus, Bifidobacetria, and Bacteroides. I am also leading the analysis and curation of nearly 50 metabolic subsystems in the SEED microbial genome database that are widely used by the scientific community.
The scope of my research includes bioinformatics reconstruction of key metabolic and associated regulatory pathways/networks in reference genomes from the human microbiota using comparative genomics approaches. I have made significant contributions to the field of computational genomics and bioinformatics—specifically, in the area of comparative genomics techniques designed to define components of the regulatory and metabolic pathways in bacteria. I have pioneered the development of workflows and databases for transcriptional regulatory network inference and metabolic reconstruction and have applied them for a number of bacterial genomes, target metabolic subsystems, and transcriptional regulons. In particular, I am leading the development of the RegPredict platform and the RegPrecise database for the inferring and reconstruction of transcriptional regulatory networks in diverse taxonomic groups of bacteria. Using a comparative genomics approach, my group identified candidate binding sites for a large number of transcription factors in bacterial genomes. This approach was previously utilized by us to predict novel regulons and describe regulatory networks in >10 groups of microorganisms including Bacillus, Shewanella, Thermotoga, Staphylococcus, Streptococcus, Lactobacillus, Bifidobacetria, and Bacteroides. I am also leading the analysis and curation of nearly 50 metabolic subsystems in the SEED microbial genome database that are widely used by the scientific community.