Three computational tools for predicting bacterial essential genes

Essential genes are those genes indispensable for the survival of any living cell. Bacterial essential genes constitute the cornerstones of synthetic biology and are often attractive targets in the development of antibiotics and vaccines. Because identification of essential genes with wet-lab ways often means expensive economic costs and tremendous labor, scientists changed to seek for alternative way of computational prediction. Aiming to help to solve this issue, our research group (CEFG: group of Computational, Comparative, Evolutionary and Functional Genomics, http://cefg.uestc.edu.cn) has constructed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. To ensure reliable predictions, the investigated species should belong to the same family (for EGP) or phylum (for CEG_Match and Geptop) with one of the reference species, respectively. As the pilot software for the issue, predicting accuracies of them have been assessed and compared with existing algorithms, and note that all of other published algorithms have not any formed online services. We hope these services at CEFG will help scientists and researchers in the field of essential genes.

[Current status of theoretical studies on essential genes in microbes]

Essential genes are indispensable for the survival of an organism in optimal conditions. Recently, study on essential gene is becoming a hot topic of microbiology, genomics, and bioinformatics. This paper described the experiments that determined essential genes in some microbes and the theoretical researches on essential genes were reviewed. The major content contained comparison of essential genes and non-essential genes based on information on evolutionary conservation and sequence composition, and in silico prediction of essential genes, and analysis of the chromosomal distributions of essential genes. Finally, related progresses were concluded and the open problems were pointed out.

Measurable notions of complexity and their relationship to biological complexity

Complexity is often invoked as a motivation for a systems approach to biology. We review three measurable notions of complexity from the areas of computation and data analysis. These measures have each led to mathematical theory and to further insight on the complexity of objects, demonstrating the benefits of having a well-defined measure of complexity. Each measure is applicable in the study of particular biological systems; however, none is satisfactory to serve as a universal measure of biological complexity. The study of biological systems will likely require numerous measures of complexity, each appropriate for analysis in specific settings.