Pathogenomes and variations in Shiga toxin production among geographically distinct clones of O113:H21.

Infections with globally disseminated Shiga toxin-producing (STEC) of the O113:H21 serotype can progress to severe clinical complications, such as hemolytic uremic syndrome (HUS). Two phylogeographically distinct clonal complexes have been established by multi locus sequence typing (MLST). Infections with ST-820 isolates circulating exclusively in Australia have caused severe human disease, such as HUS.

Pathogenomes of Atypical Non-shigatoxigenic NSF/SF O157:H7/NM: Comprehensive Phylogenomic Analysis Using Closed Genomes.

The toxigenic conversion of strains by Shiga toxin-converting (Stx) bacteriophages were prominent and recurring events in the stepwise evolution of enterohemorrhagic (EHEC) O157:H7 from an enteropathogenic (EPEC) O55:H7 ancestor. Atypical, attenuated isolates have been described for both non-sorbitol fermenting (NSF) O157:H7 and SF O157:NM serotypes, which are distinguished by the absence of Stx, the characteristic virulence hallmark of Stx-producing (STEC). Such atypical isolates either never acquired Stx-phages or may have secondarily lost during the course of infection, isolation, or routine subculture; the latter are commonly referred to as LST (Lost Shiga Toxin)-isolates.

A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum.

Background: According to the World Health organization, half the world's population is at risk of contracting malaria. They estimated that in 2010 there were 219 million cases of malaria, resulting in 660,000 deaths and an enormous economic burden on the countries where malaria is endemic. The adoption of various high-throughput genomics-based techniques by malaria researchers has meant that new avenues to the study of this disease are being explored and new targets for controlling the disease are being developed.

Python as a federation tool for GENESIS 3.0.

The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture.