Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins

Background  

Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.     

Parameter estimation for the distribution of single cell lag times

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level.We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.     

Distinguishing Drift and Selection Empirically: “The Great Snail Debate” of the 1950s

Biologists and philosophers have been extremely pessimistic about the possibility of demonstrating random drift in nature, particularly when it comes to distinguishing random drift from natural selection. However, examination of a historical case – Maxime Lamotte’s study of natural populations of the land snail, Cepaea nemoralis in the 1950s – shows that while some pessimism is warranted, it has been overstated. Indeed, by describing a unique signature for drift and showing that this signature obtained in the populations under study, Lamotte was able to make a good case for a significant role for␣drift. It may be difficult to disentangle the causes of drift and selection acting in a population, but it is not (always) impossible.     

Temperature influences the expression of fimbriae and flagella in <Emphasis Type="Italic">Hafnia alvei</Emphasis> strains: an immunofluorescence study

Hafnia alvei, a Gram negative bacillus related to the Enterobacteriaceae family, is considered an opportunistic pathogen of several animal species and humans. In this communication, we describe fimbrial-like structures from different strains of H. alvei that cannot be easily ascribed to any of the previously reported fimbrial types in this species (type I or type III). Polymerase chain reaction (PCR) and immunofluorescence assays were carried out to study fimbriae and flagella in H. alvei strains isolated from different sources. No correlation between the results obtained by PCR and those obtained by phenotypic methods were found, and the antibodies used gave cross or different recognition patterns of the surface structures present in these strains. We report as well that strain and growth temperature influence fimbriation and expression of flagella in human and animal isolates of H. alvei. This study also indicates that the absence of fimbriae have a significant positive influence on the initial adhesion of H. alvei to human epithelial cells.