J Microbiol Biotechnol 2009, 19:1127–1134 PubMedCrossRef 47 Fong

J Microbiol Biotechnol 2009, 19:1127–1134.PubMedCrossRef 47. Fong SS, Nanchen A, Palsson BO, Sauer U: Latent pathway activation and increased pathway Epacadostat purchase capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J Biol Chem 2006, 281:8024–8033.PubMedCrossRef 48. Kinnersley MA, Holben WE, Rosenzweig F: E unibus plurum: Genomic analysis of an experimentally evolved polymorphism

in Escherichia coli. PLOS Genet 2009, 5:e1000713.PubMedCrossRef 49. Notley-McRobb L, Ferenci T: The generation of multiple co-existing mal-regulatory mutations through polygenic evolution in glucose-limited populations of Escherichia coli. Environ Microbiol 1999, 1:45–52.PubMedCrossRef 50. Blattner FR, Plunkett G, Bloch CA, Perna NT, Burland V, et al.: The complete genome sequence of Escherichia coli K-12. Science 1997, 277:1453–1462.PubMedCrossRef 51. Tsuru S, Ichinose J, Kashiwagi A, Ying BW, Kaneko K, et al.: Noisy cell growth rate leads to fluctuating protein concentration in bacteria. Phys Biol 2009, 6:036015.PubMedCrossRef 52. Freed NE, Silander OK, Stecher B, Böhm A, Hardt WD, et al.: A simple screen to identify promoters conferring high levels of phenotypic noise. PLOS Genet 2008, 4:e1000307.PubMedCrossRef Competing interests The authors declare that they have no competing interests. Selleck Defactinib Authors’ contributions Conceived and designed the experiments: NN MA. Performed the experiments: NN TB. Analyzed

click here the data: NN TB MA. Wrote the manuscript: NN MA. All authors read and approved the final manuscript.”
“Background With the widespread use of culture-independent, high-throughput sequencing

technologies, ecologists have begun to describe the diversity of microbial communities that were previously difficult to detect e.g., [1–3]. Given the newness of these data types and the fact that the aims and goals of microbial studies are usually similar to those of macro-ecology, microbial ecologists often use methods from classical community ecology to analyze their data. These include Shannon’s H [4], Berger-Parker Evenness [5], rarefaction, and ordination [6]. While the use of established ecological metrics to analyze microbial diversity may sometimes be appropriate [7], the data produced by ecologists surveying macro-organismal communities differ from data obtained by high-throughput sequencing of microbial communities in three key ways. First, in contrast to plant and animal assemblages, microbial assemblages are typically made up of more than one domain of life, thus necessitating the ability to quantify diversity across very disparate organism types. Second, many classical indices assume ecological communities are composed of unique species. However, traditional biological species concepts do not fit the natural histories of many microbial taxa that routinely undergo non-homologous recombination [8–10] and sometimes lack sexual reproduction.

Comments are closed.