Characterization of growth and metabolism of the haloalkaliphile Natronomonas pharaonis

Natronomonas pharaonis is an archaeon adapted to two extreme conditions: high salt concentration and alkaline pH. It has become one of the model organisms for the study of extremophilic life. Here, we present a genome-scale, manually curated metabolic reconstruction for the microorganism. The reconstruction itself represents a knowledge base of the haloalkaliphile's metabolism and, as such, would greatly assist further investigations on archaeal pathways. In addition, we experimentally determined several parameters relevant to growth, including a characterization of the biomass composition and a quantification of carbon and oxygen consumption. Using the metabolic reconstruction and the experimental data, we formulated a constraints-based model which we used to analyze the behavior of the archaeon when grown on a single carbon source. Results of the analysis include the finding that Natronomonas pharaonis, when grown aerobically on acetate, uses a carbon to oxygen consumption ratio that is theoretically near-optimal with respect to growth and energy production. This supports the hypothesis that, under simple conditions, the microorganism optimizes its metabolism with respect to the two objectives. We also found that the archaeon has a very low carbon efficiency of only about 35%. This inefficiency is probably due to a very low P/O ratio as well as to the other difficulties posed by its extreme environment

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

How Firms Adapt and Interact in Open Source Ecosystems: Analyzing Stakeholder Influence and Collaboration Patterns

[Context and motivation] Ecosystems developed as Open Source Software (OSS) are considered to be highly innovative and reactive to new market trends due to their openness and wide-ranging contributor base. Participation in OSS often implies opening up of the software development process and exposure towards new stakeholders. [Question/Problem] Firms considering to engage in such an environment should carefully consider potential opportunities and challenges upfront. The openness may lead to higher innovation potential but also to frictional losses for engaged firms. Further, as an ecosystem progresses, power structures and influence on feature selection may fluctuate accordingly. [Principal ideas/results] We analyze the Apache Hadoop ecosystem in a quantitative longitudinal case study to investigate changing stakeholder influence and collaboration patterns. Further, we investigate how its innovation and time-to-market evolve at the same time. [Contribution] Findings show collaborations between and influence shifting among rivaling and non-competing firms. Network analysis proves valuable on how an awareness of past, present and emerging stakeholders, in regards to power structure and collaborations may be created. Furthermore, the ecosystem’s innovation and time-to-market show strong variations among the release history. Indications were also found that these characteristics are influenced by the way how stakeholders collaborate with each other

Potential for La Crosse virus segment reassortment in nature

The evolutionary success of La Crosse virus (LACV, family Bunyaviridae) is due to its ability to adapt to changing conditions through intramolecular genetic changes and segment reassortment. Vertical transmission of LACV in mosquitoes increases the potential for segment reassortment. Studies were conducted to determine if segment reassortment was occurring in naturally infected Aedes triseriatus from Wisconsin and Minnesota in 2000, 2004, 2006 and 2007. Mosquito eggs were collected from various sites in Wisconsin and Minnesota. They were reared in the laboratory and adults were tested for LACV antigen by immunofluorescence assay. RNA was isolated from the abdomen of infected mosquitoes and portions of the small (S), medium (M) and large (L) viral genome segments were amplified by RT-PCR and sequenced. Overall, the viral sequences from 40 infected mosquitoes and 5 virus isolates were analyzed. Phylogenetic and linkage disequilibrium analyses revealed that approximately 25% of infected mosquitoes and viruses contained reassorted genome segments, suggesting that LACV segment reassortment is frequent in nature

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735