iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE.

Acinetobacter baumannii has become an urgent clinical threat due to the recent emergence of multi-drug resistant strains. There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen

Indivo: a personally controlled health record for health information exchange and communication

<p>Abstract</p> <p>Background</p> <p>Personally controlled health records (PCHRs), a subset of personal health records (PHRs), enable a patient to assemble, maintain and manage a secure copy of his or her medical data. Indivo (formerly PING) is an open source, open standards PCHR with an open application programming interface (API).</p> <p>Results</p> <p>We describe how the PCHR platform can provide standard building blocks for networked PHR applications. Indivo allows the ready integration of diverse sources of medical data under a patient's control through the use of standards-based communication protocols and APIs for connecting PCHRs to existing and future health information systems.</p> <p>Conclusion</p> <p>The strict and transparent personal control model is designed to encourage widespread participation by patients, healthcare providers and institutions, thus creating the ecosystem for development of innovative, consumer-focused healthcare applications.</p

Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

The emergence and spread of metallo-beta-lactamase–producing multidrug-resistant (MDR) Klebsiella pneumoniae is a serious public health threat, which is further complicated by the increased prevalence of colistin resistance. The link between antimicrobial resistance acquired by strains of Klebsiella and their unique metabolic capabilities has not been determined. Here, we reconstruct genome-scale metabolic models for 22 K. pneumoniae strains with various resistance profiles to different antibiotics, including two strains exhibiting colistin resistance isolated from Cairo, Egypt. We use the models to predict growth capabilities on 265 different sole carbon, nitrogen, sulfur, and phosphorus sources for all 22 strains. Alternate nitrogen source utilization of glutamate, arginine, histidine, and ethanolamine among others provided discriminatory power for identifying resistance to amikacin, tetracycline, and gentamicin. Thus, genome-scale model based predictions of growth capabilities on alternative substrates may lead to construction of classification trees that are indicative of antibiotic resistance in Klebsiella isolates

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution