Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steadystate models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.

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SAE was performed with high technical success and efficacy, but the outcomes showed nontrivial morbidity rates. Elderly patients with thrombocytopenia and hydrothorax after SAE, and patients who require secondary interventions, should be monitored for complications.

Background: To determine the relationship between adipose tissue and skeletal muscle measurements on computed tomography (CT) and overall survival and major postoperative complications in patients with softtissue sarcoma (STS). Methods:The retrospective study included 137 STS patients (75 men, 62 women; mean age, 53 years, SD 17.7; mean BMI, 28.5, SD 6.6) who had abdominal CT exams. On a single CT image, at the L4 pedicle level, measurements of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and skeletal muscle area and attenuation were obtained using clinical PACS and specialized segmentation software. Clinical information was recorded, including STS characteristics (size, depth, grade, stage, and site), overall survival, and postoperative complications. The relationships between CT metrics and survival were analyzed using Cox proportional hazard models and those between CT metrics and postoperative complications using logistic regression models.Results: There were 33 deaths and 41 major postoperative complications. Measured on clinical PACS, the psoas area (P=0.003), psoas index (P=0.006), psoas attenuation (P=0.011), and total muscle attenuation (P=0.023) were associated with overall survival. Using specialized software, psoas attenuation was also associated with overall survival (P=0.018). Adipose tissue metrics were not associated with survival or postoperative complications. Conclusions:In STS patients, CT-derived muscle size and attenuation are associated with overall survival. These prognostic biomarkers can be obtained using specialized segmentation software or routine clinical PACS.

In the study's academic ED, introduction of ELISA D-dimer testing was accompanied by an increase in PE evaluations, D-dimer testing, and pulmonary vascular imaging; there was no observed change in the rate of PE diagnosis.