A Novel Optimization-Based Tool to Automate Infeasible Cycle-Free Gapfilling of Genome-Scale Metabolic Models

SUMMARYStoichiometric metabolic modeling, particularly Genome-Scale Models (GSMs), is now an indispensable tool for systems biology. The model reconstruction process typically involves collecting information from public databases; however, incomplete systems knowledge leaves gaps in any reconstruction. Current tools for addressing gaps use databases of biochemical functionalities to address gaps on a per-metabolite basis and can provide multiple solutions, but cannot avoid Thermodynamically Infeasible Cycles (TICs), invariably requiring lengthy manual curation. To address these limitations, this work introduces an optimization-based multi-step method named OptFill which performs TIC-avoiding whole-model gapfilling. We applied OptFill to three fictional prokaryotic models of increasing sizes and to a published GSM of Escherichia coli, iJR904. This application resulted in holistic and infeasible cycle free gapfilling solutions. Part of OptFill can, in addition, be adapted to automate inherent TICs identification in any GSM, such as iJR904. Overall, OptFill can address critical issues in automated development of high-quality GSMs.In BriefStoichiometric models of metabolism are useful in studying metabolic interactions in biological systems, but are labor-intensive to create, particularly when addressing gaps or cycles in metabolic reconstruction process. Introduced here is a novel tool, OptFill, which can be used to address both gaps and cycles in model reconstruction, increasing automation.HighlightsThis work presents an alternative to state-of-the-art methods for gapfilling.Unlike current methods, this method is holistic and infeasible cycle free.This method is applied to three test and one published model.This method might also be used to address infeasible cycling.

Comparative Analyses of Transport Proteins Encoded within the Genomes of Bdellovibrio bacteriovorus HD100 and Bdellovibrio exovorus JSS

<i>Bdellovibrio</i>, δ-proteobacteria, including <i>B. bacteriovorus</i> (Bba) and <i>B. exovorus</i> (Bex), are obligate predators of other Gram-negative bacteria. While Bba grows in the periplasm of the prey cell, Bex grows externally. We have analyzed and compared the transport proteins of these 2 organisms based on the current contents of the Transporter Classification Database (TCDB; www.tcdb.org). Bba has 103 transporters more than Bex, 50% more secondary carriers, and 3 times as many MFS carriers. Bba has far more metabolite transporters than Bex as expected from its larger genome, but there are 2 times more carbohydrate uptake and drug efflux systems, and 3 times more lipid transporters. Bba also has polyamine and carboxylate transporters lacking in Bex. Bba has more than twice as many members of the Mot-Exb family of energizers, but both may have energizers for gliding motility. They use entirely different types of systems for iron acquisition. Both contain unexpectedly large numbers of pseudogenes and incomplete systems, suggesting that they are undergoing genome size reduction. Interestingly, all 5 outer-membrane receptors in Bba are lacking in Bex. The 2 organisms have similar numbers and types of peptide and amino acid uptake systems as well as protein and carbohydrate secretion systems. The differences observed correlate with and may account, in part, for the different lifestyles of these 2 bacterial predators.

Assessment of Technology Acceptance in Intensive Care Units

The process of deploy a technology in critical services need to be very careful planned and processed. As an example it is the Intensive Care Unit (ICU). In the ICU the patients are in critically ill conditions and there are not available time to make experiences or to develop incomplete systems. With the objective to improve the implementation process, the same should be accompanied in order to understand the environment and user behaviour. In this case and with the goal to evaluate the implementation process, an assessment model was applied to a real system called INTCare. INTCare is a Pervasive Intelligent Decision Support System (PIDSS). It was deployed in the ICU of Centro Hospitalar do Porto and was evaluated using the Technology Acceptance Model 3 (TAM). This assessment was made using the four constructs proposed by the TAM and a questionnaire-based approach guided by the Delphi Methodology. The results obtained so far show that although the users are satisfied with the offered information recognizing this importance, they demand for a faster system. This work present the main results achieved and suggest one way to follow when some technology is deployed in an environment like is ICU.