An in vivo control map for the eukaryotic mRNA translation machinery

Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNAi to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5′untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5′UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis

Specificity of the osmotic stress response in Candida albicans highlighted by quantitative proteomics

We are grateful to the BBSRC for funding the CRISP Consortium (Combinatorial Responses in Stress Pathways) under the SABR Initiative (Systems Approaches to Biological Research) (BB/F00513X/1; BB/F005210/1). AJPB was also funded by the BBSRC (BB/K017365/1), the ERC (C-2009-AdG-249793), the Wellcome Trust (097377), the MRC (MR/M026663/1), and the MRC Centre for Medical Mycology and the University of Aberdeen (MR/M026663/1).Peer reviewedPublisher PD

Extracting isovolumes from three-dimensional torso geometry using PROLOG

Three-dimensional (3-D) Imite element torso models are widely used to simulate deflbrillation field quantities, such as potential, gradient, and current density. These quantities are computed at spatial nodes that comprise the torso model. These spatial nodes typically number between 105 and 106, which makes the comprehension of torso deli brillat ion simulation output difficult. Therefore, the objective of this study is to rapidly prototype software to extract a subset of the geometric model of the torso for visualization in which the nodal information associated with the geometry of the model meets a specified threshold value (e.g., minimum gradient). The data extraction software is implemented in PROLOG, which is used to correlate the coordinate, structural, and nodal data of the torso model. A PROLOG-based environment has been developed and is used to rapidly design and test new methods for sorting, collecting, and optimizing data extractions from defibrillation simulations in a human torso model for subsequent visualization. © 1998 IEEE

Effects of cardiac anisotropy on modeling transvenous defibrillation in the human thorax

The objective of this study is to determine the effects of cardiac tissue anisotropy on transvenous defibrillation fields in a human torso model. The study is implemented with a physiologically realistic 3-D finite element model of the human thorax. The model computes potential and potential gradient distributions within the heart from a knowledge of defibrillation shock strength, defibrillation electrode location, and the relative conductivities of the interior thorax. Coil electrodes were placed in the right ventricular cavity and the superior vena cava. Results are compared between a model with an isotropic myocardium and a model with an anisotropic myocardium. Comparison of the potential and potential gradient distributions within the myocardium between the isotropic and anisotropic models yielded root mean square errors of 4.9% and 19.4%, respectively, and correlation coefficients of 0.999 and 0.981, respectively. These results indicate that cardiac anisotropy and fiber orientation do not significantly affect transvenous defibrillation fields

Optimizing dual threshold shocks with right- and left-ventricular electrodes: Simulating Defibrillation with a human thorax model

This research focuses on developing new implantable cardioverter defibrillator (ICD) dual lead configurations that reduce the defibrillation threshold (DFT) energy by delivering a second threshold shock in the area where the conventional shock\u27s electric field is weakest. The objective of this study is to optimize electrode placements for lead systems including left-ventricular (LV) electrodes. A physiologically realistic 3D finite element model of the human thorax is employed to compute DFTs. The lead configurations investigated consist of a conventional lead system (TRIADTM, Guidant Corporation) and additional LV shocking electrodes placed in the apical and basal portion of the posteriolateral coronary vein or directly within the TRIAD system\u27s weak field region. The LV electrodes measure 50 mm in length and 1 mm in diameter. The computed DFT energy for the TRIAD is 6.2 J, falling within one standard deviation of the mean DFT reported in clinical studies using the TRIAD leads. LV leads located in the apical and basal portion of the posteriolateral coronary vein result in a DFT of 3.1 J, a 50% reduction from the TRIAD alone. LV leads placed in the anterior, middle, and posterior TRIAD weak field result in a DFT of 2.9 J, 2.7 J., and 3.5 J, respectively, corresponding to a 44-56% reduction in DFT from the TRIAD. The results indicate that an additional electrode placed in the proximity of the TRIAD weak field is just as effective in reducing DFTs as one placed directly within the weak field

Defibrillation efficacy of different electrode placements in a human thorax model

The objective of this study was to measure the defibrillation threshold (DFT) associated with different electrode placements using a three- dimensional anatomically realistic finite element model of the human thorax. Coil electrodes (Endotak DSP, model 125, Guidant/CPI) were placed in the RV apex along the lateral wall (RV), withdrawn 10 mm away from the RV apex along the lateral wall (RVprox), in the RV apex along the anterior septum (RVseptal), and in the SVC. An active pulse generator (can) was placed in the subcutaneous prepectoral space. Five electrode configurations were studied: RV→SVC, Rv(prox)→SVC, RV(SEPTAL)→SVC, RV→Can, and RV→SVC+Can. DFTs are defined as the energy required to produce a potential gradient of at least 5 V/cm in 95% of the ventricular myocardium. DFTs for RV→SVC, RV(prox) →SVC, RV(septal)→SVC, RV→Can, and RV→SVC+Can were 10, 16, 7, 9, and 6J, respectively. The DFTs measured at each configuration fell within one standard deviation of the mean DFTs reported in clinical studies using the Endotak leads. The relative changes in DFT among electrode configurations also compared favorably. This computer model allows measurements of DFT or other defibrillation parameters with several different electrode configurations saving time and cost of clinical studies