RESUMEN
Physiological processes in the human body can be predicted by mathematical models. Medical Decision Support Systems (MDSS) might exploit these predictions when optimizing therapy settings. In critically ill patients depending on mechanical ventilation, these predictions should also consider other organ systems of the human body. In a previously presented framework we combine elements of three model families: respiratory mechanics, cardiovascular dynamics and gas exchange. Computing combinations of moderately complex submodels showed to be computationally costly thus limiting the applicability of those model combinations in an MDSS. A decoupled computing approach was therefore developed, which enables individual evaluation of every submodel. Direct model interaction is not possible in separate calculations. Therefore, interface signals need to be substituted by estimates. These estimates are iteratively improved by increasing model detail in every iteration exploiting the hierarchical structure of the implemented model families. Simulation error converged to a minimum after three iterations. Maximum simulation error showed to be 1.44% compared to the original common coupled computing approach. Simulation error was found to be below measurement noise generally found in clinical data. Simulation time was reduced by factor 34 using one iteration and factor 13 using three iterations. Following the proposed calculation scheme moderately complex model combinations seem to be applicable for model based decision support.
Asunto(s)
Modelos Biológicos , Algoritmos , Sistemas de Apoyo a Decisiones Clínicas , HumanosRESUMEN
Potential harmful effects of ventilation therapy could be reduced by model-based predictions of the effects of ventilator settings to the patient. To obtain optimal predictions, the model has to be individualized based on patients' data. Given a nonlinear model, the result of parameter identification using iterative numerical methods depends on initial estimates. In this work, a feasible hierarchical identification process is proposed and compared to the commonly implemented direct approach with randomized initial values. The hierarchical approach is exemplarily illustrated by identifying the viscoelastic model (VEM) of respiratory mechanics, whose a priori identifiability was proven. To demonstrate its advantages over the direct approach, two different data sources were employed. First, correctness of the approach was shown with simulation data providing controllable conditions. Second, the clinical potential was evaluated under realistic conditions using clinical data from 13 acute respiratory distress syndrome (ARDS) patients. Simulation data revealed that the success rate of the direct approach exponentially decreases with increasing deviation of the initial estimates while the hierarchical approach always obtained the correct solution. The average computing time using clinical data for the direct approach equals 4.77 s (SD = 1.32) and 2.41 s (SD = 0.01) for the hierarchical approach. These investigations demonstrate that a hierarchical approach may be beneficial with respect to robustness and efficiency using simulated and clinical data.
Asunto(s)
Modelos Biológicos , Mecánica Respiratoria/fisiología , Simulación por Computador , Elasticidad , Humanos , Modelos Lineales , Respiración Artificial , Síndrome de Dificultad Respiratoria/fisiopatología , ViscosidadRESUMEN
Restriction fragment length polymorphisms (RFLPs) have been used to characterise the genetic diversity of wheat (Triticum aestivum) germplasm. One hundred and twenty-four accessions comprising all major Australian wheat varieties and lines important for breeding purposes were assayed for RFLPs with clones of known genetic location and selected to give uniform genome coverage. The objectives of this study were to determine RFLP-based genetic similarity between accessions and to derive associations between agronomically significant traits and RFLP phenotypes. Ninety-eight probes screened against genomic DNA digested with five restriction endonucleases detected a total of 1968 polymorphic fragments. Genetic similarity (GS) calculated from the RFLP data ranged from 0.004 to 0.409 between accessions, with a mean of 0.18. Cluster analysis based on GS estimates produced four groupings that were generally consistent with available pedigree information. Comparisons of the RFLP phenotypes of accessions containing disease resistance genes present on introgressed alien segments enabled the identification of specific alleles characteristic of these regions. Associations were derived for a range of stem-rust, leaf-rust and yellow-rust resistance genes. These results suggest that RFLP analysis can be used for the characterisation and grouping of elite breeding material of wheat and RFLP profiling can identify chromosome segments associated with agronomic traits.
RESUMEN
The nuclear Ras-related protein Ran binds guanine nucleotide and is involved in cell cycle regulation. Models of the signal pathway predict Ran to be active as Ran.GTP at the initiation of S phase upon activation by the nucleotide exchange factor RCC1 and to be inactivated for the onset of mitosis by hydrolysis of bound GTP. Here a nuclear homodimeric 65-kDa protein, RanGAP1, is described, which we believe to be the immediate antagonist of RCC1. It was purified from HeLa cell lysates and induces GTPase activity of Ran, but not Ras, by more than 3 orders of magnitude. The Ran mutant Q69L, modeled after RasQ61L, which is unable to hydrolyze bound GTP, is insensitive to RanGAP1.