A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared. This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

Introduction [to Contact force models for multibody dynamics]

"Series: Solid mechanics and its applications, vol. 226"

Pure elastic contact force models

"Series: Solid mechanics and its applications, vol. 226"

Dissipative contact force models

"Series: Solid mechanics and its applications, vol. 226"

Contact force models for multibody dynamics

"Series: Solid mechanics and its applications, vol. 226"

An evolutionary approach to the use of Petri net based models: from parallel controllers to HW/SW co-design

"A workshop within the 19th International Conference on Applications and Theory of Petri Nets - ICATPN’1998"

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Modelos Mixtos-Aditivos Generalizados para el Estudio de Fenómenos Biológicos y Sociales Descriptos por Datos Agregados y/o Longitudinales. Generalized Mixed Additive Models for the Study of Biological and Social Phenomenons from Longitudinal and/or Aggregated Data.

Este proyecto propone extender y generalizar los procesos de estimación e inferencia de modelos aditivos generalizados multivariados para variables aleatorias no gaussianas, que describen comportamientos de fenómenos biológicos y sociales y cuyas representaciones originan series longitudinales y datos agregados (clusters). Se genera teniendo como objeto para las aplicaciones inmediatas, el desarrollo de metodología de modelación para la comprensión de procesos biológicos, ambientales y sociales de las áreas de Salud y las Ciencias Sociales, la condicionan la presencia de fenómenos específicos, como el de las enfermedades.Es así que el plan que se propone intenta estrechar la relación entre la Matemática Aplicada, desde un enfoque bajo incertidumbre y las Ciencias Biológicas y Sociales, en general, generando nuevas herramientas para poder analizar y explicar muchos problemas sobre los cuales tienen cada vez mas información experimental y/o observacional.Se propone, en forma secuencial, comenzando por variables aleatorias discretas (Yi, con función de varianza menor que una potencia par del valor esperado E(Y)) generar una clase unificada de modelos aditivos (paramétricos y no paramétricos) generalizados, la cual contenga como casos particulares a los modelos lineales generalizados, no lineales generalizados, los aditivos generalizados, los de media marginales generalizados (enfoques GEE1 -Liang y Zeger, 1986- y GEE2 -Zhao y Prentice, 1990; Zeger y Qaqish, 1992; Yan y Fine, 2004), iniciando una conexión con los modelos lineales mixtos generalizados para variables latentes (GLLAMM, Skrondal y Rabe-Hesketh, 2004), partiendo de estructuras de datos correlacionados. Esto permitirá definir distribuciones condicionales de las respuestas, dadas las covariables y las variables latentes y estimar ecuaciones estructurales para las VL, incluyendo regresiones de VL sobre las covariables y regresiones de VL sobre otras VL y modelos específicos para considerar jerarquías de variación ya reconocidas. Cómo definir modelos que consideren estructuras espaciales o temporales, de manera tal que permitan la presencia de factores jerárquicos, fijos o aleatorios, medidos con error como es el caso de las situaciones que se presentan en las Ciencias Sociales y en Epidemiología, es un desafío a nivel estadístico. Se proyecta esa forma secuencial para la construcción de metodología tanto de estimación como de inferencia, comenzando con variables aleatorias Poisson y Bernoulli, incluyendo los existentes MLG, hasta los actuales modelos generalizados jerárquicos, conextando con los GLLAMM, partiendo de estructuras de datos correlacionados. Esta familia de modelos se generará para estructuras de variables/vectores, covariables y componentes aleatorios jerárquicos que describan fenómenos de las Ciencias Sociales y la Epidemiología.

Investigación de defectos genéticos en el sistema de la fosforilasa hepática en pacientes argentinos. Definición nosológica mediante una estrategia de análisis molecular. Investigation of genetic defects in the hepatic phosphorylase system in Argentinean patients. Nosological definition by molecular analysis strategy.

Las Enfermedades de Atesoramiento de Glucógeno (EAGs) también llamadas Glucogenosis comprenden un grupo de entidades causadas por una deficiencia enzimática específica relacionada con la vía de síntesis o degradación de esta macromolécula. La heterogeneidad fenotípica de los pacientes afectados dificulta la identificación de las diferentes variantes de EAG y por ende la correcta definición nosológica. En el Centro de Estudio de las Metabolopatías Congénitas, CEMECO, se fueron definiendo los diferentes tipos de Glucogenosis a través de una estrategia multidisciplinaria que integra distintos niveles de investigación clínica y complementaria, laboratorio metabólico especializado, enzimático, histomorfológico y de análisis molecular. Sin embargo, en algunos enfermos, entre los que se encuentran aquellos con defectos en el sistema de la fosforilasa hepática (EAG-VI y EAG-IX), la exacta definición nosológica aún no está resulta. La EAG-VI se refiere a un defecto en la fosforilasa hepática, enzima codificada por el gen PYGL, mientras que la EAG-IX es causada por un defecto genético en una de las subunidades de la fosforilasa b quinasa hepática codicadas por los genes PHKA2, PHKB y PHKG2, respectivamente. El objetivo del presente trabajo es propender a la definición nosológica de pacientes con defectos en el sistema de la fosforilasa mediante una estrategia de análisis molecular investigando los genes PYGL, PHKA2, PHKB y PHKG2. Los pacientes incluidos en este estudio deberán ser compatibles de padecer una EAG-VI o EAG-IX sobre la base de síntomas clínicos y hallazgos bioquímicos. La metodología incluirá la determinación de la enzima fosforilasa b quinasa en glóbulos rojos y dentro del análisis molecular la extracción de DNA genómico a partir de sangre entera para la amplificación por PCR de los exones más las uniones exon/intron de los genes PHKG2 y PYGL y la extracción de RNA total y obtención de cDNA para posterior amplificación de los cDNA PHKA2 y PHKB. Todos los fragmentos amplificados serán sometidos a análisis de secuencia de nucleótidos. Resultados esperados. Este trabajo, primero en Argentina, permitirá establecer las bases moleculares de los defectos del sistema de la fosforilasa hepática (EAG-VI y EAG-IX). El poder lograr este nivel de investigación traerá aparejado, una oferta integrativa en el vasto capítulo de las glucogenosis hepáticas, con extraordinaria significación en la práctica asistencial para el manejo, pronóstico y correspondiente asesoramiento genético. Hepatic glycogen storage diseases (GSDs) are a group of disorders produced by a deficiency in a specific protein involved in the metabolism of glycogen causing different types of GSDs. Phenotypic heterogeneity of affected patients difficult to identify the different GSD variants and therefore the correct definition of the disease. In the “Centro de Estudio de las Metabolopatías Congénitas”, CEMECO, were defined the different GSD types by a protocol which included complex gradual levels of clinical, biochemical, enzymatic and morphological investigation. However, in some patients, like those one with defects in the hepatic phosphorylase system (GSD-VI and GSD-IX) the exact definition of the disease has not yet been resolved. The GSD-VI is produced by a defect in the PYGL gen that encode the liver phosphorylase, while the GSD-IX is caused by a genetic defect in one of the Phosphorylase b kinase subunits, encoded by the PHKA2, PHKB and PHKG2 genes, respectively. The aim of the present study is to define the phosphorylase system defects in argentinian patients through a molecular strategy that involve the investigation of PYGL, PHKA2, PHKB and PHKG2 genes. Patients included in the present study must be compatible with a GSD-VI or GSD-IX on the bases of clinical symptoms and biochemical findings. The phosphorylase b kinase activity will be assay on in blood red cells. The molecular study will include genomic DNA extraction for the amplification of PHKG2 and PYGL genes and the total RNA extraction for amplification of the PHKA2 and PHKB cDNA by PCR. All PCR-amplified fragments will be subjected to direct nucleotide sequencing. This work, first in Argentina, will make possible to establish the molecular basis of the defects on the hepatic phosphorylase system (GSD-VI and GSD IX). To achieve this level of research will entail advance in the study of the hepatic glycogen storage disease, with extraordinary significance in the treatment, prognosis and the genetic counselling.

Data mining with graphical models

Data Mining, Learning from data, graphical models, possibility theory

Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models

Systemidentification, evolutionary automatic, data-driven model, fuzzy Takagi-Sugeno grammar, genotype interpretability, toxicity-prediction

Models and methods for supporting lot sizing decisions in product recovery systems

Magdeburg, Univ., Fak. für Wirtschaftswiss., Diss., 2011

Approximations of the Fisher information for the construction of efficient experimental designs in nonlinear mixed effects models

Magdeburg, Univ., Fak. für Mathematik, Diss., 2012

Experimental design for mixed models with application to population pharmacokinetic studies

experimental design, mixed model, random coefficient regression model, population pharmacokinetics, approximate design

Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

AbstractBackground:30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes.Objective:This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx) at different stages of cardiac resynchronization therapy (CRT).Methods:Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC) III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves.Results:The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD), ejection fraction < 25% and use of high doses of diuretics (HDD) increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping.Conclusion:We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.