Catálogo de publicaciones - libros

Markov modeling of conformational kinetics of cardiac ion channels is a prospective means to correlate the molecular defects of channel proteins to their electrophysiological dysfunction. However, both the identifiability of the microscopic conformations and the estimation of the transition rates are challenging. In this paper, we present a new method in which the distribution space of the time constants of exponential components of mathematical models are searched as an alternative to the consideration of transition rates. Transition rate patterns were defined and quasi random seed sequences for each pattern were generated by using a multiple recursive generator algorithm. Cluster-wide Monte Carlo simulation was performed to investigate various schemes of Markov models. It was found that by increasing the number of closed conformations the time constants were shifted to larger magnitudes. With the inclusion of inactivation conformation the time distribution was altered depending on the topology of the schemes. Further results demonstrated the stability of the morphology of time distributions. Our study provides the statistical evaluation of the time constant space of Markov schemes. The method facilities the identification of the underlying models and the estimation of parameters, hence is proposed for use in investigating the functional consequences of defective genes responsible for ion channel diseases.

To evaluate the whole body insulin sensitivity, , and the rate of appearance, , of ingested glucose into plasma, the oral minimal model of glucose kinetics (OMM) was applied to insulinemia and glycemia data from six volunteer, normotensive and normoglycemic subjects, who underwent a 300 min oral glucose tolerance test (OGTT). Results from a full 22-sampling schedule (OGTT), were compared with two reduced schedules consisting of 12 samples (OGTT) and 11 samples (OGTT), respectively. The three protocols yielded virtually the same values of insulin sensitivity (denoted as , and , respectively) with intraclass correlation coefficients being not lower than 0.74. The profiles reproduced by the OMM after application to the OGTT and the OGTT data were practically indistinguishable, whereas the profile obtained from the OGTT was characterized by a 33% overshoot at the 30 minute, followed by a 22% undershoot at the 60 minute. Our results suggest that the reduced OGTTis suitable to facilitate the assessment of insulin sensitivity and plasma glucose appearance profile in pathophysiological studies by the OMM.

Human liver cell bioreactors are used in extracorporeal liver support therapy. To optimize bioreactor operation with respect to clinical application an understanding of the central metabolism is desired. A two-compartment model consisting of a system of 48 differential equations was fitted to time series data of the concentrations of 18 amino acids, ammonia, urea, glucose, galactose, sorbitol and lactate, measured in the medium outflow of seven liver cell bioreactor runs. Using the presented model, the authors predict an amino acid secretion from proteolytic activities during the first day after inoculation of the bioreactor with primary liver cells. Furthermore, gluconeogenetic activites from amino acids and/or protein were predicted.

Data clustering is a major data mining technique and has been shown to be useful in a wide variety of domains, including medical and biological statistical data analysis. A non trivial application of cluster analysis occurs in the identification of different subpopulations of particles in large-sized heterogeneous flow-cytometric data. Mixture-model based clustering has been several times applied in the past to medical and biological data analysis; to our knowledge, however, non of these applications was involved with flow-cytometric data. We claim, that utilizing the probabilities mixture model offers several advantages compared to other proposed flow-cytometric data clustering approaches. We apply this model in order to gate lymphocytes in peripheral blood, which is a necessary first-step procedure when dealing with various hematological diseases diagnoses, such as lymphocytic leukemias and lymphoma.

The microcirculatory vascular tone and the regional blood flow are regulated by an elaborate network of intracellular and extracellular signaling pathways with multiple feedback control loops. This complicates interpretation of experimental data and limits our ability to design appropriate interventions. Mathematical modeling offers a systematic approach for system and data analysis and for guiding new experimentation. We describe here our efforts to model signal transduction events involved in the regulation of blood flow and integrate mechanisms at the cellular level to describe function at the multicellular/whole-vessel level. The model provides a) a working database of rat mesenteric endothelial and smooth muscle physiology where newly acquired experimental information on cell electrophysiology and signal transduction can be incorporated, and b) a tool that will assist investigations on the regulation of vascular resistance in health and disease. An example of model application to the study of the pathogenesis of salt-sensitive hypertension is illustrated.

There has been special interest lately in using graph theory to study brain networks, as it provides the theoretic and visualization means to study the ”disconnection syndrome” for schizophrenia. In this work we try to visualize the graphs derived from electroencephalografic (EEG) signals using several graph drawing techniques and incorporate them smoothly into an easy-to-use framework. The aim is to reveal and evaluate important properties of brain networks.

The creation of virtual repositories of medical data is a very important challenge to ease collaboration, research and training among medical organisations. However, there are several technical and legal problems, such as efficient large data distribution, privacy protection, post-processing and knowledge management. The TRENCADIS project is aiming at the development of an infrastructure able to tackle with such problems using Grid Technologies. This article presents the Grid Software Architecture developed, which is implemented on top of the OGSA specification and defines the mechanisms to create virtual repositories of DICOM objects. It integrates different repositories providing a single-database virtual view through high-level components. The TRENCADIS architecture proposes the use of ontologies and templates to organise the DICOM data of Structured Reports. The TRENCADIS architecture is being used for the development of a cyberinfraestructure for medical imaging on oncology in the land of Valencia, with the participation of seven hospitals.

This paper presents a data minimization method that aims at reducing overhead for data reuse in grid environments. The data reuse here is designed to promote efficient use of grid resources by avoiding multiple executions of the same computation in a collaborative community. To promote this at the program block level, our method minimizes the data size of attribute values, which are used for identification of computation products stored in a database (DB) server. Because attribute values are specified in queries used for store, search, or retrieval of computation products, their reduction leads to less communication between computing nodes and the DB server, minimizing the runtime overhead of data reuse. We also show some experimental results obtained using a time-consuming medical application. We find that the method successfully reduces the data size of a query from 683 MB to 52 B. This reduction allows our data reuse framework to reduce execution time from approximately 9 minutes to 27 seconds.

Relational databases have been used to represent lexical knowledge since the days of machine-readable dictionaries. However, although software engineering provides a methodological framework for the construction of databases, most developing efforts focus on content, implementation and time-saving issues, and forget about the software engineering aspects of software and database construction. We have defined a methodology for the development of lexical resources that covers this and other aspects, by following a sound software engineering approach to formally represent knowledge. Nonetheless, the conceptual model from which it departs has some major limitations that need to be overcome. Based on a short analysis of common problems in existing lexical resources, we present an upgraded conceptual model as a first step towards the methodological development of a hierarchically organized concept-based terminology database, to improve the access to medical information as part of the SINAMED and ISIS projects.

Knowledge discovery in clinico-genomic data is a task that requires to integrate not only highly heterogeneous kinds of data, but also the requirements and interests of very different user groups. Technologies of grid computing promise to be an effective tool to combine all these requirements into a single architecture. In this paper, we describe scenarios and future research directions related to grid-based knowledge discovery in clinico-genomic data, and introduce the approach taken by the recently launched ACGT project. The whole endeavor is considered in the context of biomedical informatics research and aims towards the realization of an integrated and grid-enabled biomedical infrastructure. The presented integrated clinico-genomics knowledge discovery (ICGKD) scenario and its process realization is based on a multi-strategy data-mining approach that seamlessly integrates three distinct data-mining components: clustering, association rules mining, and feature-selection. Preliminary experimental results are indicative of the rational and reliability of the approach.