Search PubMed⌕ Search

Biomedical subjects

Albert Sorribas

Publications and source records attributed to Albert Sorribas.

8 recordsLinked to original sources
PubMed2007

Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations.

Cooperative and saturable systems are common in molecular biology. Nevertheless, common canonical formalisms for kinetic modeling that are theoretically well justified do not have a saturable form. Modeling and fitting data from saturable systems are widely done using Hill-like equations. In practice, there is no theoretical justification for the generalized use of these equations, other than their ability to fit experimental data. Thus it is important to find a canonical formalism that is (a) theoretically well supported, (b) has a saturable functional form, and (c) can be justifiably applicable to any biochemical network. Here we derive such a formalism using Taylor approximations in a special transformation space defined by power-inverses and logarithms of power-inverses. This formalism is generalized for processes with n-variables, leading to a useful mathematical representation for molecular biology: the Saturable and Cooperative Formalism (SC formalism). This formalism provides an appropriate representation that can be used for modeling processes with cooperativity and saturation. We also show that the Hill equation can be seen as a special case within this formalism. Parameter estimation for the SC formalism requires information that is also necessary to build Power-Law models, Metabolic Control Analysis descriptions or (log)linear and Lin-log models. In addition, the saturation fraction of the relevant processes at the operating point needs to be considered. The practical use of the SC formalism for modeling is illustrated with a few examples. Similar models are built using different formalisms and compared to emphasize advantages and limitations of the different approaches.

Algorithms↗
PubMed2006

Use of physiological constraints to identify quantitative design principles for gene expression in yeast adaptation to heat shock.

BACKGROUND: Understanding the relationship between gene expression changes, enzyme activity shifts, and the corresponding physiological adaptive response of organisms to environmental cues is crucial in explaining how cells cope with stress. For example, adaptation of yeast to heat shock involves a characteristic profile of changes to the expression levels of genes coding for enzymes of the glycolytic pathway and some of its branches. The experimental determination of changes in gene expression profiles provides a descriptive picture of the adaptive response to stress. However, it does not explain why a particular profile is selected for any given response. RESULTS: We used mathematical models and analysis of in silico gene expression profiles (GEPs) to understand how changes in gene expression correlate to an efficient response of yeast cells to heat shock. An exhaustive set of GEPs, matched with the corresponding set of enzyme activities, was simulated and analyzed. The effectiveness of each profile in the response to heat shock was evaluated according to relevant physiological and functional criteria. The small subset of GEPs that lead to effective physiological responses after heat shock was identified as the result of the tuning of several evolutionary criteria. The experimentally observed transcriptional changes in response to heat shock belong to this set and can be explained by quantitative design principles at the physiological level that ultimately constrain changes in gene expression. CONCLUSION: Our theoretical approach suggests a method for understanding the combined effect of changes in the expression of multiple genes on the activity of metabolic pathways, and consequently on the adaptation of cellular metabolism to heat shock. This method identifies quantitative design principles that facilitate understating the response of the cell to stress.

Adaptation, Physiological↗
PubMed2004

Predictive reconstruction of the mitochondrial iron-sulfur cluster assembly metabolism. II. Role of glutaredoxin Grx5.

Grx5 is a Saccharomyces cerevisiae glutaredoxin involved in iron-sulfur cluster (FeSC) biogenesis. Previous work suggests that Grx5 is involved in regulating protein cysteine glutathionylation, prompting several questions about the systemic role of Grx5. First, is the regulation of mixed protein-glutathione disulfide bridges in FeSC biosynthetic proteins by Grx5 sufficient to account for the observed phenotypes of the Deltagrx5 mutants? If so, does Grx5 regulate the oxidation state of mixed protein-glutathione disulfide bridges in FeSC biogenesis in general? Alternatively, can the Deltagrx5 mutant phenotypes be explained if Grx5 acts on just one or a few of the FeSC biogenesis proteins?In the first part of this article, we address these questions by building and analyzing a mathematical model of FeSC biosynthesis. We show that, independent of the tested parameter values, the dynamic behavior observed in cells depleted of Grx5 can only be qualitatively reproduced if Grx5 acts by regulating the initial assembly of FeSC in scaffold proteins. This can be achieved by acting on the cysteine desulfurase (Nfs1) activity and/or on scaffold functionality. In the second part of this article, we use structural bioinformatics methods to evaluate the possibility of interaction between Grx5 and proteins involved in FeSC biogenesis. Based on such methods, our results indicate that the proteins with which Grx5 is more likely to interact are consistent with the kinetic modeling results.Thus, our theoretical studies, combined with known Grx5 biochemistry, suggest that Grx5 acts on FeSC biosynthesis by regulating the redox state of important cysteine residues in Nfs1 and/or in the scaffold proteins where FeSC initially assemble.

Carbon-Sulfur Lyases↗
PubMed2004

Predictive reconstruction of the mitochondrial iron-sulfur cluster assembly metabolism: I. The role of the protein pair ferredoxin-ferredoxin reductase (Yah1-Arh1).

Adrenodoxin reductase homologue (Arh1) and yeast adrenodoxin homologue (Yah1) are essential Saccharomyces cerevisiae mitochondrial proteins involved in heme A biosynthesis and in iron-sulfur cluster (FeSC) assembly. Although the role of Arh1 and Yah1 in heme A biosynthesis is fairly well established, their systemic role on FeSC synthesis is not well understood. Also, while it is thought that the reductase Arh1 provides electrons for the ferredoxin Yah1, two hybrid experiments do not show interaction between the two proteins. In the first part of this article, we use structural bioinformatics methods to evaluate the possibility of interaction between Arh1 and Yah1. Using protein model building and docking algorithms, we predict a complex between Arh1 and Yah1 that is similar to that of their bovine homologues (adrenodoxin reductase-adrenodoxin), suggesting that Arh1 can indeed reduce Yah1. The predicted complex allows us to suggest point mutations to either molecule that could hinder Arh1-Yah1 interaction and test the role of Arh1 as the reductase for Yah1. In the second part of this article, we investigate the physiological role of Arh1-Yah1 on FeSC assembly by deriving alternative mathematical models of the process, based on published information. Comparing the dynamical behavior of each model with that observed in reported experiments emphasizes the importance of Arh1-Yah1 providing electrons for in situ FeSC repair. Only when this mode of action of either of the two proteins in FeSC synthesis is considered can previously reported results be reproduced.

Adrenodoxin↗
PubMed2004

[Methodological approach to the use of artificial neural networks for predicting results in medicine].

In clinical practice, there is an increasing interest in obtaining adequate models of prediction. Within the possible available alternatives, the artificial neural networks (ANN) are progressively more used. In this review we first introduce the ANN methodology, describing the most common type of ANN, the Multilayer Perceptron trained with backpropagation algorithm (MLP). Then we compare the MLP with the Logistic Regression (LR). Finally, we show a practical scheme to make an application based on ANN by means of an example with actual data. The main advantage of the RN is its capacity to incorporate nonlinear effects and interactions between the variables of the model without need to include them a priori. As greater disadvantages, they show a difficult interpretation of their parameters and large empiricism in their process of construction and training. ANN are useful for the computation of probabilities of a given outcome based on a set of predicting variables. Furthermore, in some cases, they obtain better results than LR. Both methodologies, ANN and LR, are complementary and they help us to obtain more valid models.

Artificial Intelligence↗
PubMed2003

Estimating conditional distributions using a method based on S-distributions reference percentile curves for body mass index in Spanish children.

BACKGROUND: Reference intervals are a fundamental tool for characterizing the health status in a given population and play a central role in defining diagnostic values in clinical applications. Estimation of the conditional distribution of a variable, as the body mass index (BMI), is necessary for providing reference values when there is a trend as a function of the covariate. SUBJECTS AND METHOD: We studied 1453 boys and young between 5 and 16 years old measured in a study carried out in the schools of Lleida (Spain). BMI conditional distributions with age have been derived using a new parametric method based on the one proposed by Sorribas et al. [Stat. Med. (2000) 19:697-713]. This method is based on S-distributions as a parametric model for the distribution and uses maximum likelihood estimation of the conditional distribution. RESULTS: The methods commonly used for estimating reference curves are based on a smoothing of sample quantiles using different techniques. However, these methods do not provide information on the conditional distribution of the target variable. Our method provides an estimation of such distribution and the corresponding reference curves for the quantiles as a function of a covariate, in our case age. CONCLUSIONS: The suggested methodology provides appropriate reference quantiles for the BMI. Our results allow characterizing the change in distribution within the age range considered. Besides describing a raise in BMI with age, we observe an increase in dispersion around puberty. This must be considered when using BMI as a diagnostic variable.

Adolescent↗
PubMed2002

A new parametric method based on S-distributions for computing receiver operating characteristic curves for continuous diagnostic tests.

Receiver operating characteristic (ROC) curves provides a method for evaluating the performance of a diagnostic test. These curves represent the true positive ratio, that is, the true positives among those affected by the disease, as a function of the false positive ratio, that is, the false positives among the healthy, corresponding to each possible value of the diagnostic variable. When the diagnostic variable is continuous, the corresponding ROC curve is also continuous. However, estimation of such curve through the analysis of sample data yields a step-line, unless some assumption is made on the underlying distribution of the considered variable. Since the actual distribution of the diagnostic test is seldom known, it is difficult to select an appropriate distribution for practical use. Data transformation may offer a solution but also may introduce a distortion on the evaluation of the diagnostic test. In this paper we show that the distribution family known as the S-distribution can be used to solve this problem. The S-distribution is defined as a differential equation in which the dependent variable is the cumulative. This special form provides a highly flexible family of distributions that can be used as models for unknown distributions. It has been shown that classical statistical distributions can be represented accurately as S-distributions and that they occur in a definite subspace of the parameter space corresponding to the whole S-distribution family. Consequently, many other distributional forms that do not correspond to known distributions are provided by the S-distribution. This property can be used to model observed data for unknown distributions and is very useful in constructing parametric ROC curves in those cases. After fitting an S-distribution to the observed samples of diseased and healthy populations, ROC curve computation is straightforward. A ROC curve can be considered as the solution of a differential equation in which the dependent variable is the ratio of true positives and the independent variable is the ratio of false positives. This equation can be easily obtained from the S-distributions fitted to observed data. Using these results, we can compute pointwise confidence bands for the ROC curve and the corresponding area under the curve. We shall compare this approach with the empirical and the binormal methods for estimating a ROC curve to show that the S-distribution based method is a useful parametric procedure.

Area Under Curve↗