Biomedical subjects

Alberto de la Fuente

Publications and source records attributed to Alberto de la Fuente.

6 recordsLinked to original sources

PubMed2004

Discovery of meaningful associations in genomic data using partial correlation coefficients.

MOTIVATION: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION: Supplementary information can be found at: URL to be decided.

Algorithms↗

PubMed2003

Integrative modelling of gene expression and cell metabolism.

The goal of modelling biochemical networks is to understand the system's behaviour (dynamics and control) of these networks in terms of the properties of the individual molecules. Most modelling approaches have dealt with either gene expression or cell metabolism. In light of the widespread use of robotic technologies in laboratories and improved computational power, it is now time to incorporate information from all biochemical levels--gene expression, protein interactions and metabolism--into integrated models. Here, we review the literature on modelling gene expression with cell metabolism. At the end we describe some analytic methods to deal with these systems.

Animals↗

PubMed2002

Gene networks: how to put the function in genomics.

An increasingly popular model of regulation is to represent networks of genes as if they directly affect each other. Although such gene networks are phenomenological because they do not explicitly represent the proteins and metabolites that mediate cell interactions, they are a logical way of describing phenomena observed with transcription profiling, such as those that occur with popular microarray technology. The ability to create gene networks from experimental data and use them to reason about their dynamics and design principles will increase our understanding of cellular function. We propose that gene networks are also a good way to describe function unequivocally, and that they could be used for genome functional annotation. Here, we review some of the concepts and methods associated with gene networks, with emphasis on their construction based on experimental data.

Animals↗

PubMed2002

Linking the genes: inferring quantitative gene networks from microarray data.

Modern microarray technology is capable of providing data about the expression of thousands of genes, and even of whole genomes. An important question is how this technology can be used most effectively to unravel the workings of cellular machinery. Here, we propose a method to infer genetic networks on the basis of data from appropriately designed microarray experiments. In addition to identifying the genes that affect a specific other gene directly, this method also estimates the strength of such effects. We will discuss both the experimental setup and the theoretical background.

Animals↗

PubMed2002

Quantifying gene networks with regulatory strengths.

A gene network is the collection of regulatory relationships between all genes in a genome. Gene networks are high-level descriptions of cellular biochemistry and show the phenomenological interactions between gene activities. These interactions are mediated by proteins and metabolites. In the gene network approach, only the transcriptome is considered and all biochemistry underlying gene-gene interactions is only implicitly present. In a previous work, we presented a method for inferring gene networks from experimental data, quantifying gene-gene interactions with regulatory strengths. Here, we show how to express these regulatory strengths in terms of properties of the whole biochemical network.

Gene Expression Profiling↗

PubMed2002

Metabolic control in integrated biochemical systems.

Traditional analyses of the control and regulation of steady-state concentrations and fluxes assume the activities of the enzymes to be constant. In living cells, a hierarchical control structure connects metabolic pathways to signal-transduction and gene-expression. Consequently, enzyme activities are not generally constant. This would seem to compromise analyses of control and regulation at the metabolic level. Here, we investigate the concept of metabolic quasi-steady state kinetics as a means of applying metabolic control analysis to hierarchical biochemical systems. We discuss four methods that enable the experimental determination of metabolic control coefficients, and demonstrate these by computer simulations. The best method requires extra measurement of enzyme activities, two others are simpler but are less accurate and one method is bound only to work under special conditions. Our results may assist in evaluating the relative importance of transcriptomics and metabolomics for functional genomics.

Enzymes↗