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Biomedical subjects

Pedro Mendes

Publications and source records attributed to Pedro Mendes.

At least 19 recordsLinked to original sources
PubMed2007

Comparison of sampling techniques for parallel analysis of transcript and metabolite levels in Saccharomyces cerevisiae.

Mathematical modelling of cellular processes is crucial for the understanding of the cell or organism as a whole. Genome-wide observations, at the levels of the transcriptome, proteome and metabolome, provide a high coverage of the molecular constituents of the system in study. Time-course experiments are important for gaining insight into a system's dynamics and are needed for mathematical modelling. In time-course experiments it is crucial to use efficient and fast sampling techniques. We evaluated several techniques to sample and process yeast cultures for parallel analysis of the transcriptome and metabolome. The evaluation was made by measuring the quality of the RNA obtained with UV-spectroscopy, capillary electrophoresis and microarray hybridization. The protocol developed involves rapid collection by spraying the sample into -40 degrees C tricine-buffered methanol (as previously described for yeast metabolome analysis), followed by the separation of cells from the culture medium in low-temperature rapid centrifugation. Removal of the residual methanol is carried out by freeze-drying the pellet at -35 degrees C. RNA and metabolites can then be extracted from the same freeze-dried sample obtained with this procedure.

Centrifugation↗
PubMed2006

COPASI--a COmplex PAthway SImulator.

MOTIVATION: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. RESULTS: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. AVAILABILITY: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

Algorithms↗
PubMed2005

A hybrid approach for efficient and robust parameter estimation in biochemical pathways.

Developing suitable dynamic models of biochemical pathways is a key issue in Systems Biology. Predictive models for cells or whole organisms could ultimately lead to model-based predictive and/or preventive medicine. Parameter estimation (i.e. model calibration) in these dynamic models is therefore a critical problem. In a recent contribution [Moles, C.G., Mendes, P., Banga, J.R., 2003b. Parameter estimation in biochemical pathways: a comparison of global optimisation methods. Genome Res. 13, 2467-2474], the challenging nature of such inverse problems was highlighted considering a benchmark problem, and concluding that only a certain type of stochastic global optimisation method, Evolution Strategies (ES), was able to solve it successfully, although at a rather large computational cost. In this new contribution, we present a new integrated optimisation methodology with a number of very significant improvements: (i) computation time is reduced by one order of magnitude by means of a hybrid method which increases efficiency while guaranteeing robustness, (ii) measurement noise (errors) and partial observations are handled adequately, (iii) automatic testing of identifiability of the model (both local and practical) is included and (iv) the information content of the experiments is evaluated via the Fisher information matrix, with subsequent application to design of new optimal experiments through dynamic optimisation.

Algorithms↗
PubMed2005

Minimum information requested in the annotation of biochemical models (MIRIAM).

Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.

Biochemistry↗
PubMed2004

Methyl jasmonate and yeast elicitor induce differential transcriptional and metabolic re-programming in cell suspension cultures of the model legume Medicago truncatula.

Exposure of cell suspension cultures of Medicago truncatula Gaerth. to methyl jasmonate (MeJA) resulted in up to 50-fold induction of transcripts encoding the key triterpene biosynthetic enzyme beta-amyrin synthase (betaAS; EC 5.4.99.-). Transcripts reached maximum levels at 24 h post-elicitation with 0.5 mM MeJA. The entry point enzymes into the phenylpropanoid and flavonoid pathways, L: -phenylalanine ammonia-lyase (PAL; EC 4.3.1.5) and chalcone synthase (CHS; EC 2.3.1.74), respectively, were not induced by MeJA. In contrast, exposure of cells to yeast elicitor (YE) resulted in up to 45- and 14-fold induction of PAL and CHS transcripts, respectively, at only 2 h post-elicitation. betaAS transcripts were weakly induced at 12 h after exposure to YE. Over 30 different triterpene saponins were identified in the cultures, many of which were strongly induced by MeJA, but not by YE. In contrast, cinnamic acids, benzoic acids and isoflavone-derived compounds accumulated following exposure of cultures to YE, but few changes in phenylpropanoid levels were observed in response to MeJA. DNA microarray analysis confirmed the strong differential transcriptional re-programming of the cell cultures for multiple genes in the phenylpropanoid and triterpene pathways in response to MeJA and YE, and indicated different responses of individual members of gene families. This work establishes Medicago cell cultures as an excellent model for future genomics approaches to understand the regulation of legume secondary metabolism.

Abscisic Acid↗
PubMed2004

Metabolic profiling of Medicago truncatula cell cultures reveals the effects of biotic and abiotic elicitors on metabolism.

GC-MS-based metabolite profiling was used to analyse the response of Medicago truncatula cell cultures to elicitation with methyl jasmonate (MeJa), yeast elicitor (YE), or ultraviolet light (UV). Marked changes in the levels of primary metabolites, including several amino acids, organic acids, and carbohydrates, were observed following elicitation with MeJa. A similar, but attenuated response was observed following YE elicitation, whereas little response was observed following UV elicitation. MeJa induced the accumulation of the triterpene beta-amyrin, a precursor to the triterpene saponins, and LC-MS analysis confirmed the accumulation of triterpene saponins in MeJa-elicited samples. In addition, YE induced a slight, but significant accumulation of shikimic acid, an early precursor to the phenylpropanoid pathway, which was also demonstrated to be YE-inducible by LC-MS analyses. Correlation analyses of metabolite relationships revealed perturbation of the glycine, serine, and threonine biosynthetic pathway, and suggested the induction of threonine aldolase activity, an enzyme as yet uncharacterized from plants. Members of the branched chain amino acid pathway accumulated in a concerted fashion, with the strongest correlation being that between leucine and isoleucine (r2=0.941). While UV exposure itself had little effect on primary metabolites, the experimental procedure, as revealed by control treatments, induced changes in several metabolites which were similar to those following MeJa elicitation. Sucrose levels were lower in MJ- and YE-elicited samples compared with control samples, suggesting that a portion of the effects observed on the primary metabolic pool are a consequence of fundamental metabolic repartitioning of carbon resources rather than elicitor-specific induction. In addition, beta-alanine levels were elevated in all elicited samples, which, when viewed in the context of other elicitation responses, suggests the altered metabolism of coenzyme A and its esters, which are essential in secondary metabolism.

Acetates↗
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↗
PubMed2004

myo-inositol oxygenase offers a possible entry point into plant ascorbate biosynthesis.

Two biosynthetic pathways for ascorbate (l-ascorbic acid [AsA]; vitamin C) in plants are presently known, the mannose/l-galactose pathway and an l-GalUA pathway. Here, we present molecular and biochemical evidence for a possible biosynthetic route using myo-inositol (MI) as the initial substrate. A MI oxygenase (MIOX) gene was identified in chromosome 4 (miox4) of Arabidopsis ecotype Columbia, and its enzymatic activity was confirmed in bacterially expressed recombinant protein. Miox4 was primarily expressed in flowers and leaves of wild-type Arabidopsis plants, tissues with a high concentration of AsA. Ascorbate levels increased 2- to 3-fold in homozygous Arabidopsis lines overexpressing the miox4 open reading frame, thus suggesting the role of MI in AsA biosynthesis and the potential for using this gene for the agronomic and nutritional enhancement of crops.

Arabidopsis↗
PubMed2004

A proposed framework for the description of plant metabolomics experiments and their results.

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

Database Management Systems↗
PubMed2003

Parameter estimation in biochemical pathways: a comparison of global optimization methods.

Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.

Algorithms↗
PubMed2003

Plant metabolomics: large-scale phytochemistry in the functional genomics era.

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.

Computational Biology↗
PubMed2003

Artificial gene networks for objective comparison of analysis algorithms.

MOTIVATION: Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. RESULTS: We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. AVAILABILITY: The data sets and kinetic models described here are available from http://www.vbi.vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats.

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↗