Armando Reyes-Palomares

Systems biology metabolic modeling assistant

SUMMARY We present Systems Biology Metabolic Modeling Assistant (SBMM Assistant), a tool built using an ontology-based mediator, and designed to facilitate metabolic modeling through the integration of data from repositories that contain valuable metabolic information. This software can be used for the visualization, design and management of metabolic networks; selection, integration and storage of metabolic information; and as an assistant for kinetic modeling. AVAILABILITY SBMM Assistant for academic use is freely available at http://www.sbmm.uma.es.

Histamine: an undercover agent in multiple rare diseases?

Histamine is a biogenic amine performing pleiotropic effects in humans, involving tasks within the immune and neuroendocrine systems, neurotransmission, gastric secretion, cell life and death, and development. It is the product of the histidine decarboxylase activity, and its effects are mainly mediated through four different G‐protein coupled receptors. Thus, histamine‐related effects are the results of highly interconnected and tissue‐specific signalling networks. Consequently, alterations in histamine‐related factors could be an important part in the cause of multiple rare/orphan diseases. Bearing this hypothesis in mind, more than 25 rare diseases related to histamine physiopathology have been identified using a computationally assisted text mining approach. These newly integrated data will provide insight to elucidate the molecular causes of these rare diseases. The data can also help in devising new intervention strategies for personalized medicine for multiple rare diseases.

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, “the human diseases networks” (HDN) and “the orphan disease networks” (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the “Human Phenotype Ontology”. The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module. Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.

A combined model of hepatic polyamine and sulfur amino acid metabolism to analyze S-adenosyl methionine availability

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

kpath: integration of metabolic pathway linked data

In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships, data formats, synonyms or ambiguity. The Linked Data approach partially solves the heterogeneity problems by introducing a uniform data representation model. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. This article introduces kpath, a database that integrates information related to metabolic pathways. kpath also provides a navigational interface that enables not only the browsing, but also the deep use of the integrated data to build metabolic networks based on existing disperse knowledge. This user interface has been used to showcase relationships that can be inferred from the information available in several public databases. Database URL: The public Linked Data repository can be queried at http://sparql.kpath.khaos.uma.es using the graph URI “www.khaos.uma.es/metabolic-pathways-app”. The GUI providing navigational access to kpath database is available at http://browser.kpath.khaos.uma.es.

What is known on angiogenesis-related rare diseases? A systematic review of literature

Angiogenesis, the formation of new vessels from pre‐existing ones, is essential during ontogenetic development and is related to many important physio‐pathological processes in the adult. In fact, a persistent and deregulated angiogenesis is a required event for many diseases and pathological situations, including cancer progression and metastasis. Some rare diseases are also angiogenesis‐related pathologies. However, there is a lack of an exhaustive review on the topic. The main purpose of this work is to carry out a systematic review of literature to determine what (and how much) scientific information concerning angiogenesis‐related rare diseases can be extracted from available sources. After exhaustive searches in bibliographic databases, preselected data were filtered by selecting only those articles on rare diseases with an Orpha number hosted in the Orphanet web. The selected bibliographic references were further curated manually. With the 187 selected references, a critical reading and analysis was carried out allowing for an identification and classification of angiogenesis‐related rare diseases, the involved genes and the drugs available for their treatment, all on the basis of the information available in Orphanet database.

PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.

BackgroundSeveral types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.ResultsPhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.ConclusionsThis framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

First Steps in Computational Systems Biology: A Practical Session in Metabolic Modeling and Simulation.

A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis‐driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever faster numerical simulations of mathematical models. Mathematical modeling plays an essential role in new systems biology approaches. As a complex, integrated system, metabolism is a suitable topic of study for systems biology approaches. However, up until recently, this topic has not been properly covered in biochemistry courses. This communication reports the development and implementation of a practical lesson plan on metabolic modeling and simulation.

Biocomputational Resources Useful For Drug Discovery Against Compartmentalized Targets

It has been estimated that the cost of bringing a new drug onto the market is 10 years and 0.5-2 billions of dollars, making it a non-profitable project, particularly in the case of low prevalence diseases. The advances in Systems Biology have been absolutely decisive for drug discovery, as iterative rounds of predictions made from in silico models followed by selected experimental validations have resulted in a substantial saving of time and investments. Many diseases have their origins in proteins that are not located in the cytosol but in intracellular compartments (i.e. mitochondria, lysosome, peroxisome and others) or cell membranes. In these cases, biocomputational approaches present limitations to their study. In the present work, we review them and propose new initiatives to advance towards a safer, more efficient and personalized pharmacology. This focus could be especially useful for drug discovery and the reposition of known drugs in rare and emergent diseases associated with compartmentalized proteins.

Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases

Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8–9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype–phenotype relationships. We aimed to improve the identification of statistically consistent genotype–phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype–patient–genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.