CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated

OREMPdb: a semantic dictionary of computational pathway models

<p>Abstract</p> <p>Background</p> <p>The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.</p> <p>Methods</p> <p>Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.</p> <p>Results</p> <p>OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared. </p

Lovastatin Protects against Experimental Plague in Mice

Background: Plague is an ectoparasite-borne deadly infection caused by Yersinia pestis, a bacterium classified among the group A bioterrorism agents. Thousands of deaths are reported every year in some African countries. Tetracyclines and cotrimoxazole are used in the secondary prophylaxis of plague in the case of potential exposure to Y. pestis, but cotrimoxazole-resistant isolates have been reported. There is a need for additional prophylactic measures. We aimed to study the effectiveness of lovastatin, a cholesterol-lowering drug known to alleviate the symptoms of sepsis, for plague prophylaxis in an experimental model. Methodology: Lovastatin dissolved in Endolipide was intraperitoneally administered to mice (20 mg/kg) every day for 6 days prior to a Y. pestis Orientalis biotype challenge. Non-challenged, lovastatin-treated and challenged, untreated mice were also used as control groups in the study. Body weight, physical behavior and death were recorded both prior to infection and for 10 days post-infection. Samples of the blood, lungs and spleen were collected from dead mice for direct microbiological examination, histopathology and culture. The potential antibiotic effect of lovastatin was tested on blood agar plates. Conclusions/Significance: Lovastatin had no in-vitro antibiotic effect against Y. pestis. The difference in the mortality between control mice (11/15; 73.5%) and lovastatin-treated mice (3/15; 20%) was significant (P,0.004; Mantel-Haensze

High Throughput, Multiplexed Pathogen Detection Authenticates Plague Waves in Medieval Venice, Italy

Background: Historical records suggest that multiple burial sites from the 14th-16(th) centuries in Venice, Italy, were used during the Black Death and subsequent plague epidemics.Methodology/Principal Findings: High throughput, multiplexed real-time PCR detected DNA of seven highly transmissible pathogens in 173 dental pulp specimens collected from 46 graves. Bartonella quintana DNA was identified in five (2.9%) samples, including three from the 16th century and two from the 15th century, and Yersinia pestis DNA was detected in three (1.7%) samples, including two from the 14th century and one from the 16th century. Partial glpD gene sequencing indicated that the detected Y. pestis was the Orientalis biotype.Conclusions: These data document for the first time successive plague epidemics in the medieval European city where quarantine was first instituted in the 14th century

Human plague: An old scourge that needs new answers

Yersinia pestis, the bacterial causative agent of plague, remains an important threat to human health. Plague is a rodent-borne disease that has historically shown an outstanding ability to colonize and persist across different species, habitats, and environments while provoking sporadic cases, outbreaks, and deadly global epidemics among humans. Between September and November 2017, an outbreak of urban pneumonic plague was declared in Madagascar, which refocused the attention of the scientific community on this ancient human scourge. Given recent trends and plague’s resilience to control in the wild, its high fatality rate in humans without early treatment, and its capacity to disrupt social and healthcare systems, human plague should be considered as a neglected threat. A workshop was held in Paris in July 2018 to review current knowledge about plague and to identify the scientific research priorities to eradicate plague as a human threat. It was concluded that an urgent commitment is needed to develop and fund a strong research agenda aiming to fill the current knowledge gaps structured around 4 main axes: (i) an improved understanding of the ecological interactions among the reservoir, vector, pathogen, and environment; (ii) human and societal responses; (iii) improved diagnostic tools and case management; and (iv) vaccine development. These axes should be cross-cutting, translational, and focused on delivering context-specific strategies. Results of this research should feed a global control and prevention strategy within a “One Health” approach

Leptospira species and serovars identified by MALDI-TOF mass spectrometry after database implementation

Background: Leptospirosis, a spirochaetal zoonotic disease of worldwide distribution, endemic in Europe, has been recognized as an important emerging infectious disease, though yet it is mostly a neglected disease which imparts its greatest burden on impoverished populations from developing countries. Leptospirosis is caused by the infection with any of the more than 230 serovars of pathogenic Leptospira sp. In this study we aimed to implement the MALDI-TOF mass spectrometry (MS) database currently available in our laboratory with Leptospira reference pathogenic (L. interrogans, L. borgpetersenii, L. kirschneri, L. noguchii), intermediate (L. fainei) and saprophytic (L. biflexa) strains of our collection in order to evaluate its possible application to the diagnosis of leptospirosis and to the typing of strains. This was done with the goal of understanding whether this methodology could be used as a tool for the identification of Leptospira strains, not only at species level for diagnostic purposes, but also at serovar level for epidemiological purposes, overcoming the limits of serological and molecular conventional methods. Twenty Leptospira reference strains were analysed by MALDI-TOF MS. Statistical analysis of the protein spectra was performed by ClinProTools software. Results: The spectra obtained by the analysis of the reference strains tested were grouped into 6 main classes corresponding to the species analysed, highlighting species-specific protein profiles. Moreover, the statistical analysis of the spectra identified discriminatory peaks to recognize Leptospira strains also at serovar level extending previously published data. Conclusions: In conclusion, we confirmed that MALDI-TOF MS could be a powerful tool for research and diagnostic in the field of leptospirosis with broad applications ranging from the detection and identification of pathogenic leptospires for diagnostic purposes to the typing of pathogenic and non-pathogenic leptospires for epidemiological purposes in order to enrich our knowledge about the epidemiology of the infection in different areas and generate control strategies