SBML Reaction Finder: Retrieve and extract specific reactions from the BioModels database

Summary: The SBML Reaction Finder (SRF) application leverages the deep semantic annotations in the BioModels database to provide efficient retrieval and extraction of individual reactions from SBML models. We hope that the SRF will be useful to quantitative modelers who seek to accelerate their modeling efforts by reusing previously published representations of specific chemical reactions.

Availability and Implementation: The SRF is open source, coded in Java, and distributed under the Mozilla Pubic License Version 1.1. Windows, Macintosh and Linux distributions are available for download at 
http://sourceforge.net/projects/sbmlrxnfinder.
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Advances in semantic representation for multiscale biosimulation: a case study in merging models

As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim’s MML)

Using multiple reference ontologies: Managing composite annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models

Integration of multi-scale biosimulation models via light-weight semantics

Currently, biosimulation researchers use a variety of computational environments and languages to model biological processes. Ideally, researchers should be able to semi- automatically merge models to more effectively build larger, multi-scale models. How- ever, current modeling methods do not capture the underlying semantics of these models sufficiently to support this type of model construction. In this paper, we both propose a general approach to solve this problem, and we provide a specific example that demon- strates the benefits of our methodology. In particular, we describe three biosimulation models: (1) a cardio-vascular fluid dynamics model, (2) a model of heart rate regulation via baroreceptor control, and (3) a sub-cellular-level model of the arteriolar smooth mus- cle. Within a light-weight ontological framework, we leverage reference ontologies to match concepts across models. The light-weight ontology then helps us combine our three models into a merged model that can answer questions beyond the scope of any single model

Bridging Biological Ontologies and Biosimulation: The Ontology of Physics for Biology

We introduce and define the Ontology of Physics for Biology (OPB), a reference ontology of physical principles that bridges the gap between bioinformatics modeling of biological structures and the biosimulation modeling of biological processes. Whereas modeling anatomical entities is relatively wellstudied, representing the physics-based semantics of biosimulation and biological processes remains an open research challenge. The OPB bridges this semantic gap--linking the semantics of biosimulation mathematics to structural bio-ontologies. Our design of the OPB is driven both by theory and pragmatics: we have applied systems dynamics theory to build an ontology with pragmatic use for annotating biosimulation models

Composite annotations: requirements for mapping multiscale data and models to biomedical ontologies

Abstract—Current methods for annotating biomedical data resources rely on simple mappings between data elements and the contents of a variety of biomedical ontologies and controlled vocabularies. Here we point out that such simple mappings are inadequate for large-scale multiscale, multidomain integrative “virtual human” projects. For such integrative challenges, we describe a “composite annotation” schema that is simple yet sufficiently extensible for mapping the biomedical content of a variety of data sources and biosimulation models to available biomedical ontologies

Location of ser-4 near arg-2 on linkage group IV.

Location of ser-4 near arg-2 on linkage group IV

Pharmacokinetic and behavioral characterization of a longterm antipsychotic delivery system in rodents and rabbits

Rationale: Non-adherence with medication remains the major correctable cause of poor outcome in schizophrenia. However, few treatments have addressed this major determinant of outcome with novel long-term delivery systems. Objectives: The aim of this study was to provide biological proof of concept for a long-term implantable antipsychotic delivery system in rodents and rabbits. Materials and methods: Implantable formulations of haloperidol were created using biodegradable polymers. Implants were characterized for in vitro release and in vivo behavior using prepulse inhibition of startle in rats and mice, as well as pharmacokinetics in rabbits. Results: Behavioral measures demonstrate the effectiveness of haloperidol implants delivering 1 mg/kg in mice and 0.6 mg/kg in rats to block amphetamine (10 mg/kg) in mice or apomorphine (0.5 mg/kg) in rats. Additionally, we demonstrate the pattern of release from single polymer implants for 1 year in rabbits. Conclusions: The current study suggests that implantable formulations are a viable approach to providing long-term delivery of antipsychotic medications in vivo using animal models of behavior and pharmacokinetics. In contrast to depot formulations, implantable formulations could last 6 months or longer. Additionally, implants can be removed throughout the delivery interval, offering a degree of reversibility not available with depot formulations

Defining the proteolytic landscape during enterovirus infection.

Viruses cleave cellular proteins to remodel the host proteome. The study of these cleavages has revealed mechanisms of immune evasion, resource exploitation, and pathogenesis. However, the full extent of virus-induced proteolysis in infected cells is unknown, mainly because until recently the technology for a global view of proteolysis within cells was lacking. Here, we report the first comprehensive catalog of proteins cleaved upon enterovirus infection and identify the sites within proteins where the cleavages occur. We employed multiple strategies to confirm protein cleavages and assigned them to one of the two enteroviral proteases. Detailed characterization of one substrate, LSM14A, a p body protein with a role in antiviral immunity, showed that cleavage of this protein disrupts its antiviral function. This study yields a new depth of information about the host interface with a group of viruses that are both important biological tools and significant agents of disease

Patient-Specific Metrics of Invasiveness Reveal Significant Prognostic Benefit of Resection in a Predictable Subset of Gliomas

Object Malignant gliomas are incurable, primary brain neoplasms noted for their potential to extensively invade brain parenchyma. Current methods of clinical imaging do not elucidate the full extent of brain invasion, making it difficult to predict which, if any, patients are likely to benefit from gross total resection. Our goal was to apply a mathematical modeling approach to estimate the overall tumor invasiveness on a patient-by-patient basis and determine whether gross total resection would improve survival in patients with relatively less invasive gliomas. Methods In 243 patients presenting with contrast-enhancing gliomas, estimates of the relative invasiveness of each patient's tumor, in terms of the ratio of net proliferation rate of the glioma cells to their net dispersal rate, were derived by applying a patient-specific mathematical model to routine pretreatment MR imaging. The effect of varying degrees of extent of resection on overall survival was assessed for cohorts of patients grouped by tumor invasiveness. Results We demonstrate that patients with more diffuse tumors showed no survival benefit (P = 0.532) from gross total resection over subtotal/biopsy, while those with nodular (less diffuse) tumors showed a significant benefit (P = 0.00142) with a striking median survival benefit of over eight months compared to sub-totally resected tumors in the same cohort (an 80% improvement in survival time for GTR only seen for nodular tumors). Conclusions These results suggest that our patient-specific, model-based estimates of tumor invasiveness have clinical utility in surgical decision making. Quantification of relative invasiveness assessed from routinely obtained pre-operative imaging provides a practical predictor of the benefit of gross total resection