7 research outputs found

    A computational approach based on the colored Petri net formalism for studying multiple sclerosis

    Get PDF
    Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the Central Nervous System (CNS) which damages the myelin sheath enveloping nerve cells thus causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS in adults and is characterized by a series of neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs, daclizumab (commercial name Zinbryta), an antibody tailored against the Interleukin-2 receptor of T cells, exhibited promising results, but its efficacy was accompanied by an increased frequency of serious adverse events. Manifested side effects consisted of infections, encephalitis, and liver damages. Therefore daclizumab has been withdrawn from the market worldwide. Another interesting case of RRMS regards its progression in pregnant women where a smaller incidence of relapses until the delivery has been observed

    GPU Accelerated Analysis of Treg-Teff Cross Regulation in Relapsing-Remitting Multiple Sclerosis

    Get PDF
    The computational analysis of complex biological systems can be hindered by two main factors. First, modeling the system so that it can be easily understood and analyzed by non-expert users is not always possible, especially when dealing with systems of Ordinary Differential Equations. Second, when the system is composed of hundreds or thousands of reactions and chemical species, the classic CPU-based simulators could not be appropriate to efficiently derive the behavior of the system. To overcome these limitations, in this paper we propose a novel approach that combines the descriptive power of Stochastic Symmetric Nets–a Petri Net formalism that allows modeler to describe the system in a parametric and compact manner–with LASSIE, a GPU-powered deterministic simulator that offloads onto the GPU the calculations required to execute many simulations by following both fine-grained and coarse-grained parallelization strategies. This pipeline has been applied to carry out a parameter sweep analysis of a relapsing-remitting multiple sclerosis model, aimed at understanding the role of possible malfunctions in the cross-balancing mechanisms that regulate peripheral tolerance of self-reactive T lymphocytes. From our experiments, LASSIE achieves around 97× speed-up with respect to the sequential execution of the same number of simulations

    GPU accelerated analysis of treg-teff cross regulation in relapsing-remitting multiple sclerosis

    Get PDF
    \u3cp\u3eThe computational analysis of complex biological systems can be hindered by two main factors. First, modeling the system so that it can be easily understood and analyzed by non-expert users is not always possible, especially when dealing with systems of Ordinary Differential Equations. Second, when the system is composed of hundreds or thousands of reactions and chemical species, the classic CPU-based simulators could not be appropriate to efficiently derive the behavior of the system. To overcome these limitations, in this paper we propose a novel approach that combines the descriptive power of Stochastic Symmetric Nets–a Petri Net formalism that allows modeler to describe the system in a parametric and compact manner–with LASSIE, a GPU-powered deterministic simulator that offloads onto the GPU the calculations required to execute many simulations by following both fine-grained and coarse-grained parallelization strategies. This pipeline has been applied to carry out a parameter sweep analysis of a relapsing-remitting multiple sclerosis model, aimed at understanding the role of possible malfunctions in the cross-balancing mechanisms that regulate peripheral tolerance of self-reactive T lymphocytes. From our experiments, LASSIE achieves around 97× speed-up with respect to the sequential execution of the same number of simulations.\u3c/p\u3

    High-Performance Modelling and Simulation for Big Data Applications

    Get PDF
    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    High-Performance Modelling and Simulation for Big Data Applications

    Get PDF
    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Phytochemical Omics in Medicinal Plants

    Get PDF
    Medicinal plants are used to treat diseases and provide health benefits, and their applications are increasing around the world. A huge array of phytochemicals have been identified from medicinal plants, belonging to carotenoids, flavonoids, lignans, and phenolic acids, and so on, with a wide range of biological activities. In order to explore our knowledge of phytochemicals with the assistance of modern molecular tools and high-throughput technologies, this book collects recent innovative original research and review articles on subtopics of mechanistic insights into bioactivities, treatment of diseases, profiling, extraction and identification, and biotechnology
    corecore