1,480 research outputs found

    Multi-agent model of hepatitis C virus infection

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    Objectives: The objective of this study is to design a method for modeling hepatitis C virus (HCV) infection using multi-agent simulation and to verify it in practice. Methods and materials: In this paper, first, the modeling of HCV infection using a multi-agent system is compared with the most commonly used model type, which is based on differential equations. Then, the implementation and results of the model using a multi-agent simulation is presented. To find the values of the parameters used in the model, a method using inverted simulation flow and genetic algorithm is proposed. All of the data regarding HCV infection are taken from the paper describing the model based on the differential equation to which the proposed method is compared. Results: Important advantages of the proposed method are noted and demonstrated; these include flexibility, clarity, re-usability and the possibility to model more complex dependencies. Then, the simulation framework that uses the proposed approach is successfully implemented in C++ and is verified by comparing it to the approach based on differential equations. The verification proves that an objective function that performs the best is the function that minimizes the maximal differences in the data. Finally, an analysis of one of the already known models is performed, and it is proved that it incorrectly models a decay in the hepatocytes number by 40%. Conclusions: The proposed method has many advantages in comparison to the currently used model types and can be used successfully for analyzing HCV infection. With almost no modifications, it can also be used for other types of viral infections

    Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression

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    Cite as: Perrin, Dimitri (2008) Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression. PhD thesis, Dublin City University

    Integrative omics data analysis to discover novel signatures in complex diseases

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    Apart from diseases caused by the defect of a single gene, most diseases are highly complex and are usually caused by a combination of biological and environmental factors. In the biological context, cellular processes are often tightly connected across molecular layers of the central dogma of biology, and the examination of a single layer would not be sufficient to address disease pathology, therefore, conclusions drawn can be limited. Combining biological observations from multiple layers or angles would greatly broaden our perspectives on the disease in concern and may lead to novel discoveries which would not be possible to deduce from a single-omics perspective. In this thesis, we focused on the method development for single-cell transcriptomics to address the prime bias problem introduced by the new dropletbased technologies; integrative omics discovery of genomic signatures specific to different brain regions in normal individuals; as well as the utilization of multiple omics to identify potential biomarkers specific to amyotrophic lateral sclerosis (ALS) disease prognosis and diagnosis. Research has been revolutionized with the advent of single-cell omics technologies in the past few decades and new methods and tools have also been developed to accommodate such scientific accelerations. These innovations however posed new challenges and could potentially introduce bias and unforeseeable circumstances if left unaddressed. Specifically, to resolve the prime-based problem introduced by the current popular droplet-based single-cell sequencing technologies which may lead to bias quantification, in Study I, we presented a novel transcript quantification tool for droplet-based single-cell RNA-Sequencing (scRNA-Seq) technologies and benchmarked our tool with other popular transcript and gene quantification tools. Our tool outperformed currently popular tools in terms of transcript- and gene-level quantifications. In Study II, we investigated the association of splicing variants with the genetic patterns from different regions of the brain in normal individuals to identify quantitative trait loci (QTL) associated with ratios of isoform expression in genes. We carried out genome-wide association studies (GWAS) on isoform ratios from 13 brain regions and identified isoform-ratio QTL (irQTL) specific to each brain region, and their associated traits which could have been missed by expression QTL derived from gene expressions. We further looked into the utilization of proteomics and genomics data for ALS disease in Study III to understand disease pathology from multiple perspectives, and to identify potential protein biomarkers and protein QTL (pQTL) specific to different stages of the disease and tissue sites. In terms of proteomics, for each tissue site, we identified potential protein biomarkers specific to disease prognosis, survival of ALS patients, the functional decline among ALS patients, and longitudinal changes after disease diagnosis. In terms of integrative omics, we performed GWAS of protein expressions with genotyping data and identified tissuesite-specific pQTL signatures for ALS patients. All in all, our studies showed efforts in developing a single-cell transcript quantification tool to address potential bias problems with improved performance; identifying novel irQTL signatures specific to various brain regions using an integrative omics approach; and also discovering potential protein and genetic signatures for different tissues sites and pathological stages in ALS disease using multiple omics. We hope our work could potentially enhance the research process in various omics in terms of methods development and the novel signatures could act as valuable resources for fostering further research ideas and potential experimental validations

    Drug Repurposing

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    This book focuses on various aspects and applications of drug repurposing, the understanding of which is important for treating diseases. Due to the high costs and time associated with the new drug discovery process, the inclination toward drug repurposing is increasing for common as well as rare diseases. A major focus of this book is understanding the role of drug repurposing to develop drugs for infectious diseases, including antivirals, antibacterial and anticancer drugs, as well as immunotherapeutics

    Genotypic analysis of HIV-1 coreceptor usage

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    The acquired immunodeficiency syndrome (AIDS) is one of the biggest medical challenges in the world today. Its causative pathogen, the human immunodeficiency virus (HIV), is responsible for millions of deaths per year. Although about two dozen antiviral drugs are currently available, progression of the disease can only be delayed but patients cannot be cured. In recent years, the new class of coreceptor antagonists has been added to the arsenal of antiretroviral drugs. These drugs block viral cell-entry by binding to one of the receptors the virus requires for infection of a cell. However, some HIV variants can also use another coreceptor so that coreceptor usage has to be tested before administration of the drug. This thesis analyzes the use of statistical learning methods to infer HIV coreceptor usage from viral genotype. Improvements over existing methods are achieved by using sequence information of so far not used genomic regions, next generation sequencing technologies, and by combining different existing prediction systems. In addition, HIV coreceptor usage prediction is analyzed with respect to clinical outcome in patients treated with coreceptor antagonists. The results demonstrate that inferring HIV coreceptor usage from viral genotype can be reliably used in daily routine.Die Immunschwächekrankheit AIDS ist eine der größten Herausforderungen weltweit. Das verursachende Humane Immundefizienz-Virus (HIV) ist verantwortlich für Millionen Tote jährlich. Obwohl es bereits mehr als zwei Dutzend verschiedene AIDS-Medikamente gibt, können diese den Krankheitsverlauf nur verlangsamen, die Patienten jedoch nicht heilen. In den letzten Jahren wurde eine weitere Medikamentenklasse den bestehenden Therapieansätzen hinzugefügt: die Korezeptorantagonisten. Diese Wirkstoffe binden an Rezeptoren, die das Virus zum Eintritt in die Zelle benötigt und blockieren es somit. Allerdings gibt es auch Virusvarianten, die in der Lage sind Zellen mit Hilfe eines anderen Rezeptors zu infizieren. Daher sollte man vor Verschreibung eines Korezeptorantagonisten den Korezeptorgebrauch des Virus testen. Diese Arbeit befasst sich mit der Bestimmung des Korezeptorgebrauchs aus dem viralen Erbgut mit Hilfe von statistischen Lernverfahren. Verbesserungen gegenüber existierenden Methoden werden erreicht in dem bisher nicht verwendete Genomregionen analysiert werden, durch den Gebrauch von neuesten Hochdurchsatz-Sequenziertechniken, sowie durch die Kombination von zwei existierenden Vorhersagesystemen. Schließlich wird die Qualität der Korezeptorvorhersagen bezüglich klinischem Ansprechens bei Patienten untersucht, die mit Korezeptorantagonisten therapiert wurden. Die Ergebnisse zeigen, dass die Vorhersage des Korezeptorgebrauchs aus dem viralen Erbgut eine verläßliche Methode für den klinischen Alltag darstellt

    Proceedings, MSVSCC 2012

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    Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia

    Global Effect of Climate Change on Seasonal Cycles, Vector Population and Rising Challenges of Communicable Diseases: A Review

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    This article explains ongoing changes in global climate and their effect on the resurgence of vector and pathogen populations in various parts of the world. Today, major prevailing changes are the elevation of global temperature and accidental torrent rains, floods, droughts, and loss of productivity and food commodities. Due to the increase in water surface area and the longer presence of flood water, the breeding of insect vectors becomes very high; it is responsible for the emergence and re-emergence of so many communicable diseases. Due to the development of resistance to chemicals in insect pests, and pathogens and lack of control measures, communicable zoonotic diseases are remerging with high infectivity and mortality. This condition is becoming more alarming as the climate is favoring pathogen-host interactions and vector populations. Rapid changes seen in meteorology are promoting an unmanageable array of vector-borne infectious diseases, such as malaria, Japanese encephalitis, filarial, dengue, and leishmaniasis. Similarly, due to unhygienic conditions, poor sanitation, and infected ground and surface water outbreak of enteric infections such as cholera, vibriosis, and rotavirus is seen on the rise. In addition, parasitic infection ascariasis, fasciolosis, schistosomiasis, and dysentery cases are increasing. Today climate change is a major issue and challenge that needs timely quick solutions. Climate change is imposing non-adaptive forced human migration territorial conflicts, decreasing ecosystem productivity, disease outbreaks, and impelling unequal resource utilization. Rapid climate changes, parasites, pathogens, and vector populations are on the rise, which is making great threats to global health and the environment. This article highlighted the necessity to develop new strategies and control measures to cut down rising vector and pathogen populations in endemic areas. For finding quick solutions educational awareness, technology up-gradation, new vaccines, and safety measures have to be adopted to break the cycle of dreadful communicable diseases shortly

    Full Issue: vol. 65, no.1

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    High-Performance Modelling and Simulation for Big Data Applications

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    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

    SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery.

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    Since the publication of the Society for Immunotherapy of Cancer\u27s (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients
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