Enhancing the performance of HLA-based simulation systems via software diversity and active replication

In this paper we explore active replication based on software diversity for improving the responsiveness of simulation systems. Our proposal is framed by the High-Level-Architecture (HLA), namely the emerging standard for interoperability of simulation packages, and results in the design and implementation of an Active Replication Management Layer (ARML), which supports the execution of multiple software diversity-based replicas of a same simulator in a totally transparent manner. Beyond presenting the replication framework and the design/implementation of ARML, we also report the results of an experimental evaluation on a case study, quantifying the benefits from our proposal in terms of execution speed. © 2006 IEEE

Managing Bandwidth and Traffic via Bundling and Filtration in Large-Scale Distributed Simulations

Research has shown that bandwidth can be a limiting factor in the performance of distributed simulations. The Air Force\u27s Distributed Mission Operations Center (DMOC) periodically hosts one of the largest distributed simulation events in the world. The engineers at the DMOC have dealt with the difficult problem of limited bandwidth by implementing application level filters that process all DIS PDUs between the various networks connected to the exercise. This thesis examines their implemented filter and proposes: adaptive range-based filtering and bundling together of PDUs. The goals are to reduce the number of PDUs passed by the adaptive filter and to reduce network overhead and the total amount of data transferred by maximizing packet size up to the MTU. The proposed changes were implemented and logged data from previous events were used on a test network in order to measure the improvement from the base filter to the improved filter. The results showed that the adaptive range based filter was effective, though minimally so, and that the PDU bundling resulted in a reduction of 17% to 20% of the total traffic transmitted across the network

Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System

We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein–protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system

Key Features Relevant to Select Antigens and TCR From the MHC-Mismatched Repertoire to Treat Cancer

Adoptive transfer of T cells transgenic for tumor-reactive T-cell receptors (TCR) is an attractive immunotherapeutic approach. However, clinical translation is so far limited due to challenges in the identification of suitable target antigens as well as TCRs that are concurrent safe and efficient. Definition of key characteristics relevant for effective and specific tumor rejection is essential to improve current TCR-based adoptive T-cell immunotherapies. We here characterized in-depth two TCRs derived from the human leukocyte antigen (HLA)-mismatched allogeneic repertoire targeting two different myeloperoxidase (MPO)-derived peptides presented by the same HLA-restriction element side by side comprising state of the art biochemical and cellular in vitro, in vivo, and in silico experiments. In vitro experiments reveal comparable functional avidities, off-rates, and cytotoxic activities for both TCRs. However, we observed differences especially with respect to cytokine secretion and cross-reactivity as well as in vivo activity. Biochemical and in silico analyses demonstrate different binding qualities of MPO-peptides to the HLA-complex determining TCR qualities. We conclude from our biochemical and in silico analyses of peptide-HLA-binding that rigid and high-affinity binding of peptides is one of the most important factors for isolation of TCRs with high specificity and tumor rejection capacity from the MHC-mismatched repertoire. Based on our results, we developed a workflow for selection of such TCRs with high potency and safety profile suitable for clinical translation

Exact-Differential Large-Scale Traffic Simulation

Analyzing large-scale traffics by simulation needs repeating execution many times with various patterns of scenarios or parameters. Such repeating execution brings about big redundancy because the change from a prior scenario to a later scenario is very minor in most cases, for example, blocking only one of roads or changing the speed limit of several roads. In this paper, we propose a new redundancy reduction technique, called exact-differential simulation, which enables to simulate only changing scenarios in later execution while keeping exactly same results as in the case of whole simulation. The paper consists of two main efforts: (i) a key idea and algorithm of the exact-differential simulation, (ii) a method to build large-scale traffic simulation on the top of the exact-differential simulation. In experiments of Tokyo traffic simulation, the exact-differential simulation shows 7.26 times as much elapsed time improvement in average and 2.26 times improvement even in the worst case as the whole simulation

Mise en place et évaluation d'un algorithme de répartition de charge pour les plateformes de simulations distribuées basées sur les systèmes multi-agents

International audienceCet article traite de la problématique de la répartition de charge dans les systèmes multi-agents à travers un algorithme qui assure la distribution de ces agents. Le besoin est né de l'observation de fréquents problèmes de surcharge lors de simulations basées sur ces systèmes multi-agents. Miro, qui est une plateforme de simulation à grande échelle de la mobilité urbaine en est un exemple concret. La difficulté de ces travaux se situe dans la considération des spécificités des plateformes de simulation orientée agent : autonomie des entités à distribuer et forte imprévisibilité du système. Nous adaptons un algorithme de répartition de charge appelé Comet aux spécificités des simulations distribuées à base d'agents. Cet algorithme est basé sur l'emploi d'un indicateur appelé " crédit " qui pour chaque agent quantifie son affinité pour chaque machine et détermine les meilleurs agents candidats à la migration. Hormis l'algorithme en lui même, ce document en présente une implémentation et une évaluation sur un simulateur développé avec Netlogo. Le but final est d'identifier les paramètres à prendre en considération pour assurer le bon fonctionnement de l'algorithme lors de son implémentation sur une plateforme réelle de simulation

Design, Construction and Cloning of Truncated ORF2 and tPAsp-PADRE-Truncated ORF2 Gene Cassette From Hepatitis E Virus in the pVAX1 Expression Vector

Background: Hepatitis E Virus (HEV) is the causative agent of enterically transmitted acute hepatitis and has high mortality rate of up to 30% among pregnant women. Therefore, development of a novel vaccine is a desirable goal. Objectives: The aim of this study was to construct tPAsp-PADRE-truncated open reading frame 2 (ORF2) and truncated ORF2 DNA plasmid, which can assist future studies with the preparation of an effective vaccine against Hepatitis E Virus. Materials and Methods: A synthetic codon-optimized gene cassette encoding tPAsp-PADRE-truncated ORF2 protein was designed, constructed and analyzed by some bioinformatics software. Furthermore, a codon-optimized truncated ORF2 gene was amplified by the polymerase chain reaction (PCR), with a specific primer from the previous construct. The constructs were sub-cloned in the pVAX1 expression vector and finally expressed in eukaryotic cells. Results: Sequence analysis and bioinformatics studies of the codon-optimized gene cassette revealed that codon adaptation index (CAI), GC content, and frequency of optimal codon usage (Fop) value were improved, and performance of the secretory signal was confirmed. Cloning and sub-cloning of the tPAsp-PADRE-truncated ORF2 gene cassette and truncated ORF2 gene were confirmed by colony PCR, restriction enzymes digestion and DNA sequencing of the recombinant plasmids pVAX-tPAsp-PADRE-truncated ORF2 (aa 112-660) and pVAX-truncated ORF2 (aa 112-660). The expression of truncated ORF2 protein in eukaryotic cells was approved by an Immunofluorescence assay (IFA) and the reverse transcriptase polymerase chain reaction (RT-PCR) method. Conclusions: The results of this study demonstrated that the tPAsp-PADRE-truncated ORF2 gene cassette and the truncated ORF2 gene in recombinant plasmids are successfully expressed in eukaryotic cells. The immunogenicity of the two recombinant plasmids with different formulations will be evaluated as a novel DNA vaccine in future investigations

CIG-DB: the database for human or mouse immunoglobulin and T cell receptor genes available for cancer studies

<p>Abstract</p> <p>Background</p> <p>Immunoglobulin (IG or antibody) and the T-cell receptor (TR) are pivotal proteins in the immune system of higher organisms. In cancer immunotherapy, the immune responses mediated by tumor-epitope-binding IG or TR play important roles in anticancer effects. Although there are public databases specific for immunological genes, their contents have not been associated with clinical studies. Therefore, we developed an integrated database of IG/TR data reported in cancer studies (the Cancer-related Immunological Gene Database [CIG-DB]).</p> <p>Description</p> <p>This database is designed as a platform to explore public human and murine IG/TR genes sequenced in cancer studies. A total of 38,308 annotation entries for IG/TR proteins were collected from GenBank/DDBJ/EMBL and the Protein Data Bank, and 2,740 non-redundant corresponding MEDLINE references were appended. Next, we filtered the MEDLINE texts by MeSH terms, titles, and abstracts containing keywords related to cancer. After we performed a manual check, we classified the protein entries into two groups: 611 on cancer therapy (Group I) and 1,470 on hematological tumors (Group II). Thus, a total of 2,081 cancer-related IG and TR entries were tabularized. To effectively classify future entries, we developed a computational method based on text mining and canonical discriminant analysis by parsing MeSH/title/abstract words. We performed a leave-one-out cross validation for the method, which showed high accuracy rates: 94.6% for IG references and 94.7% for TR references. We also collected 920 epitope sequences bound with IG/TR. The CIG-DB is equipped with search engines for amino acid sequences and MEDLINE references, sequence analysis tools, and a 3D viewer. This database is accessible without charge or registration at <url>http://www.scchr-cigdb.jp/</url>, and the search results are freely downloadable.</p> <p>Conclusions</p> <p>The CIG-DB serves as a bridge between immunological gene data and cancer studies, presenting annotation on IG, TR, and their epitopes. This database contains IG and TR data classified into two cancer-related groups and is able to automatically classify accumulating entries into these groups. The entries in Group I are particularly crucial for cancer immunotherapy, providing supportive information for genetic engineering of novel antibody medicines, tumor-specific TR, and peptide vaccines.</p