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Vladimir Brusic - One of the best experts on this subject based on the ideXlab platform.

  • Editorial Computational and Bioinformatics Techniques for Immunology
    2016
    Co-Authors: Vladimir Brusic, Christian Schönbach
    Abstract:

    License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational Immunology and immunological bioinfor-matics are well-established and rapidly evolving research fields. Whereas the former aims to develop mathematical and/or Computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter targets proposing methods to analyze large genomic and proteomic immunological-related datasets and derive (i.e., predict) new knowledge mainly by statistical inference and machine learning algorithms. Since Immunology provides key information about basic mechanisms in a number of related diseases, it represents the most critical target for medical intervention. There-fore an advance in either Computational or bioinformatic

  • Editorial Advances in Computational Immunology
    2016
    Co-Authors: Vladimir Brusic, Marzio Pennisi, Guanglan Zhang
    Abstract:

    License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computational Immunology and immunological bioinfor-matics are firm and quickly growing research fields. Whereas the former aims to develop mathematical and/or computa-tional methods to study the dynamics of cellular and molec-ular entities during the immune response [1–4], the latter focuses on proposing methods to investigate big genomic and proteomic immunological-related datasets and predict new knowledge mainly by statistical inference and machine learning algorithms. The glut of data produced by high-throughput instru-mentation, notably genomics, transcriptomics, epigenetics, and proteomics methods, requires Computational tools for acquisition, storage, and analysis of immunological data

  • advances in Computational Immunology
    Clinical & Developmental Immunology, 2015
    Co-Authors: Francesco Pappalardo, Vladimir Brusic, Marzio Pennisi, Guanglan Zhang
    Abstract:

    Computational Immunology and immunological bioinformatics are firm and quickly growing research fields. Whereas the former aims to develop mathematical and/or Computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter focuses on proposing methods to investigate big genomic and proteomic immunological-related datasets and predict new knowledge mainly by statistical inference and machine learning algorithms. The glut of data produced by high-throughput instrumentation, notably genomics, transcriptomics, epigenetics, and proteomics methods, requires Computational tools for acquisition, storage, and analysis of immunological data. The exploitation of such a huge amount of immunological data usually requires its conversion into Computational problems, their solution using mathematical and Computational approaches, and then the translation of the obtained results into immunologically meaningful interpretations. In this special issue, we take an interest from mathematicians, bioinformaticians, Computational scientists, and engineers together with experimental immunologists to present and discuss latest developments in different subareas of Computational Immunology, ranging from databases applications to Computational vaccine design, modelling, and simulation and their application to basic and clinical Immunology. The review from N. Sepulveda et al. calls attention to serology data in conjunction with mathematical modelling in providing a powerful approach to inform on malaria transmission intensity and putative changes over time. Their conclusions show that an interesting idea with public health potential is to use a panel of multidisease antibodies that can be instrumental to know what the infectious agents are in circulation in a given population and their putative dynamics. This idea has not been tested in practice, but definitely will require the extension of classical mathematical models to fully account the immunological interaction between different diseases. In their paper W. Schreiner and colleagues illustrate that molecular dynamics was used to simulate large molecules of the immune system (major histocompatibility complexes, T-cell receptors, and coreceptors). To characterize the relative orientation and movements of domains local coordinate systems (based on principal component analysis) were generated and directional cosines and Euler angles computed. As a most interesting result, they found that the presence of the coreceptor seems to influence the dynamics within the protein complex, in particular the relative movements of the two α-helices, Gα1 and Gα2. It is assessed that the application of personalized medicine requires integration of different data to determine each patient's unique clinical constitution. The automated analysis of medical data is a growing field where different machine learning techniques are used to minimize the time consuming task of manual analysis. In the paper contributed by C.-M. Svensson et al., the authors investigate the interobserver variability of image data comprising fluorescently stained circulating tumor cells and its effect on the performance of two automated classifiers, a random forest and a support vector machine. They found that uncertainty in annotation between observers limited the performance of the automated classifiers, especially when it was included in the test set on which classifier performance was measured. Therapeutic protein products (TPP) have been widely used to treat a variety of human diseases, including cancer, hemophilia, and autoimmune diseases. However, TPP can induce unwanted immune responses that can impact both drug efficacy and patient safety. The presence of aggregates is of particular concern as they have been implicated in inducing both T-cell independent and T-cell dependent immune responses. L. Yin and collaborators used mathematical modelling to evaluate several mechanisms through which aggregates of TPP could contribute to the development of immunogenicity. Their Computational analyses suggest that aggregates are unlikely to induce T-cell independent antibody responses through BCR cross-linking. In contrast, aggregates could contribute to immunogenicity via the T-cell dependent pathway by inducing the presentation of high affinity epitopes that may not be present in nonaggregated TPP and/or by enhancing danger signal to maturate dendritic cells and activate T-cells. A. K. Irin et al. investigate Computational modelling approaches on epigenetic factors in neurodegenerative and autoimmune diseases and their mechanistic analysis. The authors examine the major milestones in epigenetics research in the context of diseases and various Computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases, they said. In this work, A. K. Irin and coauthors show how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables the integration of epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. In the paper by G. Bocharov et al., the authors show how the modelling approaches can be implemented to address diverse aspects of immune system functioning under normal conditions and in response to LCMV and, importantly, make quantitative predictions of the outcomes of immune system perturbations. This may highlight that data-driven applications of meaningful mathematical models in infection biology remain a challenge. MHC α-helices form the antigen-binding cleft and are of particular interest for immunological reactions. To monitor these helices in molecular dynamics simulations, the paper contributed by R. Ribarics et al. applied a parsimonious fragment-fitting method to trace the axes of the α-helices. Each resulting axis was fitted by polynomials in a least-squares sense and the curvature integral was computed. To find the appropriate polynomial degree, the method was tested on two artificially modelled helices, one performing a bending and another a hinge movement. They found that second-order polynomials retrieve predefined parameters of helical motion with minimal relative error. There are at present few tools available to assist with the determination and analysis of B-cell lineage trees from next-generation sequencing data. The paper from W. D Lees and A. J. Shepherd presents two utilities that support automated large-scale analysis and the creation of publication-quality results. The tools are available on the web and are also available for download so that they can be integrated into an automated pipeline. These utilities can be used with any suitable phylogenetic inference method and with any antibody germline library and hence are species-independent. Vaccination is historically one of the most important medical interventions for the prevention of infectious disease. Previously, vaccines were typically made of rather crude mixtures of inactivated or attenuated causative agents. However, over the last 10–20 years, several important technological and Computational advances have enabled major progress in the discovery and design of potently immunogenic recombinant protein vaccine antigens. L. Liljeroos and colleagues discuss three key breakthrough approaches that have potentiated structural and Computational vaccine design. They illustrate the growing power of combining sequencing, structural, and Computational approaches and discuss how this may drive the design of novel immunogens suitable for future vaccines urgently needed to increase the global prevention of infectious disease. MIrExpress is a new database which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. In the work from J. Wang et al., the authors describe this database that totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. Publically available influenza data are a valuable resource for Computational analyses with applications in vaccine design. Similarly, existing bioinformatics tools provide the means for extraction of information and new knowledge. However, to utilize the full potential of these resources, data preprocessing must be performed and analytical tools must be carefully combined into well-defined workflows. C. Simon et al. describe FluKB, a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. Francesco Pappalardo Vladimir Brusic Marzio Pennisi Guanglan Zhang

  • Computational and bioinformatics techniques for Immunology
    BioMed Research International, 2014
    Co-Authors: Francesco Pappalardo, Vladimir Brusic, Filippo Castiglione, Christian Schönbach
    Abstract:

    Computational Immunology and immunological bioinformatics are well-established and rapidly evolving research fields. Whereas the former aims to develop mathematical and/or Computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter targets proposing methods to analyze large genomic and proteomic immunological-related datasets and derive (i.e., predict) new knowledge mainly by statistical inference and machine learning algorithms. Since Immunology provides key information about basic mechanisms in a number of related diseases, it represents the most critical target for medical intervention. Therefore an advance in either Computational or bioinformatics Immunology research field has the potential to pave the way for improvement of human health through better patient-specific diagnostics and optimized immune treatment. In this special issue, we take an interest from mathematicians, bioinformaticians, Computational scientists, and engineers together with experimental immunologists, to present and discuss latest developments in different subareas ranging from modeling and simulation to machine learning predictions and their application to basic and clinical Immunology. Of the possible directions for development in immune-informatics special interest is raising for models focusing on innate-adaptive immune response activation, immune senescence, and multiscale and multiorgan models of immune-related diseases and for models accounting for cell trafficking in lymph nodes and/or in the lymphatic mesh as in “Modeling biology spanning different scales: an open challenge” by F. Castiglione et al. Exploring the connections between classical mathematical modeling (at different scales) and bioinformatics predictions of omic scope along with specific aspects of the immune system in combination with concepts and methods like computer simulations, mathematics and statistics for the discovery, design, and optimization of drugs, vaccines, and other immunotherapies represents a hot topic in Computational biology and systems medicine [5, 6]. The review from F. Castiglione et al. calls attention to the importance of the different time-space scale involved in biological phenomena and in particular in the immune system. It dissects the problem and discusses various techniques that have been developed in scientific areas other than Computational biology. In their paper S. Jarrah et al. illustrate a simple ODE model to investigate the role of the immune response in muscle degeneration and regeneration in the mdx mouse model of Duchenne muscular dystrophy. Their model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes and predicts in a certain parameter range a permanent immune activation damaging muscle fibers. In the paper contributed by T. Clancy and E. Hovig, the authors propose a new method to integrate expression profiles and protein-protein interaction (PPI) data. Bioinformatics techniques are used to study differential protein interaction mechanisms across the entire immune cell lineages and the transcriptional activators and modules and are analyzed in the context of exemplars obtained by clustering the PPI network. The results illustrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. The development of mathematical models of the immune response allows a better understanding of the multifaceted mechanisms of the defense system. In this scenario, as already introduced in the review from F. Castiglione et al., multiscale approaches play a fundamental role. B. de M. Quintela et al. propose a scheme for coupling distinct models of different scales and aspects of the immune system describing a new model that deals with the inflammation processes. These processes are simulated coupling and ordinary differential equations that are used as a model for the systemic response. The dynamics of various immune cells is shown in the presence of an antigen. There is a controversy about the relationship between HLA-A2 and Alzheimer's disease. HLA supposedly plays a modifier effect on the risk that depends on genetic loadings. Garcia and Murillo present an in silico method to evaluate this relationship and to reveal genes associated with both the HLA-A2 and Alzheimer's disease. They used experimental knowledge of protein-protein interactions to evaluate the top ranked genes shared by both concepts, previously found through text mining. With the vast amount of immunological data available, Immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. In the paper contributed by G. L. Zhang et al., it is clearly stated that the present challenge is to make the transition from data to actionable knowledge and wisdom and bridge the gap between knowledge and application. In their work, the authors present a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. This technique speeds up rational vaccine design by providing accurate and well-annotated data coupled with tailored Computational analysis tools and workflows. Hepatitis C virus and HIV are rapidly mutating viruses. They have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immune-virology of viral infections, as these allow fast and cheap generation of genomic data. P. Leung et al. propose a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Using next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, the authors prove the applicability of these tools in the context of primary HCV infection. The proposed pipeline is a freely accessible collection of tools (see the paper for details). M. Kenn et al. point the attention on the dynamic variations in the distances between pairs of atoms that are used for clustering subdomains of biomolecules. They draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed, proving that this method reduces the Computational load of clustering drastically, demonstrating results for several biomolecules relevant in immunoinformatics. In the paper by R. Ribarics et al., molecular dynamics is presented as a valuable tool for the investigation of functional elements in biomolecules. They used several spline models to approximate the overall shape of MHC α-helices. The authors applied this technique to a series of MD simulations of alloreactive MHC molecules that allowed them to capture the dynamics of MHC α-helices' steric configurations. In the paper, they discuss the variability of spline models underlying the geometric analysis with varying polynomial degrees of the splines. HIV represents a widespread viral infection without cure. Drug treatment has transformed HIV disease into a treatable long-term infection. However, the appearance of mutations within the viral genome reduces the susceptibility of HIV to drugs. In the paper contributed by M. Haering et al., the authors discuss predictions derived from a mathematical model of HIV dynamics. Their results indicate that early therapy initiation (within 2 years after infection) is critical to delay AIDS progression. Francesco Pappalardo Vladimir Brusic Filippo Castiglione Christian Schonbach

  • the future for Computational modelling and prediction systems in clinical Immunology
    Novartis Foundation symposium, 2003
    Co-Authors: Nikolai Petrovsky, Diego G Silva, Vladimir Brusic
    Abstract:

    Advances in Computational science, despite their enormous potential, have been surprisingly slow to impact on clinical practice. This paper examines the potential of bioinformatics to advance clinical Immunology across a number of key examples including the use of Computational Immunology to improve renal transplantation outcomes, identify novel genes involved in immunological disorders, decipher the relationship between antigen presentation pathways and human disease, and predict allergenicity. These examples demonstrate the enormous potential for immunoinformatics to advance clinical and experimental Immunology. The acceptance of immunoinformatic techniques by clinical and research immunologists will need robust standards of data quality, system integrity and properly validated immunoinformatic systems. Such validation, at a minimum, will require appropriately designed clinical studies conducted according to Good Clinical Practice standards. This strategy will enable immunoinformatics to achieve its full potential to advance and shape clinical Immunology in the future.

Vida Abedi - One of the best experts on this subject based on the ideXlab platform.

  • Data_Sheet_1_Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling.PDF
    2019
    Co-Authors: Xi Chen, Raquel Hontecillas, Josep Bassaganya-riera, Wenjing Wang, Guangrui Xie, Meghna Verma, Andrew Leber, Vida Abedi
    Abstract:

    Computational Immunology studies the interactions between the components of the immune system that includes the interplay between regulatory and inflammatory elements. It provides a solid framework that aids the conversion of pre-clinical and clinical data into mathematical equations to enable modeling and in silico experimentation. The modeling-driven insights shed lights on some of the most pressing immunological questions and aid the design of fruitful validation experiments. A typical system of equations, mapping the interaction among various immunological entities and a pathogen, consists of a high-dimensional input parameter space that could drive the stochastic system outputs in unpredictable directions. In this paper, we perform spatio-temporal metamodel-based sensitivity analysis of immune response to Helicobacter pylori infection using the Computational model developed by the ENteric Immune SImulator (ENISI). We propose a two-stage metamodel-based procedure to obtain the estimates of the Sobol' total and first-order indices for each input parameter, for quantifying their time-varying impacts on each output of interest. In particular, we fully reuse and exploit information from an existing simulated dataset, develop a novel sampling design for constructing the two-stage metamodels, and perform metamodel-based sensitivity analysis. The proposed procedure is scalable, easily interpretable, and adaptable to any multi-input multi-output complex systems of equations with a high-dimensional input parameter space.

  • Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling
    'Frontiers Media SA', 2019
    Co-Authors: Xi Chen, Raquel Hontecillas, Josep Bassaganya-riera, Wenjing Wang, Guangrui Xie, Meghna Verma, Andrew Leber, Vida Abedi
    Abstract:

    Computational Immunology studies the interactions between the components of the immune system that includes the interplay between regulatory and inflammatory elements. It provides a solid framework that aids the conversion of pre-clinical and clinical data into mathematical equations to enable modeling and in silico experimentation. The modeling-driven insights shed lights on some of the most pressing immunological questions and aid the design of fruitful validation experiments. A typical system of equations, mapping the interaction among various immunological entities and a pathogen, consists of a high-dimensional input parameter space that could drive the stochastic system outputs in unpredictable directions. In this paper, we perform spatio-temporal metamodel-based sensitivity analysis of immune response to Helicobacter pylori infection using the Computational model developed by the ENteric Immune SImulator (ENISI). We propose a two-stage metamodel-based procedure to obtain the estimates of the Sobol' total and first-order indices for each input parameter, for quantifying their time-varying impacts on each output of interest. In particular, we fully reuse and exploit information from an existing simulated dataset, develop a novel sampling design for constructing the two-stage metamodels, and perform metamodel-based sensitivity analysis. The proposed procedure is scalable, easily interpretable, and adaptable to any multi-input multi-output complex systems of equations with a high-dimensional input parameter space

Francesco Pappalardo - One of the best experts on this subject based on the ideXlab platform.

  • advances in Computational Immunology
    Clinical & Developmental Immunology, 2015
    Co-Authors: Francesco Pappalardo, Vladimir Brusic, Marzio Pennisi, Guanglan Zhang
    Abstract:

    Computational Immunology and immunological bioinformatics are firm and quickly growing research fields. Whereas the former aims to develop mathematical and/or Computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter focuses on proposing methods to investigate big genomic and proteomic immunological-related datasets and predict new knowledge mainly by statistical inference and machine learning algorithms. The glut of data produced by high-throughput instrumentation, notably genomics, transcriptomics, epigenetics, and proteomics methods, requires Computational tools for acquisition, storage, and analysis of immunological data. The exploitation of such a huge amount of immunological data usually requires its conversion into Computational problems, their solution using mathematical and Computational approaches, and then the translation of the obtained results into immunologically meaningful interpretations. In this special issue, we take an interest from mathematicians, bioinformaticians, Computational scientists, and engineers together with experimental immunologists to present and discuss latest developments in different subareas of Computational Immunology, ranging from databases applications to Computational vaccine design, modelling, and simulation and their application to basic and clinical Immunology. The review from N. Sepulveda et al. calls attention to serology data in conjunction with mathematical modelling in providing a powerful approach to inform on malaria transmission intensity and putative changes over time. Their conclusions show that an interesting idea with public health potential is to use a panel of multidisease antibodies that can be instrumental to know what the infectious agents are in circulation in a given population and their putative dynamics. This idea has not been tested in practice, but definitely will require the extension of classical mathematical models to fully account the immunological interaction between different diseases. In their paper W. Schreiner and colleagues illustrate that molecular dynamics was used to simulate large molecules of the immune system (major histocompatibility complexes, T-cell receptors, and coreceptors). To characterize the relative orientation and movements of domains local coordinate systems (based on principal component analysis) were generated and directional cosines and Euler angles computed. As a most interesting result, they found that the presence of the coreceptor seems to influence the dynamics within the protein complex, in particular the relative movements of the two α-helices, Gα1 and Gα2. It is assessed that the application of personalized medicine requires integration of different data to determine each patient's unique clinical constitution. The automated analysis of medical data is a growing field where different machine learning techniques are used to minimize the time consuming task of manual analysis. In the paper contributed by C.-M. Svensson et al., the authors investigate the interobserver variability of image data comprising fluorescently stained circulating tumor cells and its effect on the performance of two automated classifiers, a random forest and a support vector machine. They found that uncertainty in annotation between observers limited the performance of the automated classifiers, especially when it was included in the test set on which classifier performance was measured. Therapeutic protein products (TPP) have been widely used to treat a variety of human diseases, including cancer, hemophilia, and autoimmune diseases. However, TPP can induce unwanted immune responses that can impact both drug efficacy and patient safety. The presence of aggregates is of particular concern as they have been implicated in inducing both T-cell independent and T-cell dependent immune responses. L. Yin and collaborators used mathematical modelling to evaluate several mechanisms through which aggregates of TPP could contribute to the development of immunogenicity. Their Computational analyses suggest that aggregates are unlikely to induce T-cell independent antibody responses through BCR cross-linking. In contrast, aggregates could contribute to immunogenicity via the T-cell dependent pathway by inducing the presentation of high affinity epitopes that may not be present in nonaggregated TPP and/or by enhancing danger signal to maturate dendritic cells and activate T-cells. A. K. Irin et al. investigate Computational modelling approaches on epigenetic factors in neurodegenerative and autoimmune diseases and their mechanistic analysis. The authors examine the major milestones in epigenetics research in the context of diseases and various Computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases, they said. In this work, A. K. Irin and coauthors show how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables the integration of epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. In the paper by G. Bocharov et al., the authors show how the modelling approaches can be implemented to address diverse aspects of immune system functioning under normal conditions and in response to LCMV and, importantly, make quantitative predictions of the outcomes of immune system perturbations. This may highlight that data-driven applications of meaningful mathematical models in infection biology remain a challenge. MHC α-helices form the antigen-binding cleft and are of particular interest for immunological reactions. To monitor these helices in molecular dynamics simulations, the paper contributed by R. Ribarics et al. applied a parsimonious fragment-fitting method to trace the axes of the α-helices. Each resulting axis was fitted by polynomials in a least-squares sense and the curvature integral was computed. To find the appropriate polynomial degree, the method was tested on two artificially modelled helices, one performing a bending and another a hinge movement. They found that second-order polynomials retrieve predefined parameters of helical motion with minimal relative error. There are at present few tools available to assist with the determination and analysis of B-cell lineage trees from next-generation sequencing data. The paper from W. D Lees and A. J. Shepherd presents two utilities that support automated large-scale analysis and the creation of publication-quality results. The tools are available on the web and are also available for download so that they can be integrated into an automated pipeline. These utilities can be used with any suitable phylogenetic inference method and with any antibody germline library and hence are species-independent. Vaccination is historically one of the most important medical interventions for the prevention of infectious disease. Previously, vaccines were typically made of rather crude mixtures of inactivated or attenuated causative agents. However, over the last 10–20 years, several important technological and Computational advances have enabled major progress in the discovery and design of potently immunogenic recombinant protein vaccine antigens. L. Liljeroos and colleagues discuss three key breakthrough approaches that have potentiated structural and Computational vaccine design. They illustrate the growing power of combining sequencing, structural, and Computational approaches and discuss how this may drive the design of novel immunogens suitable for future vaccines urgently needed to increase the global prevention of infectious disease. MIrExpress is a new database which takes advantage of the information theory, as well as the Pearson linear correlation method, to measure the linear correlation, nonlinear correlation, and their hybrid of cell-specific gene coexpressions in immune cells. In the work from J. Wang et al., the authors describe this database that totally includes 16 human cell groups, involving 20,283 human genes. The expression data and the calculated correlation results from the database are interactively accessible on the web page and can be implemented for other related applications and researches. Publically available influenza data are a valuable resource for Computational analyses with applications in vaccine design. Similarly, existing bioinformatics tools provide the means for extraction of information and new knowledge. However, to utilize the full potential of these resources, data preprocessing must be performed and analytical tools must be carefully combined into well-defined workflows. C. Simon et al. describe FluKB, a knowledge-based system focusing on data and analytical tools for influenza vaccine discovery. The main goal of FluKB is to provide access to curated influenza sequence and epitope data and enhance the analysis of influenza sequence diversity and the analysis of targets of immune responses. FluKB consists of more than 400,000 influenza protein sequences, known epitope data (357 verified T-cell epitopes, 685 HLA binders, and 16 naturally processed MHC ligands), and a collection of 28 influenza antibodies and their structurally defined B-cell epitopes. Francesco Pappalardo Vladimir Brusic Marzio Pennisi Guanglan Zhang

  • Computational and bioinformatics techniques for Immunology
    BioMed Research International, 2014
    Co-Authors: Francesco Pappalardo, Vladimir Brusic, Filippo Castiglione, Christian Schönbach
    Abstract:

    Computational Immunology and immunological bioinformatics are well-established and rapidly evolving research fields. Whereas the former aims to develop mathematical and/or Computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter targets proposing methods to analyze large genomic and proteomic immunological-related datasets and derive (i.e., predict) new knowledge mainly by statistical inference and machine learning algorithms. Since Immunology provides key information about basic mechanisms in a number of related diseases, it represents the most critical target for medical intervention. Therefore an advance in either Computational or bioinformatics Immunology research field has the potential to pave the way for improvement of human health through better patient-specific diagnostics and optimized immune treatment. In this special issue, we take an interest from mathematicians, bioinformaticians, Computational scientists, and engineers together with experimental immunologists, to present and discuss latest developments in different subareas ranging from modeling and simulation to machine learning predictions and their application to basic and clinical Immunology. Of the possible directions for development in immune-informatics special interest is raising for models focusing on innate-adaptive immune response activation, immune senescence, and multiscale and multiorgan models of immune-related diseases and for models accounting for cell trafficking in lymph nodes and/or in the lymphatic mesh as in “Modeling biology spanning different scales: an open challenge” by F. Castiglione et al. Exploring the connections between classical mathematical modeling (at different scales) and bioinformatics predictions of omic scope along with specific aspects of the immune system in combination with concepts and methods like computer simulations, mathematics and statistics for the discovery, design, and optimization of drugs, vaccines, and other immunotherapies represents a hot topic in Computational biology and systems medicine [5, 6]. The review from F. Castiglione et al. calls attention to the importance of the different time-space scale involved in biological phenomena and in particular in the immune system. It dissects the problem and discusses various techniques that have been developed in scientific areas other than Computational biology. In their paper S. Jarrah et al. illustrate a simple ODE model to investigate the role of the immune response in muscle degeneration and regeneration in the mdx mouse model of Duchenne muscular dystrophy. Their model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes and predicts in a certain parameter range a permanent immune activation damaging muscle fibers. In the paper contributed by T. Clancy and E. Hovig, the authors propose a new method to integrate expression profiles and protein-protein interaction (PPI) data. Bioinformatics techniques are used to study differential protein interaction mechanisms across the entire immune cell lineages and the transcriptional activators and modules and are analyzed in the context of exemplars obtained by clustering the PPI network. The results illustrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. The development of mathematical models of the immune response allows a better understanding of the multifaceted mechanisms of the defense system. In this scenario, as already introduced in the review from F. Castiglione et al., multiscale approaches play a fundamental role. B. de M. Quintela et al. propose a scheme for coupling distinct models of different scales and aspects of the immune system describing a new model that deals with the inflammation processes. These processes are simulated coupling and ordinary differential equations that are used as a model for the systemic response. The dynamics of various immune cells is shown in the presence of an antigen. There is a controversy about the relationship between HLA-A2 and Alzheimer's disease. HLA supposedly plays a modifier effect on the risk that depends on genetic loadings. Garcia and Murillo present an in silico method to evaluate this relationship and to reveal genes associated with both the HLA-A2 and Alzheimer's disease. They used experimental knowledge of protein-protein interactions to evaluate the top ranked genes shared by both concepts, previously found through text mining. With the vast amount of immunological data available, Immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. In the paper contributed by G. L. Zhang et al., it is clearly stated that the present challenge is to make the transition from data to actionable knowledge and wisdom and bridge the gap between knowledge and application. In their work, the authors present a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. This technique speeds up rational vaccine design by providing accurate and well-annotated data coupled with tailored Computational analysis tools and workflows. Hepatitis C virus and HIV are rapidly mutating viruses. They have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immune-virology of viral infections, as these allow fast and cheap generation of genomic data. P. Leung et al. propose a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Using next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, the authors prove the applicability of these tools in the context of primary HCV infection. The proposed pipeline is a freely accessible collection of tools (see the paper for details). M. Kenn et al. point the attention on the dynamic variations in the distances between pairs of atoms that are used for clustering subdomains of biomolecules. They draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed, proving that this method reduces the Computational load of clustering drastically, demonstrating results for several biomolecules relevant in immunoinformatics. In the paper by R. Ribarics et al., molecular dynamics is presented as a valuable tool for the investigation of functional elements in biomolecules. They used several spline models to approximate the overall shape of MHC α-helices. The authors applied this technique to a series of MD simulations of alloreactive MHC molecules that allowed them to capture the dynamics of MHC α-helices' steric configurations. In the paper, they discuss the variability of spline models underlying the geometric analysis with varying polynomial degrees of the splines. HIV represents a widespread viral infection without cure. Drug treatment has transformed HIV disease into a treatable long-term infection. However, the appearance of mutations within the viral genome reduces the susceptibility of HIV to drugs. In the paper contributed by M. Haering et al., the authors discuss predictions derived from a mathematical model of HIV dynamics. Their results indicate that early therapy initiation (within 2 years after infection) is critical to delay AIDS progression. Francesco Pappalardo Vladimir Brusic Filippo Castiglione Christian Schonbach

  • grid computing and Computational Immunology
    2011
    Co-Authors: Ferdinando Chiacchio, Francesco Pappalardo
    Abstract:

    Biological function emerges from the interaction of processes acting across a range of spatio-temporal scales. Therefore understanding disease and developing potential therapeutic strategies requires studies that bridge across multiple levels. This requires a systems biology approach and the tools used must be based on effective mathematical algorithms and built by combining experimental and theoretical approaches, addressing concrete problems and clearly defined questions. Technological revolutions in both biotechnology and information technology have produced enormous amounts of data and are accelerating the extension of our knowledge of biological systems. These advances are changing the way biomedical research, development and applications are done. Mathematical and Computational models are increasingly used to help interpret data produced by high-throughput genomics and proteomics projects, and through advances in instrumentation. Advanced applications of computer models that enable the simulation of biological processes are used to generate hypotheses and plan experiments. Computational modeling of immune processes has emerged as a major support area for Immunology and vaccinology research. Computational models have been developed for the simulation of immune processes at the molecular, cellular, and system levels. Computer models are used to complement or replace actual testing or experimentation. They are commonly used in situations where experimentation is expensive, dangerous, or impossible to perform. Models of the immune system fall into two categories:

Victor Greiff - One of the best experts on this subject based on the ideXlab platform.

  • Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires.
    Frontiers in immunology, 2018
    Co-Authors: Enkelejda Miho, Sai T. Reddy, Alexander Yermanos, Cédric R. Weber, Christoph Berger, Victor Greiff
    Abstract:

    The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the Computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in Computational Immunology and propose future directions for systems Immunology toward coupling AIRR-seq with the Computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.

Filippo Castiglione - One of the best experts on this subject based on the ideXlab platform.

  • Computational and bioinformatics techniques for Immunology
    BioMed Research International, 2014
    Co-Authors: Francesco Pappalardo, Vladimir Brusic, Filippo Castiglione, Christian Schönbach
    Abstract:

    Computational Immunology and immunological bioinformatics are well-established and rapidly evolving research fields. Whereas the former aims to develop mathematical and/or Computational methods to study the dynamics of cellular and molecular entities during the immune response [1–4], the latter targets proposing methods to analyze large genomic and proteomic immunological-related datasets and derive (i.e., predict) new knowledge mainly by statistical inference and machine learning algorithms. Since Immunology provides key information about basic mechanisms in a number of related diseases, it represents the most critical target for medical intervention. Therefore an advance in either Computational or bioinformatics Immunology research field has the potential to pave the way for improvement of human health through better patient-specific diagnostics and optimized immune treatment. In this special issue, we take an interest from mathematicians, bioinformaticians, Computational scientists, and engineers together with experimental immunologists, to present and discuss latest developments in different subareas ranging from modeling and simulation to machine learning predictions and their application to basic and clinical Immunology. Of the possible directions for development in immune-informatics special interest is raising for models focusing on innate-adaptive immune response activation, immune senescence, and multiscale and multiorgan models of immune-related diseases and for models accounting for cell trafficking in lymph nodes and/or in the lymphatic mesh as in “Modeling biology spanning different scales: an open challenge” by F. Castiglione et al. Exploring the connections between classical mathematical modeling (at different scales) and bioinformatics predictions of omic scope along with specific aspects of the immune system in combination with concepts and methods like computer simulations, mathematics and statistics for the discovery, design, and optimization of drugs, vaccines, and other immunotherapies represents a hot topic in Computational biology and systems medicine [5, 6]. The review from F. Castiglione et al. calls attention to the importance of the different time-space scale involved in biological phenomena and in particular in the immune system. It dissects the problem and discusses various techniques that have been developed in scientific areas other than Computational biology. In their paper S. Jarrah et al. illustrate a simple ODE model to investigate the role of the immune response in muscle degeneration and regeneration in the mdx mouse model of Duchenne muscular dystrophy. Their model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes and predicts in a certain parameter range a permanent immune activation damaging muscle fibers. In the paper contributed by T. Clancy and E. Hovig, the authors propose a new method to integrate expression profiles and protein-protein interaction (PPI) data. Bioinformatics techniques are used to study differential protein interaction mechanisms across the entire immune cell lineages and the transcriptional activators and modules and are analyzed in the context of exemplars obtained by clustering the PPI network. The results illustrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. The development of mathematical models of the immune response allows a better understanding of the multifaceted mechanisms of the defense system. In this scenario, as already introduced in the review from F. Castiglione et al., multiscale approaches play a fundamental role. B. de M. Quintela et al. propose a scheme for coupling distinct models of different scales and aspects of the immune system describing a new model that deals with the inflammation processes. These processes are simulated coupling and ordinary differential equations that are used as a model for the systemic response. The dynamics of various immune cells is shown in the presence of an antigen. There is a controversy about the relationship between HLA-A2 and Alzheimer's disease. HLA supposedly plays a modifier effect on the risk that depends on genetic loadings. Garcia and Murillo present an in silico method to evaluate this relationship and to reveal genes associated with both the HLA-A2 and Alzheimer's disease. They used experimental knowledge of protein-protein interactions to evaluate the top ranked genes shared by both concepts, previously found through text mining. With the vast amount of immunological data available, Immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. In the paper contributed by G. L. Zhang et al., it is clearly stated that the present challenge is to make the transition from data to actionable knowledge and wisdom and bridge the gap between knowledge and application. In their work, the authors present a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. This technique speeds up rational vaccine design by providing accurate and well-annotated data coupled with tailored Computational analysis tools and workflows. Hepatitis C virus and HIV are rapidly mutating viruses. They have adopted evolutionary strategies that allow escape from the host immune response via genomic mutations. Recent advances in high-throughput sequencing are reshaping the field of immune-virology of viral infections, as these allow fast and cheap generation of genomic data. P. Leung et al. propose a pipeline that allows visualization and statistical analysis of viral mutations that are associated with immune escape. Using next generation sequencing data from longitudinal analysis of HCV viral genomes during a single HCV infection, along with antigen specific T-cell responses detected from the same subject, the authors prove the applicability of these tools in the context of primary HCV infection. The proposed pipeline is a freely accessible collection of tools (see the paper for details). M. Kenn et al. point the attention on the dynamic variations in the distances between pairs of atoms that are used for clustering subdomains of biomolecules. They draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed, proving that this method reduces the Computational load of clustering drastically, demonstrating results for several biomolecules relevant in immunoinformatics. In the paper by R. Ribarics et al., molecular dynamics is presented as a valuable tool for the investigation of functional elements in biomolecules. They used several spline models to approximate the overall shape of MHC α-helices. The authors applied this technique to a series of MD simulations of alloreactive MHC molecules that allowed them to capture the dynamics of MHC α-helices' steric configurations. In the paper, they discuss the variability of spline models underlying the geometric analysis with varying polynomial degrees of the splines. HIV represents a widespread viral infection without cure. Drug treatment has transformed HIV disease into a treatable long-term infection. However, the appearance of mutations within the viral genome reduces the susceptibility of HIV to drugs. In the paper contributed by M. Haering et al., the authors discuss predictions derived from a mathematical model of HIV dynamics. Their results indicate that early therapy initiation (within 2 years after infection) is critical to delay AIDS progression. Francesco Pappalardo Vladimir Brusic Filippo Castiglione Christian Schonbach

  • Computational Immunology meets bioinformatics the use of prediction tools for molecular binding in the simulation of the immune system
    PLOS ONE, 2010
    Co-Authors: Nicolas Rapin, Ole Lund, Massimo Bernaschi, Filippo Castiglione
    Abstract:

    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.