Understanding adaptive immunity is of paramount importance for the prevention and treatment of disease as well as the development of novel immunotherapeutics and immunodiagnostics (precision immunology and medicine). Recently, the advent of high-throughput biological methods has provided unprecedented molecular insight into the immunology of B- and T-cells. The immense complexity of adaptive immunity spanning several orders of physical and temporal scales may however only be grasped by developing powerful computational immunology approaches, which process, model, and integrate these big immunological data.
The development of computational approaches and their application for the in silico interrogation and dissection of adaptive immunity has recently provided novel insight into (i) the architecture, diversity, and evolution of immune repertoires, (ii) phenotypic immune repertoire recognition and epitope mapping and prediction, and (iii) the cellular and molecular dynamics of lymphocyte interaction and regulation. Computational methods have been developed in many areas including (i) bayesian statistics, (ii) phylogenetics, (iii) mathematical ecology, (iv) network theory, (v) agent-based modeling, and (vi) machine learning. Furthermore, recent developments in immuno-bioinformatics with respect to the processing and error-correction of immunogenomics and proteomics data, utilizing high-performance computing, have greatly increased the biological conclusiveness of the downstream conception and application of computational immunology methods.
This Research Topic will give a comprehensive overview of current methods and applications of computational immunology for the dissection of adaptive immunity. We aim to broaden the conceptual foundation of current computational immunology as well as to highlight unresolved questions and future avenues of research in this field. We welcome the submission of Original Research, Review, Hypothesis, and Theory and Opinion articles encompassing experimental data, method development, mathematical theory and/or simulation studies that cover, but are not limited to, the following topics:
1. Multi-scale modeling of lymphocyte development, interaction and diversity.
2. Network analysis of immune repertoires.
3. Phylogenetic analysis of B-cell repertoires.
4. T and B-cell receptor recognition and MHC/epitope mapping and prediction.
5. Probabilistic approaches for modeling immunogenomics and immunoproteomics data.
6. Machine learning for immune data mining.
7. Computational immunodiagnostics.
8. Immunobioinformatics for fast, reliable, error-reduced treatment of large-scale immunological data.
Understanding adaptive immunity is of paramount importance for the prevention and treatment of disease as well as the development of novel immunotherapeutics and immunodiagnostics (precision immunology and medicine). Recently, the advent of high-throughput biological methods has provided unprecedented molecular insight into the immunology of B- and T-cells. The immense complexity of adaptive immunity spanning several orders of physical and temporal scales may however only be grasped by developing powerful computational immunology approaches, which process, model, and integrate these big immunological data.
The development of computational approaches and their application for the in silico interrogation and dissection of adaptive immunity has recently provided novel insight into (i) the architecture, diversity, and evolution of immune repertoires, (ii) phenotypic immune repertoire recognition and epitope mapping and prediction, and (iii) the cellular and molecular dynamics of lymphocyte interaction and regulation. Computational methods have been developed in many areas including (i) bayesian statistics, (ii) phylogenetics, (iii) mathematical ecology, (iv) network theory, (v) agent-based modeling, and (vi) machine learning. Furthermore, recent developments in immuno-bioinformatics with respect to the processing and error-correction of immunogenomics and proteomics data, utilizing high-performance computing, have greatly increased the biological conclusiveness of the downstream conception and application of computational immunology methods.
This Research Topic will give a comprehensive overview of current methods and applications of computational immunology for the dissection of adaptive immunity. We aim to broaden the conceptual foundation of current computational immunology as well as to highlight unresolved questions and future avenues of research in this field. We welcome the submission of Original Research, Review, Hypothesis, and Theory and Opinion articles encompassing experimental data, method development, mathematical theory and/or simulation studies that cover, but are not limited to, the following topics:
1. Multi-scale modeling of lymphocyte development, interaction and diversity.
2. Network analysis of immune repertoires.
3. Phylogenetic analysis of B-cell repertoires.
4. T and B-cell receptor recognition and MHC/epitope mapping and prediction.
5. Probabilistic approaches for modeling immunogenomics and immunoproteomics data.
6. Machine learning for immune data mining.
7. Computational immunodiagnostics.
8. Immunobioinformatics for fast, reliable, error-reduced treatment of large-scale immunological data.
