Virus-immune system interplay has a significant effect on the final fate of infection. Therefore, it is essential to survey the pathogenesis mechanism of viral-caused disease through the alteration of the immune system function. It can help to identify the critical functional players for designing novel therapeutic components and effective vaccines. One of the most effective approaches to surveying the biological processes is systems biology, in which the whole system is studied rather than the individual parts. Computational systems biology relies on the mathematical and statistical algorithms and modeling of complex biological systems. Besides the traditional investigation of immunological processes, voluminous immunological data have been generated by employing high-throughput data technology. Therefore, computational approaches and mathematical models can be employed in the analysis of immunogenomics, immunoproteomics, and immune-pharmacogenomics data. We aim to highlight studies that have been performed to survey the function of immune components after the viral infections and further utilization for designing vaccines or therapy through computational systems immunovirology.
With this Research Topic, we want to bring together current state of the art on computational systems biology applied to viral infections. We welcome the submissions of Original Research, Review, Mini-Review, Methods and Technology and Code articles focusing on, but not limited to, the following sub-topics:
• Novel computational algorithms, tools, and bioinformatics approaches to understand the changes in the function of immune systems due to the viral infections
• Novel computational algorithms, tools, and bioinformatics approaches to help the development of new vaccines and therapeutic approaches
• Multi-scale computational tools that provide an accurate approximation to immunological data at the intracellular, cellular, or individual level in viral infections
• Modelling viral-host interactions and immunity that explain infections/ disease progression
Virus-immune system interplay has a significant effect on the final fate of infection. Therefore, it is essential to survey the pathogenesis mechanism of viral-caused disease through the alteration of the immune system function. It can help to identify the critical functional players for designing novel therapeutic components and effective vaccines. One of the most effective approaches to surveying the biological processes is systems biology, in which the whole system is studied rather than the individual parts. Computational systems biology relies on the mathematical and statistical algorithms and modeling of complex biological systems. Besides the traditional investigation of immunological processes, voluminous immunological data have been generated by employing high-throughput data technology. Therefore, computational approaches and mathematical models can be employed in the analysis of immunogenomics, immunoproteomics, and immune-pharmacogenomics data. We aim to highlight studies that have been performed to survey the function of immune components after the viral infections and further utilization for designing vaccines or therapy through computational systems immunovirology.
With this Research Topic, we want to bring together current state of the art on computational systems biology applied to viral infections. We welcome the submissions of Original Research, Review, Mini-Review, Methods and Technology and Code articles focusing on, but not limited to, the following sub-topics:
• Novel computational algorithms, tools, and bioinformatics approaches to understand the changes in the function of immune systems due to the viral infections
• Novel computational algorithms, tools, and bioinformatics approaches to help the development of new vaccines and therapeutic approaches
• Multi-scale computational tools that provide an accurate approximation to immunological data at the intracellular, cellular, or individual level in viral infections
• Modelling viral-host interactions and immunity that explain infections/ disease progression
