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.

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.

In the past two decades, 7 coronaviruses have infected the human population, with two major outbreaks caused by SARS-CoV and MERS-CoV in the year 2002 and 2012, respectively. Currently, the entire world is facing a pandemic of another coronavirus, SARS-CoV-2, with a high fatality rate. The spike glycoprotein of SARS-CoV-2 mediates entry of virus into the host cell and is one of the most important antigenic determinants, making it a potential candidate for a vaccine. In this study, we have computationally designed a multi-epitope vaccine using spike glycoprotein of SARS-CoV-2. The overall quality of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed the stability of the designed vaccine. Docking studies revealed stable interactions of the vaccine with Toll-Like Receptors and MHC Receptors. The in silico cloning and codon optimization supported the proficient expression of the designed vaccine in E. coli expression system. The efficiency of the candidate vaccine to trigger an effective immune response was assessed by an in silico immune simulation. The computational analyses suggest that the designed multi-epitope vaccine is structurally stable which can induce specific immune responses and thus, can be a potential vaccine candidate against SARS-CoV-2. Wuhan, a city in China, witnessed the outbreak of a febrile respiratory illness on 19th December 2019 due to the coronavirus provisionally named as 2019-nCoV and later SARS-CoV-2 1,2. The disease caused by this coronavirus was named as COVID-19 1,2. Since then, the world is experiencing a grave situation of global public health emergency due to the viral pandemic of severe febrile pneumonia like respiratory syndrome caused by the novel coronavirus 2. Coronaviruses are known to have caused three epidemics in the last two decades, namely COVID-19 in 2019/20, Severe Acute Respiratory Syndrome (SARS) in 2002, and Middle East Respiratory Syndrome (MERS) in 2012 3. As of June 3rd 2020, total cases of SARS-CoV-2 confirmed globally by World Health Organization are 6,287,771 with 379,941 reported deaths (https ://www.who.int/emerg encie s/disea ses/ novel-coron aviru s-2019/situa tion-repor ts). Human coronavirus (H-CoV) is a member of Coronaviridae family, a virus family characterized with the largest RNA genome (26-32 kb), among all of the viruses known till date 4-6. A lipid envelope bilayer containing the spike and membrane proteins surround the positive stranded RNA genome of this virus 7. The spike protein binds to the host cell receptors and releases the viral genome into the host cell, thereby facilitating the viral replication 8. Coronaviruses (CoVs) are mostly associated with respiratory illness and common cold 9 , but can also cause infections in Central Nervous System (CNS) 10. To date, four genera of coronaviruses (α, β, γ, δ) have been identified 11. Human coronaviruses (H-CoVs) belong to α (HCoV-229E and NL63) and β (MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-HKU1 and SARS-CoV-2) genera of coronavirus, respectively 11. In late De...