"Quantifying the Effects of Dose, Strain and Tissue Tropism on Parainfluenza Virus Infection Kinetics"
Human parainfluenza viruses (HPIVs) are a leading cause of acute respiratory infection hospitalization in children yet little is known about the growth and clearance kinetics. In mice, longitudinal measurements are possible by using reporter sendai viruses that express luciferase, where the insertion location yields wild-type (rSeV-luc(M-F)) or attenuated (rSeV-luc(P-M)) phenotypes. Bioluminescence from individual animals suggests that there is a rapid increase in expression within the first 1-2 days followed by a peak, biphasic clearance, and resolution. However, these kinetics vary with dose, strain, individuals, and the upper and lower respiratory tracts. To quantify the differences, we first translated the bioluminescence measurements taken from nasopharynx, trachea, and lungs into viral loads. We then fit a mathematical model to the estimated viral load data for each scenario using nonlinear mixed effects modeling. The results confirmed a higher rate of virus production with the rSeV-luc(M-F) virus compared to its attenuated counterpart, and suggested that the infected cell clearance was expedited when infected with rSeV-luc(M-F) in lungs with high dose. Our analysis suggested that the number of infected cells scales with dose, and that distinct infected cell clearance rates are associated with each dose, strain, individual and respiratory tract compartment. This analysis provides important insight into parainfluenza infection kinetics and the dynamical differences based on dose, viral attenuation, individual heterogeneity, and tissue tropism.
Maria Rodriguez Martinez
IBM Research Europe, Zurich (Switzerland)
"Multiscale clonal model of Germinal Center B cell differentiation"
Germinal Centers (GCs) are B cell follicles in the secondary lymphoid organs where B cells proliferate, mature their B cell receptors (BCRs) following exposure to antigen and interaction with other GCs cells, and eventually differentiate as plasma cells or memory B cells. We have recently developed a stochastic hybrid model of the GC reaction that combines: i) an intra-cellular gene circuit that captures the regulatory interplay of a few key master regulators of the differentiation process; and ii) an extra-cellular component that accounts for the stochastic events that take place in the GC, such as antigen acquisition and competition for T cell help (https://www.mdpi.com/2073-4409/9/6/1448). Mimicking the evolutionary processes undergone by B cells, in our model new B cells constantly emerge exhibiting mutated BCRs that present different affinity to the existing pool of antigens, which in turn probabilistically bias their evolution to certain fates. We faithfully recapitulate the process of BCR maturation by explicitly modelling the hypermutation process that takes place in the BCR genes. We compare the model predictions to experimental data of matched single-cell transcriptional high-resolution maps and BCR repertoire sequencing of GC B cells. Our explicit modeling of B cell maturation enables us to characterise the evolutionary processes and competition at the heart of the GC dynamics, and explains the emergence of clonal dominance as a result of initially small stochastic advantages in the affinity to antigen. Interestingly, a subset of the GC undergoes massive expansion of higher-affinity B cell variants (clonal bursts), leading to a loss of clonal diversity at a significantly faster rate than in GCs that do not exhibit clonal dominance. Our work contributes towards an in silico vaccine design, and has implications for the better understanding of the mechanisms underlying autoimmune disease and GC-derived lymphomas.
Gustavo Hernandez Mejia
Frankfurt Institue for Advanced Studies (FIAS); Goethe University Frankfurt, Germany
"Antibodies cross-reaction in influenza A infections: a modeling approach"
Disclosing key phenomena of how antibodies (Abs) induced by one influenza strain are effective against another, the so-called cross-reaction, is central for the design of universal flu vaccines. Here, using data of mice infected with influenza, we develop a stochastic mathematical modeling scheme to explore the impact of consecutive influenza A infection in the cross-reactome. The model successfully recapitulates the antibody cross-reactive data from mice infected with different H3N2 influenza strains. Of note, without framework modifications, the model can also represent the Abs response in mice to diverse H1N1 strains. Furthermore, we found that while the antigen differences, time of infection, and the B-cells population shape between infections directly influence the Abs outcome, the naive B-cells repertoire has minor effects on Abs behavior. Importantly, we found that affinity changes in immunity between infections satisfy necessary conditions for a successful Abs cross-reaction. We envisage this work will add to the forces among public makers, virologists, biologists, and theoreticians, bringing clarity of mind when experimental and clinical evidence is fragmented.
"Community-driven multiscale modeling of SARS-CoV-2"
The 2019 novel coronavirus (SARS-CoV-2) is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-, cellular-, and multicellular-scale processes that drive disease dynamics is limited. Multiscale simulation models can shed light on these dynamics, identify actionable 'choke points' for intervention, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. In this talk, we present progress by a multi-institution, multi-disciplinary coalition of over 40 mathematical biologists, immunologists, virologists, pharmacologists, and others to build a comprehensive multiscale model of SARS-CoV-2 infection dynamics and immune response in lung tissue. We will demonstrate and explore the current spatio-temporal agent-based model prototype, which includes intracellular virus and chemokine transport, virus-ACE2 receptor binding, receptor trafficking, viral replication dynamics (and subsequent viral release), infected cell phenotypic responses, cell-cell communication, immune cell recruitment, chemotactic exploration, T cell attacks on infected cells, and phagocytosis. This coalition-based approach is developing submodels in parallel and coordination, allowing us to rapidly advance towards a framework that can drive many independent investigations on COVID-19. Moreover, the novel mix of domain experts is fueling creative advances and new technical capabilities in multiscale tissue modeling. We anticipate that this progress will drive advances in immunology, inflammation, CAR T cell therapy, and virus-driven carcinogenesis for years to come. Interested members of the audience can try this open source framework live in a web browser at https://nanohub.org/tools/pc4covid19.