Monday, June 14 at 03:15pm (PDT)Monday, June 14 at 11:15pm (BST)Tuesday, June 15 07:15am (KST)
SMB2021 FollowMonday (Tuesday) during the "CT01" time block.
University of California, Los Angeles
"Developing a mechanistic view of mixed IgG antibody immune effector responses"
IgG antibodies bind antigen targets and then interact with Fcγ receptors (FcγR) on effector cells to direct cellular responses. Effector responses involve multiple cell types and processes (e.g., cytokines, phagocytosis) making it a systems-level challenge to precisely engineer these responses. We previously showed a multivalent binding model could accurately predict in vitro binding to synthetic complexes and in vivo anti-tumor antibody response.Here, we extend this work to predict the binding and immune response to complexes with combinations of Fc domains using a multivalent, multi-receptor, and multi-ligand model. We first validated the accuracy of this model through binding experiments using synthetic IgG mixtures. Applying this model, we could predict the effector-elicited target depletion of individual IgG and their combinations in mouse models of both ITP and melanoma. Our model correctly inferred that Kupffer cells are essential for platelet depletion in ITP, and specifically identified a FcγRIIBhigh subpopulation with outsized importance. Exploring the predicted effects of IgG combinations, we develop a framework for drug additivity. IgG synergy cannot occur with antibodies of identical antigen binding but antagonism is widespread through Fc receptor competition. These results demonstrate a suite of capabilities to more precisely engineer antibodies.
Indiana University Bloomington
"Multiscale modeling of SARS-CoV-2 infectious dynamics and antiviral drugs intervention"
The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Antiviral drugs and vaccines are currently under development and test to address this pandemic. However, the knowledge of dynamics of viral endocytosis, replication, single-cell response and pharmacodynamics is still limited. Therefore, the establishment of such dynamics is very urgent for understanding how SARS-CoV-2 infectious spread and finding an optimal therapeutic strategy. More importantly, this pandemic will likely not be the last one as new pathogens emergency, so we may be able to reuse the dynamics for faster response to another pandemic in the future. In this talk, we will present our work on multiscale modeling of SARS-CoV-2 infectious spread and antiviral drugs intervention (through an agent-based modeling approach-PhysiCell). This work is an extension of 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. The model of this work can be run on a cloud-hosted platform at: https://nanohub.org/resources/virion2pbpd.
Texas Tech University
"Deterministic and Stochastic in-host Tuberculosis Models for Bacterium-directed and Host-directed Therapy Combination"
The goal of this paper is to investigate in-host tuberculosis models to provide insights into therapy development. Focusing on therapy-targeting parameters, the parameter regions for different disease outcomes are identified from an established ODE model. Interestingly, the ODE model also demonstrates that the immune responses can both benefit and impede disease progression, which depend on the number of bacteria engulfed and released by macrophages. We then develop two Ito SDE models, which consider demographic variations at the cellular level only and environmental variations during therapies along with demographic variations. The SDE model with demographic variation suggests that stochastic fluctuations at the cellular level have significant influences on (1) the T-cell population in all parameter regions, (2) the bacterial population when parameters locate in the region with multiple disease outcomes, and (3) the uninfected macrophage population in parameter region representing active disease. Further, considering environmental variations from therapies, the second SDE model suggests that disease progression is more likely to be inhibited if therapies (1) can have fast return rates and (2) are able to bring parameter values into the disease clearance regions.
Pennsylvania State University
"Pathogen competition and mutant invasion in face of human choice in vaccination:"
Competition between multiple strain for vaccine preventable diseases often leads to exclusion of some pathogens, while it may influence the invasion of an emerging mutant in the population. Previous studies have shown that basic reproductive numbers among multiple strains are sufficient to predict which strains will invade a population. But human vaccination decision plays crucial role in shaping the type of strain that will invade or persist or get eliminated. Humans adapt to changing behavior or virulence of strains and for highly transmissible strains, they vaccinate at a faster rate due to higher perceived severity from the diseases. This on the other-hand gives scope for mutant strains to invade new number of susceptible in the population. In our study, we have coupled game dynamic model of vaccination choice and compartmental disease transmission model of two-strains to explore invasion, extinction and persistence of a mutant in the population which have a lower reproduction rate than the resident one. We illustrate that higher perceived strain severity and lower perceived vaccine efficacy are necessary conditions for persistence of a mutant strain. Numerically we explore these invasion and persistence analyses under asymmetric cross-protective immunity of these strains.