Subgroup Contributed Talks

eSMB2020 eSMB2020 Follow Thursday at 9:30am EDT
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Shilian Xu

Monash University
"Saturation of influenza virus neutralization and antibody consumption can both lead to bistable growth kinetics"
Influenza virus is a major human health threat. Neutralizing antibodies elicited through prior infection or vaccination play an irreplaceable role in protection from subsequent infection. The efficacy of antibody-dependent vaccines relies on both virus replication and neutralisation, but their quantitative relationship was unknown. Here we use mathematical models to quantitatively investigate viral survivability determined by antibody concentration and inocula size. We performed focus reduction assays for 49 seasonal influenza A/H3N2 viruses circulating during 2017–2019 against influenza antisera raised in ferrets, and find that the antibody consumption rates of individual reactions were either small or large, and this was strongly positively correlated with virus saturation. Regardless of antibody consumption rate, virus-antibody interactions always lead to antibody-induced bistable viral kinetics. As a result, at a specific interval of antibody concentration, small viral inocula are eliminated but not large virus inocula, which is triggered by saturated virus neutralization or antibody consumption. Our finding highlights virus-antibody interaction with different antigenic properties, thereby explaining commonly observed influenza re-infection and enhancing vaccine efficiency.

Jacob Summers

University of Tennessee
"Mathematical modeling of Mycobacterium tuberculosis dynamics in macaques"
Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), infecting a large proportion (~30%) of the world’s population. Only a small proportion of infected individuals develop clinical disease, and factors determining why some individuals remain asymptomatic and some become sick remain unknown. Unfortunately, understanding disease progression in humans remains challenging because most people do not know they are infected. Instead, several different animal species such as mice and monkeys can be infected with Mtb, thus, providing potential explanations for progression of humans to TB. Several recent studies provided interesting insights into early Mtb dynamics in monkeys. In particular, it was found that infection of monkeys with a set of individually barcoded Mtb strains resulted in most local foci of infection (called granulomas) to contain a single barcode. This suggested that individual granulomas were started by a single bacterium. We used stochastic mathematical models to understand whether this observation is also consistent with the hypothesis that multiple strains cause granuloma formation but most die during early dynamics. Our model included the simplest possible way to describe early stochastic dynamics (linear birth-death model); interestingly, we could not find one set of model parameters that allowed us to accurately describe both mean number of bacteria per granuloma and distribution of founder strains in different granulomas. This suggests early Mtb dynamics in monkeys are unlikely to be described by a simple birth-death model, and other biological aspects must be included in the model, in particular, impact of the immune response on rates of Mtb replication and death.

Suneet Singh Jhutty

Frankfurt Institue for Advanced Studies (FIAS); Goethe University Frankfurt, Germany
"Mapping Influenza from blood data using deep learning"
Seasonal and pandemic influenza causes enormous economic loss and leads to health complications and death. A better understanding of the role of different blood constituents during infection is necessary. Mathematical analysis of data can help us to better understand the link between blood properties and the influenza virus. Furthermore, the measurement of influenza viral load in a person is laborious and time-consuming. Therefore, it is crucial to have a reliable and fast method to determine the viral load in a patient. Here, we analyze blood data from mice and explore the different correlations between the key players during an infection. We test successfully a novel approach to use deep learning to infer viral load from this blood data. Hence, the viral load is directly inferred from a blood test. Using a simple multilayer perceptron, we train the algorithm with a comparatively small data set, to map blood data to the viral load. This shows the general possibility to use blood constituents measured in every routine blood count (like lymphocytes and erythrocytes) to infer the viral load in the body. Even with high variability in the data, the model prediction is reasonably accurate. Our results may lead the way to allow the measurement of the viral load from already collected blood data in the future. Hence, it would not only reduce the workload but be probably also faster. Lastly, our results suggest that platelets and granulocytes play an essential role during influenza infection.

Yuhuang Wu

University of New South Wales, Sydney
"Impact of fluctuation in frequency of HIV reactivation during antiretroviral therapy interruption"
Antiretroviral Therapy (ART) provides effective control of human immunodeficiency virus (HIV) replication and maintains the viral loads of HIV at undetectable levels. Interruption of ART causes recrudescence of HIV plasma viremia due to the reactivation of latently HIV-infected cells, generally within weeks of discontinuation of ART. Here we characterize the timing of both the initial and subsequent successful viral reactivations following ART interruption in macaques infected with simian immunodeficiency virus (SIV). We compare these to previous results from human patients infected with HIV. We find that on average the time until the first successful viral reactivation event is longer than the time between subsequent successful reactivations. Based on this result, we hypothesise that the reactivation frequency of both HIV and SIV may fluctuate over time and that this may have implications for treatment of HIV. We develop a stochastic model to simulate the behaviour of viral reactivation following ART interruption that incorporates fluctuations in the frequency of reactivation. Our model is able to explain the difference in timing between the initial and subsequent successful reactivation events. Furthermore, we show that one of the impacts of a fluctuating reactivation frequency would be to significantly reduce the efficacy of “anti-latency” interventions for HIV that aim to reduce the frequency of reactivation. It is therefore essential to consider the possibility of a fluctuating reactivation frequency when assessing the impact of such intervention strategies.

Vitaly Ganusov

University of Tennessee
"Structure-imposed constrains make Brownian walkers efficient searchers"
Pathogen-specific CD8 T cells face the problem of finding rare cells that present their cognate antigen either in the lymph node or infected tissue. To optimize the search for rare targets it has been proposed that T cells might perform a random walk with long displacements called Levy walks enabling superdiffusive behavior and shorter search times. Many agents ranging from molecules to large animals have been found to perform Levy walks suggesting that Levy walk-based search may be evolutionary selected. However, whether random walk patterns are driven by agent-intrinsic programs or being shaped by environmental factors remains largely unknown. We examined the behavior of activated CD8 T cells in the liver where both the movement of the cells and the underlying structural constrains can be clearly defined. We show that Plasmodium-specific liver-localized CD8 T cells perform Brownian, short displacement walks and yet display superdiffusive overall displacement, the cardinal feature of efficient Levy walks. Because liver-localized CD8 T cells are mainly associated with liver sinusoids, we show that linear structure of the sinusoids is sufficient to cause T cells to superdiffuse even when movement lengths are Brownian. Simulations of Brownian or Levy walkers in structures derived from the liver sinusoids illustrate that structure alone can enforce superdiffusive movement. Moreover, Brownian walkers require less time and thus are more efficient than Levy walkers at finding a rare target when T cells search for the infection in physiologically-derived liver structures. Importantly, analysis of fibroblastic reticular cell networks on which CD8 T cells move in lymph nodes also allows for superdiffusion in simulations which is not observed experimentally suggesting that structure is not the only factor determining movement patterns of T cells. Our results strongly suggest that observed patterns of movement of CD8 T cells are likely to result as a combination of a cell-intrinsic movement program, physical constrains imposed by the environmental structures, and other environmental cues. Future work needs to focus on quantifying relative contributions of these factors to the overall observed movement patterns of agents.

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Virtual conference of the Society for Mathematical Biology, 2020.