Modeling Within-Host Dynamics of Influenza Virus Infection Including Immune Responses

Abstract

Influenza virus infection remains a public health problem worldwide. The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood. Here, we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses. By comparing modeling predictions with both interferon and viral kinetic data, we examined the relative roles of target cell availability, and innate and adaptive immune responses in controlling the virus. Our results show that the rapid and substantial viral decline (about 2 to 4 logs within 1 day) after the peak can be explained by the killing of infected cells mediated by interferon activated cells, such as natural killer cells, during the innate immune response. After the viral load declines to a lower level, the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals. An adaptive immune response is needed in our model to explain the eventual viral clearance. This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection.

Effects of Aging on Influenza Virus Infection Dynamics

“…Thus, mathematical modeling has been used to capture the dynamics of influenza virus infection and to understand the interaction of the virus with the immune system (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Much of the work has been focused on the basic relationship between the host and the virus (25,26,32,34,35), whereas other work has strived to quantify the interplay between viral replication and adaptive immunity (27)(28)(29)(30)36). These models have been important to estimate the kinetic parameters describing influenza virus infection (25, 26, 28-30, 35, 36).…”

OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

“…As described in Pommerenke et al [23] -called hereafter reference study, C57BL/6J mice were infected with a mouse-adapted influenza A virus (PR8). Three replicates, from three individually infected mice, were taken for each time point after infection (1,2,3,5,8,10,14,18,22,26,30,40, 60 days) and nine replicates from three mockinfected mice (day 0). The complete dataset is accessible through ArrayExpress database under the accession number E-MTAB-764.…”

“…The level of severity as well as the outcome during Influenza A infection is defined by many host and viral factors. Moving further, the triggered host responses are highly dynamic [22] and long lasting [23], where the related cell populations and pathways are recruited in specific time intervals even after two months after the onset of infection. The ultimate goal in this kind of experimental design is to define a kinetic model that sets the timeline of response mechanisms throughout the acute innate immune response phase, to the clearance of the virus until the establishment of the long-lasting immunity and the restoration of homeostasis.…”

A Robust and Efficient Numerical Method for RNA-Mediated Viral Dynamics

“…There is no vaccine for HCV and for more than a decade the standard-of-care of pegylated interferon-alpha (IFN) and ribavirin was suboptima [3]. However the recent advent of direct-acting antivirals (DAAs) allows for interferon-free, all-oral treatment yielding cure rates exceeding 90% with pangenotypic activity and shorter durations of therapy (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) weeks) compared to IFN-based therapy (24-48 weeks [4]). While these highly effective DAAs are considered one of the greatest achievements in medicine, significant challenges remain for eliminating HCV infection such as finding an optimal approach to current DAA failures, preventing re-infection, identifying all those infected and the high cost of the new DAAs which represents a major barrier to treating the populations that are most affected by HCV [3].…”

“…Mathematical models are valuable tools for understanding the in vivo serum dynamics of viruses that trigger both persistent infection (e.g., HIV-1 [6][7][8][9], hepatitis B virus [10][11][12], hepatitis D virus [13][14][15], Theiler murine encephalomyelitis virus [16], herpes simplex virus [17] and HCV [18][19][20]) and acute infection (e.g., influenza A [21][22][23] and ebola [24]). Mathematical modeling is also improving our understanding of intracellular viral genome dynamics [25][26][27][28] and the quantitative events that underlie the immune response to pathogens [6,9].…”