A Computational Technique to Estimate Within-Host Productively Infected Cell Lifetimes in Emerging Viral Infections

Abstract

Emerging viruses cause a lot of fatalities as they jump to humans from other species. Here we develop a novel technique to computationally estimate an important parameter of within-host viral infection: the lifespan of infected cells. Our approach is general and can be applied to a large class of viruses and leverages experimental data from replicon studies. Current techniques have difficulties reliably estimating infected cell lifetimes due to parameter identifiability and correlation of parameters. The infected cell lifetime is an important parameter that gives an estimate of how fast virus levels will decline. Our method would also help determine if some infected cells are short-lived or have longer lifespans with the consequence that longer lived cells could be reservoirs of infection. This would give a mechanistic understanding of why particular cell types are reservoirs of infection and may motivate therapy targeted towards these cell types. We apply our technique to West Nile virus (WNV), an emerging disease of public health relevance related to Zika virus. Our analysis suggests that the most abundant infectible cells are short-lived and could motivate therapy that targets these particular cells. Our approach is very general and can be combined with more traditional methods of using differential equation models to simulate viremia in hosts: the combination of these two techniques will likely yield results that may not be achievable using the models in isolation. This will be of great interest especially in modelling emerging diseases.

Emergent rules of computation in the Universe lead to life and consciousness: a computational framework for consciousness

“…It has memory: it remembers pathogens it has seen before. It can adapt to different challenges [13][14][15][16][17][18][19][20][21] and it has a sense of self (it does not attack cells in the body of the host) [40].…”