Viral Invasion Flow-Chart for Pathogens With Replication Target in a Host Cell

Introduction

After the last year's pandemic, some interdisciplinary approaches allowed the investigation of the virus pathways inside the host cells, appealing to statistics tools and artificial intelligence. The actual organizational chart is an extension of a previous one, (Ravariu et al, 2021a). The viruses demand specific cells defined by the tissue tropism to multiply, (Hicham et al, 2016). They operate by conventional parasitism, altering the host cell metabolism after the invasion. The virion-cell affinity is strongly related to the lock-key matching between cell receptors and Spike-Glycoproteins (SG) from the viral envelope (VE). Despite well-known theories about the viral invasion pathway inside the animal or human cells (Cojocaru et al., 2020; Binod & Bala, 2016; Russu et al., 2020), many secondary mechanisms and their specific steps are not completely understood, (Reggio et al., 2020). In those non-elucidated cases, the theory lack is filled with examples, (Delcuve et al., 2020). For instance, the viral uncoating mechanism is not completely known once the virus has entered the host cell, except for some particular examples. Many authors largely described the viral capsid loss for picornavirus in recent years, (Real-Hohn et al., 2020; Rodriguez & Arzt, 2022). Some advanced studies proposed a possible variant of orderly protein efflux from picornavirus capsid by distinct sets of nano-channels (Ren et al., 2013), but an experimental method was exposed in 2020, (Real-Hohn et al., 2020). SARS-COV-2 virus was discovered to bind to a specific receptor as angiotensin-converting enzyme 2 (ACE2), (Hoffman et al., 2020). ACE2 receptor bind to the surface of the cell membranes from the lungs, heart, arterial vessels and bowels, (Hamming et al., 2004). Viral tropism is not defined only by the affinity to cell receptors but also ability of the cell to support virus replication under many circumstances like - transcription factors, pH, physical barriers, local temperature, and enzymatic tools in common with the cell nucleus.

On the other hand, software engineering offers algorithms and flowchart tools to model the interaction of the cells with foreign intruders, (Yuan & Allen, 2011; Ravariu et al., 2009).

In 2020, a flowchart of SARS-COV2 mutating virus and its dispersion in society, was approached as multi-scales analysis, (Bellomo et al, 2022). During the last Covid-19 pandemic, the flowcharts were frequently applied, for epidemiologic purposes, (Prasad et al., 2022), or medical statistics in huge datasets (Hackett & Zaia, 2021). The software applies different target decoy analysis to estimate false discovery rates and they use chart database oriented search methodologies with different degrees of quantification capabilities. Other authors applied multi-scale modeling for the tumor, including oncolytic virus interactions. The method is based on moving boundaries, which results from a system of differential equations with partial derivatives both at tissue-scale as macro-scale and at cell-scale as micro-scale (Alzahrani et al., 2019). A large review paper was allocated to mathematical modeling of viral invasion, starting from dynamic replication, up to some strategies development of the antiviral drugs, (Zitzmann & Kaderali, 2018). They modeled the virus-host cell aggregate, targeting to establish new drug classes, to predict the cure duration and to minimize the treatment costs. The main accomplishments made by mathematical modeling in viral infections were emphasized at the cellular scale and at multi-scale level for the Dengue virus, Ebola virus, HIV (Human Immunodeficiency Virus), Influenza A virus, Zika virus, Hepatitis C virus, (Zitzmann & Kaderali, 2018). Recently, the oncolytic virotherapy in pancreatic cancer has just been approached by cellular automata models. The authors used probabilistic initial conditions for the diffusion–reaction equation with smoothed point viral sources. The equation was discretized by the finite difference method and integrated by the implicit-explicit splitting method, for Monte Carlo simulations (Chen et al., 2020).