SARS-Cov-2 Systems Biology

SARS-CoV-2: Severe Acute Respiratory Syndrome Coronavirus 2; COVID19: Coronavirus Disease 2019; ACE2: Angiotensin-Converting Enzyme 2; Ang II: Angiotensin II; Ang (1-7): Angiotensin (1-7); Nsp: Nonstructural protein: RTC: Replication-Transcription Complex; AI: Artifi cial Intelligence; ODE: Ordinary Differential Equation; orf: Open Reading Frame; Nsp: Nonstructural protein; E: Envelop protein; S: Spike protein; M: Membrane protein; N: Nucleocapside protein

binds with high affi nity to the S protein, and forms a molecular complex that begins the process of fusion of the virion envelope and the host cell membrane. The nucleocapside containing the viral genome in then released into the host cytoplasm [5].
The open reading frames 1a (orf1a) and 1b (orf1b) are located near the untranslated region 5' (5'UTR) of the positive single stranded RNA ((+)ssRNA) and they code for the polyproteins pp1a and pp1ab. The process of translation occurs in the cytoplasm and produces a set of viral polyproteins whose maturation results in 11 nonstructural proteins (Nsp) from the orf1a segment (Nsp1 to Nsp11) and 5 nonstructural proteins from the orf1b segment (Nsp12 to Nsp16). Nsp proteins form the replication-transcription complex (RTC) in a doublemembrane vesicle where a set of nested subgenomic minusstrands of RNA ((-)sgRNA) are synthesized in a process of discontinuous transcription. These (-) sgRNAs serve as the templates for the production of subgenomic mRNAs from which the structural proteins E, M, N and S, together with the accessory proteins orf 3a, orf6, orf7a, orf7b, orf8, orf9b, orf9c and orf10 are synthesized [2,6,7]. SARS-CoV-2 uses the host translational machinery to redirect it to viral protein synthesis and replication, while cellular mRNA translation is inhibited [1]. Viral proteins are inserted into the host molecular machinery to modify and redirect a great number of host cell functions towards the production of more virus particles [8,9].
Experimental analysis of the interaction of viral and host proteins, or interactome, by Gordon and collaborators [10] has been a fundamental contribution to understand the form in

Discussion
Systems Biology uses network theory as a tool to understand the organization and dynamics of complex systems [14]. A theoretical approach to biological networks structure and function allows the integration of disperse experimental data in a coherent model of the spatio-temporal dynamics of interconnected cellular processes [2]. The number of nodes, the number of connections of each node to his neighbours, and the distribution of these connections in the network determine its complexity, structure and dynamical properties [15].
In the particular case of SARS-CoV-2 virus, the analysis of the statistical properties of the undirected network model ( Figure 1) of its interactome [2,10,15] shows that the degree distribution of its 332 proteins or nodes is a decaying exponential with great number of nodes with few connections and a low number of nodes with degree above 20. A model of the Calu-3-specifi c human-SARS-CoV-2 interactome, with 4,123 nodes, also shows a degree decaying exponential distribution [15].
Both results suggest that the SARS-CoV-2 network has a scalefree structure. In particular, orf8, M, Nsp7, orf9c, Nsp12 and Nsp13 proteins are the most connected nodes in the network and can be considered as hubs [2]. Three hubs from this set control approximately 102 viral-host processes [2] as shown in Figure 1: orf8 (47 links), M (29 links) and Nsp7 (26 links).
Hubs identifi cation is of great importance because these highly  information centrality and Page Rank index [2,15]. From these set of centrality measures, modularity is the key to determine if the host cell-virus interactome has any kind of nonrandom structure.
Modularity is the fraction of links that fall within a cluster minus the expected fraction if links were distributed at random, and indicates the nodes that are more densely connected between them than with the rest. In a nonrandom network, modularity has a value between 0 and 1 and reveals clues about the structure and the vulnerable spots of the network. A common method used for community detection is the Louvain method [2,16], from which the modularity of the undirected SARS-CoV-2 was calculated as approximately 0.85 that is far from the negative value for a random network. Furthermore, in the Gordon et al. interactome [10], 21 clusters were detected for which the number of nodes distributed between them is larger than the expected number due to random. The viral proteins orf8, M, orf9b, N, orf10, E, orf6, orf7 and S also belong to high modularity classes. These results suggest that SARS-CoV-2 is not a random network but a free-scale modular hierarchical structure in which orf8, N, and Nsp7 are the central hubs [2].

This kind of organization confers an extra level of complexity
to the SARS-CoV-2 molecular network allowing it to resist the attack of drugs on single nodes. However, simultaneous suppression of these three hubs could effectively disrupt the network organization and stop the viral replication cycle [17]. These results reveal the complexity of the SARS-CoV-2 molecular network, and open the possibility to understand the factors that determine the pathogenicity of the virus [2,13], and how can they be modifi ed to decrease it [2,13,15,18].
In silico analysis of the structure and dynamics of the SARS-CoV-2 network can be of aid in the identifi cation of drug targets and in the design of novel medicaments against COVID19.
Artifi cial Intelligence (AI) and bioinformatics methods have been used for this purpose with great success [19,20].
CoV-responsive human genes and their functional roles were indentifi ed based on available genomic data and on the basis of both the relative synonymous codon usage (RSCU)based correlation of viral genes with human genes and differential gene expression analysis. From this analysis, potential drugs for COVID-19 treatment based on these genes were predicted [21]. Furthermore, experimental data from Gordon, et al. [10], suggest that drug attack on orf8, N, and Nsp7 can block the effect of these three hubs on downstream nodes [2]. However, there are not therapeutic drugs that can hit directly these proteins, although there are FDA approved drugs than block their downstream activity. For example, orf8 is a target of Rapamycin, which also targets Nsp2 and N. Rapamycin blocks Tor1a activity in the quality control of protein folding in the Endoplasmic Reticulum (ER), disrupting the effects of orf8 [22,23]. Unfortunately, Rapamycin has strong immunosuppressant effects which turn it inadequate for the treatment of the COVID19 disease [21]. It is necessary more research to design drugs that target directly orf8, M and Nsp7 as a complement or an alternative to a vaccine.

Conclusion
Systems biology approach to the analysis of the SARS-CoV-2 interactome reveals that is a modular free-scale hierarchical network in which six nodes orf8, M, orf9b, N, orf10, E, orf6, orf7 and S that belong to high modularity classes control the fl ow This kind of modeling is necessary because viral protein insertion is a perturbation that distorts the original host cell phase space topology. Probably, viral proteins produce some type of bifurcation, with a restructuration of the original fi xed points, which leads the overall host cell dynamics to the production of new virus [24]. Characterization of the kind of bifurcation involved in the infection by SARS-CoV-2 is a necessary step to know the factors that determine the pathogenicity of the virus [2], and how they can be modifi ed with specifi c drugs to decrease it.
However, there is a lack of the quantitative information necessary to propose an ODEs-based continuous model for the analysis of the spatio-temporal dynamics of the infection due to the novelty of this virus species.

Confl ict of interest
The author declares that the research was conducted in the absence of any commercial or fi nancial relationships that could be construed as a potential confl ict of interest.