From the index case to global spread: the global mobility based modelling of the COVID-19 pandemic implies higher infection rate and lower detection ratio than current estimates.

BACKGROUND: Since the outbreak of the COVID-19 pandemic, multiple efforts of modelling of the geo-temporal transmissibility of the virus have been undertaken, but none describes the pandemic spread at the global level. The aim of this research is to provide a high-resolution global model of the pandemic that overcomes the problem of biased country-level data on the number of infected cases. To achieve this we propose a novel SIR-type metapopulation transmission model and a set of analytically derived model parameters. We used them to perform a simulation of the disease spread with help of the Global Epidemic and Mobility (GLEAM) framework embedding actual population densities, commute patterns and long-range travel networks. The simulation starts on 17 November 2019 with the index case (presymptomatic, yet infectious) in Wuhan, China, and results in an accurate prediction of the number of diagnosed cases after 154 days in multiple countries across five continents. In addition, the model outcome shows high compliance with the results of a random screening test conducted on pregnant women in the New York area. METHODS: We have built a modified SIR metapopulation transmission model and parameterized it analytically either by setting the values of the parameters based on the literature, or by assuming their plausible values. We compared our results with the number of diagnosed cases in twenty selected countries, ones which provide reliable statistics but differ substantially in terms of strength and speed of undertaken Non-Drug Interventions. The obtained 95% confidence intervals for the predictions are in agreement with the empirical data. RESULTS: The parameters that successfully model the pandemic are: the basic reproduction number R 0, 4.4; a latent non-infectious period of 1.1. days followed by 4.6 days of the presymptomatic infectious period; the probability of developing severe symptoms, 0.01; the probability of being diagnosed when presenting severe symptoms of 0.6; the probability of diagnosis for cases with mild symptoms or asymptomatic, 0.001. DISCUSSION: Parameters that successfully reproduce the observed number of cases indicate that both R 0 and the prevalence of the virus might be underestimated. This is in concordance with the newest research on undocumented COVID-19 cases. Consequently, the actual mortality rate is putatively lower than estimated. Confirmation of the pandemic characteristic by further refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis.

Co-regulation of translation in protein complexes.

BACKGROUND: Co-regulation of gene expression has been known for many years, and studied widely both globally and for individual genes. Nevertheless, most analyses concerned transcriptional control, which in case of physically interacting proteins and protein complex subunits may be of secondary importance. This research is the first quantitative analysis that provides global-scale evidence for translation co-regulation among associated proteins. RESULTS: By analyzing the results of our previous quantitative model of translation, we have demonstrated that protein production rates plus several other translational parameters, such as mRNA and protein abundance, or number of produced proteins from a gene, are well concerted between stable complex subunits and party hubs. This may be energetically favorable during synthesis of complex building blocks and ensure their accurate production in time. In contrast, for connections with regulatory particles and date hubs translational co-regulation is less visible, indicating that in these cases maintenance of accurate levels of interacting particles is not necessarily beneficial. CONCLUSIONS: Similar results obtained for distantly related model organisms, Saccharomyces cerevisiae and Homo sapiens, suggest that the phenomenon of translational co-regulation applies to the variety of living organisms and concerns many complex constituents. This phenomenon was also observed among the set of functionally linked proteins from Escherichia coli operons. This leads to the conclusion that translational regulation of a protein should always be studied with respect to the expression of its primary interacting partners.

Transimulation - protein biosynthesis web service.

Although translation is the key step during gene expression, it remains poorly characterized at the level of individual genes. For this reason, we developed Transimulation - a web service measuring translational activity of genes in three model organisms: Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. The calculations are based on our previous computational model of translation and experimental data sets. Transimulation quantifies mean translation initiation and elongation time (expressed in SI units), and the number of proteins produced per transcript. It also approximates the number of ribosomes that typically occupy a transcript during translation, and simulates their propagation. The simulation of ribosomes' movement is interactive and allows modifying the coding sequence on the fly. It also enables uploading any coding sequence and simulating its translation in one of three model organisms. In such a case, ribosomes propagate according to mean codon elongation times of the host organism, which may prove useful for heterologous expression. Transimulation was used to examine evolutionary conservation of translational parameters of orthologous genes. Transimulation may be accessed at http://nexus.ibb.waw.pl/Transimulation (requires Java version 1.7 or higher). Its manual and source code, distributed under the GPL-2.0 license, is freely available at the website.

A comprehensive, quantitative, and genome-wide model of translation.

Translation is still poorly characterised at the level of individual proteins and its role in regulation of gene expression has been constantly underestimated. To better understand the process of protein synthesis we developed a comprehensive and quantitative model of translation, characterising protein synthesis separately for individual genes. The main advantage of the model is that basing it on only a few datasets and general assumptions allows the calculation of many important translational parameters, which are extremely difficult to measure experimentally. In the model, each gene is attributed with a set of translational parameters, namely the absolute number of transcripts, ribosome density, mean codon translation time, total transcript translation time, total time required for translation initiation and elongation, translation initiation rate, mean mRNA lifetime, and absolute number of proteins produced by gene transcripts. Most parameters were calculated based on only one experimental dataset of genome-wide ribosome profiling. The model was implemented in Saccharomyces cerevisiae, and its results were compared with available data, yielding reasonably good correlations. The calculated coefficients were used to perform a global analysis of translation in yeast, revealing some interesting aspects of the process. We have shown that two commonly used measures of translation efficiency - ribosome density and number of protein molecules produced - are affected by two distinct factors. High values of both measures are caused, i.a., by very short times of translation initiation, however, the origins of initiation time reduction are completely different in both cases. The model is universal and can be applied to any organism, if the necessary input data are available. The model allows us to better integrate transcriptomic and proteomic data. A few other possibilities of the model utilisation are discussed concerning the example of the yeast system.