A Model of Tuberculosis Transmission and Intervention Strategies in an Urban Residential Area

The model herein aims to explore the dynamics of the spread of tuberculosis (TB) in an informal settlement or township. The population is divided into households of various sizes and also based on commuting status. The model dynamics distinguishes between three distinct social patterns: the exposure of commuters during travel, random diurnal interaction and familial exposure at night. Following the general SLIR models, the population is further segmented into susceptible (S), exposed/latently infected (L), active/infectious (I), and recovered (R) individuals. During the daytime, commuters travel on public transport, while non-commuters randomly interact in the community to mimic chance encounters with infectious persons. At night, each family interacts and sleeps together in the home. The risk of exposure to TB is based on the proximity, duration, and frequency of encounters with infectious persons. The model is applied to a hypothetical population to explore the effects of different intervention strategies including vaccination, wearing of masks or scarves during the commute, prophylactic treatment of latent infections and more effective case-finding and treatment. The most important findings of the model are: (1) members of larger families are responsible for more disease transmissions than those from smaller families, (2) daily commutes on public transport provide ideal conditions for transmission of the disease, (3) improved diagnosis and treatment has the greatest impact on the spread of the disease, and (4) detecting TB at the first clinic visit, when patients are still smear negative, is key

A Model of Tuberculosis Transmission and Intervention Strategies in an Urban Residential Area

The model herein aims to explore the dynamics of the spread of tuberculosis (TB) in an informal settlement or township. The population is divided into households of various sizes and also based on commuting status. The model dynamics distinguishes between three distinct social patterns: the exposure of commuters during travel, random diurnal interaction and familial exposure at night. Following the general SLIR models, the population is further segmented into susceptible (S), exposed/latently infected (L), active/infectious (I), and recovered (R) individuals. During the daytime, commuters travel on public transport, while non-commuters randomly interact in the community to mimic chance encounters with infectious persons. At night, each family interacts and sleeps together in the home. The risk of exposure to TB is based on the proximity, duration, and frequency of encounters with infectious persons. The model is applied to a hypothetical population to explore the effects of different intervention strategies including vaccination, wearing of masks or scarves during the commute, prophylactic treatment of latent infections and more effective case-finding and treatment. The most important findings of the model are: (1) members of larger families are responsible for more disease transmissions than those from smaller families, (2) daily commutes on public transport provide ideal conditions for transmission of the disease, (3) improved diagnosis and treatment has the greatest impact on the spread of the disease, and (4) detecting TB at the first clinic visit, when patients are still smear negative, is key

Translation elongation can control translation initiation on eukaryotic mRNAs

Synonymous codons encode the same amino acid, but differ in other biophysical properties. The evolutionary selection of codons whose properties are optimal for a cell generates the phenomenon of codon bias. Although recent studies have shown strong effects of codon usage changes on protein expression levels and cellular physiology, no translational control mechanism is known that links codon usage to protein expression levels. Here, we demonstrate a novel translational control mechanism that responds to the speed of ribosome movement immediately after the start codon. High initiation rates are only possible if start codons are liberated sufficiently fast, thus accounting for the observation that fast codons are overrepresented in highly expressed proteins. In contrast, slow codons lead to slow liberation of the start codon by initiating ribosomes, thereby interfering with efficient translation initiation. Codon usage thus evolved as a means to optimise translation on individual mRNAs, as well as global optimisation of ribosome availability

The role of tRNA and ribosome competition in coupling the expression of different mRNAs in Saccharomyces cerevisiae

Protein synthesis translates information from messenger RNAs into functional proteomes. Because of the finite nature of the resources required by the translational machinery, both the overall protein synthesis activity of a cell and activity on individual mRNAs are controlled by the allocation of limiting resources. Upon introduction of heterologous sequences into an organism—for example for the purposes of bioprocessing or synthetic biology—limiting resources may also become overstretched, thus negatively affecting both endogenous and heterologous gene expression. In this study, we present a mean-field model of translation in Saccharomyces cerevisiae for the investigation of two particular translational resources, namely ribosomes and aminoacylated tRNAs. We firstly use comparisons of experiments with heterologous sequences and simulations of the same conditions to calibrate our model, and then analyse the behaviour of the translational system in yeast upon introduction of different types of heterologous sequences. Our main findings are that: competition for ribosomes, rather than tRNAs, limits global translation in this organism; that tRNA aminoacylation levels exert, at most, weak control over translational activity; and that decoding speeds and codon adaptation exert strong control over local (mRNA specific) translation rates

Performance limits and trade-offs in entropy-driven biochemical computers

The properties and fundamental limits of chemical computers have recently attracted significant interest as a model of computation, an unifying principle of cellular organisation and in the context of bio-engineering. As of yet, research in this topic is based on case-studies. There exists no generally accepted criterion to distinguish between chemical processes that compute and those that do not. Here, the concept of entropy driven computer (EDC) is proposed as a general model of chemical computation. It is found that entropy driven computation is subject to a trade-off between accuracy and entropy production, but unlike many biological systems, there are no trade-offs involving time. The latter only arise when it is taken into account that the observation of the state of the EDC is not energy neutral, but comes at a cost. The significance of this conclusion in relation to biological systems is discussed. Three examples of biological computers, including an implementation of a neural network as an EDC are given

Charting the dynamics of translation

Codon usage bias (CUB) is the well-known phenomenon that the frequency of synonymous codons is unequal. This is presumably the result of adaptive pressures favouring some codons over others. The underlying reason for this pressure is unknown, although a large number of possible driver mechanisms have been proposed. According to one hypothesis, the decoding time could be such a driver. A tacit assumption of this hypothesis is that faster codons lead to a higher translation rate which in turn is more resource efficient. While it is generally assumed that there is such a link, there are no rigorous studies to establish under which conditions the link between translation speed and rate actually exists. Using a computational simulation model and explicitly calculated codon decoding times, this contribution maps the entire range of dynamical regimes of translation. These simulations make it possible to understand precisely under which conditions translation speed and rate are linked

FSscan: a mechanism-based program to identify +1 ribosomal frameshift hotspots

In +1 programmed ribosomal frameshifting (PRF), ribosomes skip one nucleotide toward the 3′-end during translation. Most of the genes known to demonstrate +1 PRF have been discovered by chance or by searching homologous genes. Here, a bioinformatic framework called FSscan is developed to perform a systematic search for potential +1 frameshift sites in the Escherichia coli genome. Based on a current state of the art understanding of the mechanism of +1 PRF, FSscan calculates scores for a 16-nt window along a gene sequence according to different effects of the stimulatory signals, and ribosome E-, P- and A-site interactions. FSscan successfully identified the +1 PRF site in prfB and predicted yehP, pepP, nuoE and cheA as +1 frameshift candidates in the E. coli genome. Empirical results demonstrated that potential +1 frameshift sequences identified promoted significant levels of +1 frameshifting in vivo. Mass spectrometry analysis confirmed the presence of the frameshifted proteins expressed from a yehP-egfp fusion construct. FSscan allows a genome-wide and systematic search for +1 frameshift sites in E. coli. The results have implications for bioinformatic identification of novel frameshift proteins, ribosomal frameshifting, coding sequence detection and the application of mass spectrometry on studying frameshift proteins

Unique Cost Dynamics Elucidate the Role of Frameshifting Errors in Promoting Translational Robustness

There is now considerable evidence supporting the view that codon usage is frequently under selection for translational accuracy. There are, however, multiple forms of inaccuracy (missense, premature termination, and frameshifting errors) and pinpointing a particular error process behind apparently adaptive mRNA anatomy is rarely straightforward. Understanding differences in the fitness costs associated with different types of translational error can help us devise critical tests that can implicate one error process to the exclusion of others. To this end, we present a model that captures distinct features of frameshifting cost and apply this to 641 prokaryotic genomes. We demonstrate that, although it is commonly assumed that the ribosome encounters an off-frame stop codon soon after the frameshift and costs of mis-elongation are therefore limited, genomes with high GC content typically incur much larger per-error costs. We go on to derive the prediction, unique to frameshifting errors, that differences in translational robustness between the 5′ and 3′ ends of genes should be less pronounced in genomes with higher GC content. This prediction we show to be correct. Surprisingly, this does not mean that GC-rich organisms necessarily carry a greater fitness burden as a consequence of accidental frameshifting. Indeed, increased per-error costs are often more than counterbalanced by lower predicted error rates owing to more diverse anticodon repertoires in GC-rich genomes. We therefore propose that selection on tRNA repertoires may operate to reduce frameshifting errors