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      Entrainment to Periodic Initiation and Transition Rates in a Computational Model for Gene Translation

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          Abstract

          Periodic oscillations play an important role in many biomedical systems. Proper functioning of biological systems that respond to periodic signals requires the ability to synchronize with the periodic excitation. For example, the sleep/wake cycle is a manifestation of an internal timing system that synchronizes to the solar day. In the terminology of systems theory, the biological system must entrain or phase-lock to the periodic excitation. Entrainment is also important in synthetic biology. For example, connecting several artificial biological systems that entrain to a common clock may lead to a well-functioning modular system. The cell-cycle is a periodic program that regulates DNA synthesis and cell division. Recent biological studies suggest that cell-cycle related genes entrain to this periodic program at the gene translation level, leading to periodically-varying protein levels of these genes. The ribosome flow model (RFM) is a deterministic model obtained via a mean-field approximation of a stochastic model from statistical physics that has been used to model numerous processes including ribosome flow along the mRNA. Here we analyze the RFM under the assumption that the initiation and/or transition rates vary periodically with a common period . We show that the ribosome distribution profile in the RFM entrains to this periodic excitation. In particular, the protein synthesis pattern converges to a unique periodic solution with period . To the best of our knowledge, this is the first proof of entrainment in a mathematical model for translation that encapsulates aspects such as initiation and termination rates, ribosomal movement and interactions, and non-homogeneous elongation speeds along the mRNA. Our results support the conjecture that periodic oscillations in tRNA levels and other factors related to the translation process can induce periodic oscillations in protein levels, and may suggest a new approach for re-engineering genetic systems to obtain a desired, periodic, protein synthesis rate.

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          Most cited references58

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          Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution.

          Strikingly consistent correlations between rates of coding-sequence evolution and gene expression levels are apparent across taxa, but the biological causes behind the selective pressures on coding-sequence evolution remain controversial. Here, we demonstrate conserved patterns of simple covariation between sequence evolution, codon usage, and mRNA level in E. coli, yeast, worm, fly, mouse, and human that suggest that all observed trends stem largely from a unified underlying selective pressure. In metazoans, these trends are strongest in tissues composed of neurons, whose structure and lifetime confer extreme sensitivity to protein misfolding. We propose, and demonstrate using a molecular-level evolutionary simulation, that selection against toxicity of misfolded proteins generated by ribosome errors suffices to create all of the observed covariation. The mechanistic model of molecular evolution that emerges yields testable biochemical predictions, calls into question the use of nonsynonymous-to-synonymous substitution ratios (Ka/Ks) to detect functional selection, and suggests how mistranslation may contribute to neurodegenerative disease.
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            Synthetic biology: applications come of age

            Key Points Early synthetic biology designs, namely the genetic toggle switch and repressilator, showed that regulatory components can be characterized and assembled to bring about complex, electronics-inspired behaviours in living systems (for example, memory storage and timekeeping). Through the characterization and assembly of genetic parts and biological building blocks, many more devices have been constructed, including switches, memory elements, oscillators, pulse generators, digital logic gates, filters and communication modules. Advances in the field are now allowing expansion beyond small gene networks to the realm of larger biological programs, which hold promise for a wide range of applications, including biosensing, therapeutics and the production of biofuels, pharmaceuticals and biomaterials. Synthetic biosensing circuits consist of sensitive elements that bind analytes and transducer modules that mobilize cellular responses. Balancing these two modules involves engineering modularity and specificity into the various circuits. Biosensor sensitive elements include environment-responsive promoters (transcriptional), RNA aptamers (translational) and protein receptors (post-translational). Biosensor transducer modules include engineered gene networks (transcriptional), non-coding regulatory RNAs (translational) and protein signal-transduction circuits (post-translational). The contributions of synthetic biology to therapeutics include: engineered networks and organisms for disease-mechanism elucidation, drug-target identification, drug-discovery platforms, therapeutic treatment, therapeutic delivery, and drug production and access. In the microbial production of biofuels and pharmaceuticals, synthetic biology has supplemented traditional genetic and metabolic engineering efforts by aiding the construction of optimized biosynthetic pathways. Optimizing metabolic flux through biosynthetic pathways is traditionally accomplished by driving the expression of pathway enzymes with strong, inducible promoters. New synthetic approaches include the rapid diversification of various pathway components, the rational and model-guided assembly of pathway components, and hybrid solutions.
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              The selection-mutation-drift theory of synonymous codon usage.

              M Bulmer (1991)
              It is argued that the bias in synonymous codon usage observed in unicellular organisms is due to a balance between the forces of selection and mutation in a finite population, with greater bias in highly expressed genes reflecting stronger selection for efficiency of translation. A population genetic model is developed taking into account population size and selective differences between synonymous codons. A biochemical model is then developed to predict the magnitude of selective differences between synonymous codons in unicellular organisms in which growth rate (or possibly growth yield) can be equated with fitness. Selection can arise from differences in either the speed or the accuracy of translation. A model for the effect of speed of translation on fitness is considered in detail, a similar model for accuracy more briefly. The model is successful in predicting a difference in the degree of bias at the beginning than in the rest of the gene under some circumstances, as observed in Escherichia coli, but grossly overestimates the amount of bias expected. Possible reasons for this discrepancy are discussed.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                6 May 2014
                : 9
                : 5
                : e96039
                Affiliations
                [1 ]School of Electrical Engineering and the Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
                [2 ]Dept. of Mathematics and Cancer Institute of New Jersey, Rutgers University, Piscataway, New Jersey, United States of America
                [3 ]Dept. of Biomedical Engineering and the Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
                Universitat Pompeu Fabra, Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Wrote the paper: MM ES TT.

                Article
                PONE-D-13-41039
                10.1371/journal.pone.0096039
                4011696
                24800863
                644810b0-4238-49f1-9585-b076f96d7bda
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 September 2013
                : 2 April 2014
                Page count
                Pages: 14
                Funding
                All sources of support are internal (Tel-Aviv University and Rutgers University). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biophysics
                Biophysical Simulations
                Computational Biology
                Genetics
                Gene Expression
                Protein Translation
                Synthetic Biology
                Systems Biology

                Uncategorized
                Uncategorized

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