Stochastic theory of protein synthesis and polysome: Ribosome profile on a single mRNA transcript

Sharma, Ajeet K.; Chowdhury, Debashish

doi:10.1016/j.jtbi.2011.08.023

Cited by 39 publications

(39 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If motion is allowed only in one direction then ASEP is sometimes called the totally asymmetric simple exclusion process (TASEP). ASEP (and its many variants) is regarded as a paradigmatic model for non-equilibrium statistical mechanics and has been used to model and analyze various biological systems and processes, including intracellular transport, molecular motors, pedestrian dynamics and of course gene expression [75]–[80].…”

Section: Preliminaries: From Asep To the Rfmmentioning

confidence: 99%

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

2014

View full text Add to dashboard Cite

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.

show abstract

Section: Preliminaries: From Asep To the Rfmmentioning

confidence: 99%

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

2014

View full text Add to dashboard Cite

show abstract

“…Moreover, these basic TASEP models neither incorporate any mechanism for specifically selecting the correct amino acid monomer, nor do they allow for the possibility of translational error, etc. Therefore, such TASEP models are too simple to account for the effects of various mechano-chemical processes on the statistical properties of polysomes (58,72,111,130–134). Finally, these models did not consider more complicated and global aspects of translation such as competition between mRNA molecules on ribosomes and translation factors, and between cognate and near-cognate tRNAs.…”

Section: Introductionmentioning

confidence: 99%

“…For example, these aspects can include, among others, the different conformational steps of the ribosome during one iteration of elongation (56,59,135–136), the initiation itself includes various sub-steps (5′ end recognition, scanning, start codon recognition, etc) (55,137) that are usually not modeled in a rudimentary TASEP, the unfolding of the mRNA (61,138–139), the diffusion of tRNA molecules (19,46,51,54,61–62,66,69,131,134,140–142), interactions between the nascent peptide and the ribosome (61,143–144), diffusion of elongation factors (42), the addition of an amino acid to the growing polypeptide chain (5), tRNA molecule release (51), re-cycling of elongation factors (58,65,145), ribosome frame-shift (146), interactions between ribosomes and ribosome drop-off (46,53–54,59,69,131,135,142), mRNA decay (70), and many additional aspects (Figure 1C). …”

Section: Introductionmentioning

confidence: 99%

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

2016

View full text Add to dashboard Cite

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

show abstract

“…Extensive studies on the mechanisms of mRNA translation have been reported and draw on multiple approaches and tools such as experimental cell biology, bioinformatics, theoretical and computational biology, and recently systems and synthetic biology [2-9]. However, even though the fundamental mechanisms underlying mRNA translation are relatively clear, a number of detailed regulatory mechanisms are only now being uncovered, the full understanding of which will require an interplay between experiments and modelling.…”

Section: Introductionmentioning

confidence: 99%

mRNA translation and protein synthesis: an analysis of different modelling methodologies and a new PBN based approach

Zhao

Krishnan

2014

BMC Syst Biol

View full text Add to dashboard Cite

BackgroundmRNA translation involves simultaneous movement of multiple ribosomes on the mRNA and is also subject to regulatory mechanisms at different stages. Translation can be described by various codon-based models, including ODE, TASEP, and Petri net models. Although such models have been extensively used, the overlap and differences between these models and the implications of the assumptions of each model has not been systematically elucidated. The selection of the most appropriate modelling framework, and the most appropriate way to develop coarse-grained/fine-grained models in different contexts is not clear.ResultsWe systematically analyze and compare how different modelling methodologies can be used to describe translation. We define various statistically equivalent codon-based simulation algorithms and analyze the importance of the update rule in determining the steady state, an aspect often neglected. Then a novel probabilistic Boolean network (PBN) model is proposed for modelling translation, which enjoys an exact numerical solution. This solution matches those of numerical simulation from other methods and acts as a complementary tool to analytical approximations and simulations. The advantages and limitations of various codon-based models are compared, and illustrated by examples with real biological complexities such as slow codons, premature termination and feedback regulation. Our studies reveal that while different models gives broadly similiar trends in many cases, important differences also arise and can be clearly seen, in the dependence of the translation rate on different parameters. Furthermore, the update rule affects the steady state solution.ConclusionsThe codon-based models are based on different levels of abstraction. Our analysis suggests that a multiple model approach to understanding translation allows one to ascertain which aspects of the conclusions are robust with respect to the choice of modelling methodology, and when (and why) important differences may arise. This approach also allows for an optimal use of analysis tools, which is especially important when additional complexities or regulatory mechanisms are included. This approach can provide a robust platform for dissecting translation, and results in an improved predictive framework for applications in systems and synthetic biology.

show abstract

Stochastic theory of protein synthesis and polysome: Ribosome profile on a single mRNA transcript

Cited by 39 publications

References 50 publications

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

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

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

mRNA translation and protein synthesis: an analysis of different modelling methodologies and a new PBN based approach

Contact Info

Product

Resources

About