A computer simulation of in vivo protein synthesis

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

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.

Mathematical and Computational Modelling of Ribosomal Movement and Protein Synthesis: an overview

Translation or protein synthesis consists of a complex system of chemical reactions, which ultimately result in decoding of the mRNA and the production of a protein. The complexity of this reaction system makes it difficult to quantitatively connect its input parameters (such as translation factor or ribosome concentrations, codon composition of the mRNA, or energy availability) to output parameters (such as protein synthesis rates or ribosome densities on mRNAs). Mathematical and computational models of translation have now been used for nearly five decades to investigate translation, and to shed light on the relationship between the different reactions in the system. This review gives an overview over the principal approaches used in the modelling efforts, and summarises some of the major findings that were made.

Model-based inference of gene expression dynamics from sequence information

A dynamic model of prokaryotic gene expression is developed that makes considerable use of gene sequence information. The main contribution arises from the fact that the combined gene expression model allows us to access the impact of altering a nucleotide sequence on the dynamics of gene expression rates mechanistically. The high level of detail of the mathematical model is considered as an important step towards bringing together the tremendous amount of biological in-depth knowledge that has been accumulated at the molecular level, using a systems level analysis (in the sense of a bottom-up, inductive approach). This enables to the model to provide highly detailed insights into the various steps of the protein expression process and it allows us to access possible targets for model-based design. Taken as a whole, the mathematical gene expression model presented in this study provides a comprehensive framework for a thorough analysis of sequence-related effects on the stages of mRNA synthesis, mRNA degradation and ribosomal translation, as well as their nonlinear interconnectedness. Therefore, it may be useful in the rational design of recombinant bacterial protein synthesis systems, the modulation of enzyme activities in pathway design, in vitro protein biosynthesis, and RNA-based vaccination.

Translational step inhibited in vivo by aflatoxin B1 in rat-liver polysomes

Aflatoxin B1 strongly inhibits protein synthesis in rat liver cells. We previously demonstrated that this inhibition could be divided into two steps: up to 5 h aflatoxin blocks protein synthesis directly and specifically at the polysome level; beyond 7 h protein synthesis inhibition appears chiefly as a consequence of transcription impairment due to drug action. This paper confirms the foregoing results and represents an attempt to localize the translational step inhibited in vivo by aflatoxin B1. We used the simulation study developed by Li, Kisilevsky, Wasan and Hammond, 1972 (Biochim. Biophys. Acta, 272, 451-462) to determine precisely the site inhibited in vivo after drug intoxication. This analysis is based on two parameters: the kinetics of polysome labeling to follow the nascent peptide synthesis, and the kinetics of supernatant labeling to follow the completed protein synthesis. Up to 5 h after dosing, aflatoxin specifically inhibits the elongation and/or termination steps during protein synthesis; after longer periods of time inhibition occurs essentially at the initiation step. When the intracellular concentration of aflatoxin is too high, particularly 2 h after dosing, each step of protein synthesis is blocked. Polypeptide synthesis by the postmitochondrial supernatants isolated from aflatoxin-treated animals is impaired in the same proportion as protein synthesis in vivo. The damage caused by aflatoxin is mostly observed on microsomes. However, purified polysomes isolated from aflatoxin-treated rats synthesize proteins in vitro to the same extent as those from controls. These results suggest that aflatoxin metabolite(s) are bound to polysomes with noncovalent bonds. These active metabolites are probably lost during polysome isolation procedures. Finally, relationships between protein metabolism and aflatoxin carcinogenesis are discussed.