A stochastic model for simulating ribosome kinetics in vivo
A stochastic model for simulating ribosome kinetics in vivo
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DOI：
10.1371/journal.pcbi.1007618
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发表时间：
2020-02-01
影响因子：
4.3
通讯作者：
Dykeman, Eric Charles
中科院分区：
文献类型：
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作者：
Dykeman, Eric Charles
体内蛋白质合成的计算模型非常复杂，因为它需要模拟整个转录组上的核糖体运动，并考虑 40 多种不同类型的 tRNA 和许多其他蛋白质因素的浓度效应。在这里，我报告了蛋白质翻译随机模型的开发，该模型能够模拟原核细胞中蛋白质合成的动态过程，该原核细胞包含数千个独特的 mRNA 序列，每个序列都有明确的核苷酸信息，并报告了一些超出现有模型范围的生物学预测。特别是，我表明，当延伸因子、tRNA、核糖体和蛋白质合成所需的其他因子之间的浓度依赖性相互作用的复杂网络被完全详细地包括在内时，几种生物现象，例如随着细菌生长速率而增加的肽延伸率，被预测为模型的新兴特性。这里提出的随机模型证明了在这种细节级别上考虑翻译过程的重要性，并提供了一个平台来询问翻译的各个方面，这些方面在更粗粒度的模型中难以研究。
Computational modelling of in vivo protein synthesis is highly complicated, as it requires the simulation of ribosomal movement over the entire transcriptome, as well as consideration of the concentration effects from 40+ different types of tRNAs and numerous other protein factors. Here I report on the development of a stochastic model for protein translation that is capable of simulating the dynamical process of in vivo protein synthesis in a prokaryotic cell containing several thousand unique mRNA sequences, with explicit nucleotide information for each, and report on a number of biological predictions which are beyond the scope of existing models. In particular, I show that, when the complex network of concentration dependent interactions between elongation factors, tRNAs, ribosomes, and other factors required for protein synthesis are included in full detail, several biological phenomena, such as the increasing peptide elongation rate with bacterial growth rate, are predicted as emergent properties of the model. The stochastic model presented here demonstrates the importance of considering the translational process at this level of detail, and provides a platform to interrogate various aspects of translation that are difficult to study in more coarse-grained models.