Vinokour Sophie, Tuller Tamir
Department of Biomedical Engineering, Engineering Faculty, Tel Aviv University, Israel.
The Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv 69978, Israel.
Comput Struct Biotechnol J. 2021 Nov 8;19:6064-6079. doi: 10.1016/j.csbj.2021.10.030. eCollection 2021.
mRNA translation is the process which consumes most of the cellular energy. Thus, this process is under strong evolutionary selection for its optimization and rational optimization or reduction of the translation efficiency can impact the cell growth rate. Algorithms for modulating cell growth rate can have various applications in biotechnology, medicine, and agriculture. In this study, we demonstrate that the analysis of these algorithms can also be used for understanding translation. We specifically describe and analyze various generic algorithms, based on comprehensive computational models and whole cell simulations of translation, for introducing silent mutations that can either reduce or increase ribosomal traffic jams along the mRNA. As a result, more or less resources are available, for the cell, promoting improved or reduced cells growth-rate, respectively. We then explore the cost of these algorithms' performance, in terms of their computational time, the number of mutations they introduce, the modified genomic region, the effect on local translation rates, and the properties of the modified genes. Among others, we show that mRNA levels of a gene are much stronger predictors for the effect of its engineering on the ribosomal pool than the ribosomal density of the gene. We also demonstrate that the mutations at the ends of the coding regions have a stronger effect on the ribosomal pool. Furthermore, we report two optimization algorithms that exhibit a tread-off between the number of mutations they introduce and their executing time. The reported results here are fundamental both for understanding the biophysics and evolution of translation, as well as for developing efficient approaches for its engineering.
mRNA翻译是消耗细胞大部分能量的过程。因此，这一过程在进化上受到强烈选择以实现优化，合理优化或降低翻译效率会影响细胞生长速率。调节细胞生长速率的算法在生物技术、医学和农业中有着广泛应用。在本研究中，我们证明对这些算法的分析也可用于理解翻译过程。我们基于全面的计算模型和翻译的全细胞模拟，具体描述并分析了各种通用算法，这些算法用于引入沉默突变，从而减少或增加沿mRNA的核糖体交通拥堵。结果，细胞可获得或多或少的资源，分别促进细胞生长速率的提高或降低。然后，我们从计算时间、引入的突变数量、修饰的基因组区域、对局部翻译速率的影响以及修饰基因的特性等方面，探讨了这些算法性能的代价。其中，我们表明基因的mRNA水平比其核糖体密度更能有力地预测其工程改造对核糖体库的影响。我们还证明编码区末端的突变对核糖体库有更强的影响。此外，我们报告了两种优化算法，它们在引入的突变数量和执行时间之间表现出一种权衡。本文所报道的结果对于理解翻译的生物物理学和进化，以及开发有效的翻译工程方法都具有重要意义。
