Zhao Yun-Bo, Krishnan J
Department of Chemical Engineering, Centre for Process Systems Engineering, Institute for Systems and Synthetic Biology, Imperial College London, South Kensington, London SW7 2AZ, UK.
BMC Syst Biol. 2014 Feb 27;8:25. doi: 10.1186/1752-0509-8-25.
mRNA 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.
We 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.
The 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.
信使核糖核酸（mRNA）翻译涉及多个核糖体在mRNA上的同步移动，并且在不同阶段还受到调控机制的影响。翻译可以用各种基于密码子的模型来描述，包括常微分方程（ODE）模型、随机并行更新单通道模型（TASEP）和Petri网模型。尽管这些模型已被广泛使用，但这些模型之间的重叠与差异以及每个模型假设的含义尚未得到系统阐明。在不同背景下选择最合适的建模框架以及开发粗粒度/细粒度模型的最合适方法尚不清楚。
我们系统地分析和比较了如何使用不同的建模方法来描述翻译过程。我们定义了各种统计等效的基于密码子的模拟算法，并分析了更新规则在确定稳态方面的重要性，而这一方面常常被忽视。然后提出了一种用于翻译建模的新型概率布尔网络（PBN）模型，该模型具有精确的数值解。此解与其他方法的数值模拟结果相匹配，并作为解析近似和模拟的补充工具。比较了各种基于密码子的模型的优缺点，并通过具有实际生物学复杂性的示例进行说明，如稀有密码子、提前终止和反馈调节。我们的研究表明，虽然不同模型在许多情况下给出大致相似的趋势，但在翻译速率对不同参数的依赖性方面也会出现重要差异，并且可以清晰地看到这些差异。此外，更新规则会影响稳态解。
基于密码子的模型基于不同层次的抽象。我们的分析表明，采用多模型方法来理解翻译过程能够确定哪些结论在建模方法的选择方面具有稳健性，以及何时（以及为何）可能会出现重要差异。这种方法还允许对分析工具进行优化使用，当纳入额外的复杂性或调控机制时，这一点尤为重要。这种方法可以为剖析翻译过程提供一个稳健的平台，并为系统生物学和合成生物学中的应用带来改进的预测框架。
