希尔函数遗传调控的本征噪声计算

Riccardo Ziraldo, Lan Ma
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引用次数: 1

摘要

本文采用理论建模和计算模拟的方法,包括随机模拟算法(SSA)和线性噪声近似(LNA)的标准方法,研究了物种低拷贝数随机基因调控中嵌入的固有噪声。在平均细胞水平上,Hill函数被广泛用作一种紧凑的格式来表示涉及多转录因子结合和协同的基因调控。启发式SSA和LNA方法(hSSA和hLNA)已被应用于研究采用Hill函数的随机模型。然而,目前尚不清楚哪种建模和仿真方法适合用于表征hill型基因调控的固有噪声,并且具有足够的精度和计算效率。在这项工作中,我们对以二阶激活和抑制Hill函数为代表的两种基因调控模型进行了噪声分析,试图评估五种现有噪声建模方法的性能。具体来说,SSA和LNA适用于由仅包含基本反应的Hill函数扩展而来的完整模型,而hSSA和hLNA适用于启发式使用Hill函数的简化模型。此外,我们使用最近提出的慢尺度LNA (ssLNA)方法来表征固有噪声,该方法用于处理具有快速和慢时间尺度的模型。使用SSA作为基础真值,我们发现hSSA和hLNA低估了hill型模型的本征噪声水平,尽管计算效率很高。ssLNA方法计算噪声的精度与SSA和LNA相当,同时所需的计算资源要少得多。此外,在相同的慢尺度框架下,化学朗之万方程(CLE)与SSA一样精确地模拟单细胞随机轨迹,但计算需求明显降低。本研究表明,在表征hill型遗传模型的内在随机性方面,ssLNA与慢尺度CLE相结合提供了一个优于其他四种方法的计算平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing intrinsic noise of the genetic regulation modeled by Hill functions
Intrinsic noise embedded in stochastic gene regulation due to low copy number of species has been studied using the approach of theoretical modeling and computational simulation, including the standard methods of stochastic simulation algorithm (SSA) and linear noise approximation (LNA). At average cell level, Hill functions are widely used as a compact format to represent gene regulation involving multi-transcription-factor binding and cooperativity. Heuristic SSA and LNA methods (hSSA and hLNA) have been applied to study stochastic models employing Hill functions. It is however unclear which modeling and simulation method is suitable to characterize intrinsic noise of Hill-type gene regulation with sufficient accuracy and computational efficiency. In this work, we perform noise analysis of two gene regulatory models represented by second-order activating and inhibitory Hill functions, seeking to evaluate the performance of five existing noise modeling methods. Specifically, SSA and LNA are applied to the full models that are expanded from the Hill functions containing only elementary reactions, while hSSA and hLNA are applied to reduced models where the Hill function is heuristically used. In addition, we characterize intrinsic noise using the slow-scale LNA (ssLNA) method that is recently proposed to deal with models with both fast and slow time scales. Using SSA as ground truth, we find that hSSA and hLNA underestimate the level of intrinsic noise in the Hill-type models, despite of high computational efficiency. The ssLNA approach calculates noise with comparable accuracy as SSA and LNA, while requesting much less computational resources. In addition, the chemical Langevin equation (CLE) under the same slow-scale framework simulates single-cell stochastic trajectories as accurately as SSA yet with significantly lower computational demands. This study shows that ssLNA complemented by slow-scale CLE offers a computational platform that out-performs the other four methods in characterizing intrinsic stochasticity of the Hill-type genetic models.
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