Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods.

IF 7.9 Q1 Medicine Wiley Interdisciplinary Reviews-Systems Biology and Medicine Pub Date : 2019-11-01 Epub Date: 2019-07-01 DOI:10.1002/wsbm.1459
Giulia Simoni, Federico Reali, Corrado Priami, Luca Marchetti
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引用次数: 14

Abstract

Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well-stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection-based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ-leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic-deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to computational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods.

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计算系统生物学的随机模拟算法:精确、近似和混合方法。
如今,数学建模在许多不同的研究领域发挥着关键作用。在系统生物学的背景下,数学模型及其相关的计算机模拟构成了研究的基本工具。其中,他们提供了一种方法来系统地分析系统扰动,发展假设来指导新的实验测试的设计,并最终评估特定分子作为新的治疗靶点的适用性。为了达到这些目的,随机模拟算法(SSAs)已经被引入,通过适当地考虑这种系统固有的随机性,来数值模拟搅拌良好的化学反应系统的时间演化。在这项工作中,我们回顾了在精确、近似和混合随机模拟的背景下引入的主要ssa。具体来说,我们将介绍精确随机模拟领域的直接法(DM)、第一反应法(FRM)、次反应法(NRM)和基于拒绝的SSA (RSSA)。然后,我们将在近似随机模拟领域提出τ跳跃法和化学朗格万法,并在混合随机-确定性模拟的背景下实现混合RSSA (HRSSA)。最后,我们将考虑鞘脂代谢模型,通过举例说明不同的模拟策略如何揭示对所研究的生物现象的不同见解,为SSA在计算系统生物学中的应用提供一个例子。本文分类为:系统特性与过程模型>机制模型>分析与计算方法>计算方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
18.40
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Journal Name:Wiley Interdisciplinary Reviews-Systems Biology and Medicine Focus: Strong interdisciplinary focus Serves as an encyclopedic reference for systems biology research Conceptual Framework: Systems biology asserts the study of organisms as hierarchical systems or networks Individual biological components interact in complex ways within these systems Article Coverage: Discusses biology, methods, and models Spans systems from a few molecules to whole species Topical Coverage: Developmental Biology Physiology Biological Mechanisms Models of Systems, Properties, and Processes Laboratory Methods and Technologies Translational, Genomic, and Systems Medicine
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