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使用随机Petri网对分子生物学中的随机系统进行定量建模。

Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets.

作者信息

Goss P J, Peccoud J

机构信息

Department of Organismic and Evolutionary Biology, Harvard University, Museum of Comparative Zoology Laboratories, 26 Oxford Street, Cambridge, MA 02138, USA.

出版信息

Proc Natl Acad Sci U S A. 1998 Jun 9;95(12):6750-5. doi: 10.1073/pnas.95.12.6750.

Abstract

An integrated understanding of molecular and developmental biology must consider the large number of molecular species involved and the low concentrations of many species in vivo. Quantitative stochastic models of molecular interaction networks can be expressed as stochastic Petri nets (SPNs), a mathematical formalism developed in computer science. Existing software can be used to define molecular interaction networks as SPNs and solve such models for the probability distributions of molecular species. This approach allows biologists to focus on the content of models and their interpretation, rather than their implementation. The standardized format of SPNs also facilitates the replication, extension, and transfer of models between researchers. A simple chemical system is presented to demonstrate the link between stochastic models of molecular interactions and SPNs. The approach is illustrated with examples of models of genetic and biochemical phenomena where the ULTRASAN package is used to present results from numerical analysis and the outcome of simulations.

摘要

对分子生物学和发育生物学的综合理解必须考虑到所涉及的大量分子种类以及许多体内分子的低浓度。分子相互作用网络的定量随机模型可以表示为随机Petri网(SPN),这是计算机科学中发展起来的一种数学形式。现有的软件可用于将分子相互作用网络定义为SPN,并求解此类模型以获得分子种类的概率分布。这种方法使生物学家能够专注于模型的内容及其解释,而不是其实现。SPN的标准化格式也便于研究人员之间模型的复制、扩展和转移。本文展示了一个简单的化学系统,以说明分子相互作用的随机模型与SPN之间的联系。通过遗传和生化现象模型的例子来说明该方法,其中使用ULTRASAN软件包展示数值分析结果和模拟结果。

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本文引用的文献

2
Enzymatic reaction-rate theory: a stochastic approach.
Ann N Y Acad Sci. 1962 Mar 2;96:897-912. doi: 10.1111/j.1749-6632.1962.tb54110.x.
4
Stochastic mechanisms in gene expression.基因表达中的随机机制。
Proc Natl Acad Sci U S A. 1997 Feb 4;94(3):814-9. doi: 10.1073/pnas.94.3.814.
5
Stochastic simulation of the transducin GTPase cycle.转导素GTP酶循环的随机模拟。
Biophys J. 1996 Dec;71(6):3051-63. doi: 10.1016/S0006-3495(96)79499-7.
7
Qualitative analysis of biochemical reaction systems.
Comput Biol Med. 1996 Jan;26(1):9-24. doi: 10.1016/0010-4825(95)00042-9.
8
Evidence that hematopoiesis may be a stochastic process in vivo.
Nat Med. 1996 Feb;2(2):190-7. doi: 10.1038/nm0296-190.

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