存在分子噪音时两种前馈遗传模式的动力学。

IF 2 4区 生物学 Q2 BIOLOGY
Cooper Doe, David Brown, Hanqing Li
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引用次数: 0

摘要

了解基因调控网络中常见图案的功能是系统生物学的一个重要目标。前馈环(FFLs)就是这种模式的一个例子。在前馈环中,一个基因(X)直接或通过一个中间基因(Y)调控另一个基因(Z)。以往的理论研究根据 FFLs 对输入信号变化的瞬态响应(使用确定性模型)和围绕稳态的波动(使用随机模型),提出了 FFLs 的几种可能功能。在本文中,我们研究了 "相干 1 型 "和 "非相干 1 型 "这两种最常见 FFL 的随机模型。我们将 DNA 结合、转录、翻译和衰变视为随机过程,从而将分子噪声纳入其中。通过将这些环路的动态与替代网络模型(其中 X 不调控 Y)进行比较,我们探索了 FFLs 如何在存在噪声的情况下处理信息。这项工作强调了在研究基因调控网络的瞬态和稳态行为时纳入现实分子噪声的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamics of two feed forward genetic motifs in the presence of molecular noise
Understanding the function of common motifs in gene regulatory networks is an important goal of systems biology. Feed forward loops (FFLs) are an example of such a motif. In FFLs, a gene (X) regulates another gene (Z) both directly and via an intermediary gene (Y). Previous theoretical studies have suggested several possible functions for FFLs, based on their transient responses to changes in input signals (using deterministic models) and their fluctuations around steady state (using stochastic models). In this paper we study stochastic models of the two most common FFLs, “coherent type 1” and “incoherent type 1”. We incorporate molecular noise by treating DNA binding, transcription, translation, and decay as stochastic processes. By comparing the dynamics of these loops with models of alternative networks (in which X does not regulate Y), we explore how FFLs act to process information in the presence of noise. This work highlights the importance of incorporating realistic molecular noise in studying both the transient and steady-state behavior of gene regulatory networks.
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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