A New Model for Investigating the Evolution of Transcription Control Networks
A New Model for Investigating the Evolution of Transcription Control Networks
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DOI：
10.1162/artl.2009.stekel.006
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发表时间：
2009-06-01
期刊：
影响因子：
2.6
通讯作者：
Stekel, Dov J.
中科院分区：
文献类型：
--
作者：
Jenkins, Dafyd J.;Stekel, Dov J.
生物系统对复杂的行为和对环境的反应显示出无限的能力。这主要源于它们的基因网络。调控转录、翻译和基因调控的过程，以及网络进化的机制，如基因复制和水平基因转移，都得到了很好的理解。然而，从这些简单过程中产生的进化网络很难理解，而且由于涉及的时间尺度，很难对这些网络在活生物体中的进化进行实验。我们提出了一个新的框架来建模和研究转录网络在现实的、不同的环境中的进化。我们介绍的模型包含新颖，重要和逼真的特征，允许任意复杂转录网络的进化。分子间的相互作用没有规定；相反，它们是根据形状动态决定的，允许蛋白质功能自由进化。转录逻辑为定义遗传调控活动提供了一种灵活的机制。模拟表明，现实的生命周期是一种涌现的特性，即使在简单的环境中，也会进化出类似生命的复杂调节机制，包括稳定的蛋白质、不稳定的mRNA和抑制因子活性。这项研究还强调了使用硅遗传学技术来研究进化模型鲁棒性的重要性。
Biological systems show unbounded capacity for complex behaviors and responses to their environments. This principally arises from their genetic networks. The processes governing transcription, translation, and gene regulation are well understood, as are the mechanisms of network evolution, such as gene duplication and horizontal gene transfer. However, the evolved networks arising from these simple processes are much more difficult to understand, and it is difficult to perform experiments on the evolution of these networks in living organisms because of the timescales involved. We propose a new framework for modeling and investigating the evolution of transcription networks in realistic, varied environments. The model we introduce contains novel, important, and lifelike features that allow the evolution of arbitrarily complex transcription networks. Molecular interactions are not specified; instead they are determined dynamically based on shape, allowing protein function to freely evolve. Transcriptional logic provides a flexible mechanism for defining genetic regulatory activity. Simulations demonstrate a realistic life cycle as an emergent property, and that even in simple environments lifelike and complex regulation mechanisms are evolved, including stable proteins, unstable mRNA, and repressor activity. This study also highlights the importance of using in silico genetics techniques to investigate evolved model robustness.