Analysis of combinatorial regulation: scaling of partnerships between regulators with the number of governed targets.
Analysis of combinatorial regulation: scaling of partnerships between regulators with the number of governed targets.
复制标题
DOI：
10.1371/journal.pcbi.1000755
复制
发表时间：
2010-05-27
影响因子：
4.3
通讯作者：
Gerstein MB
中科院分区：
文献类型：
--
作者：
Bhardwaj N;Carson MB;Abyzov A;Yan KK;Lu H;Gerstein MB
通过组合监管，监管机构相互合作，以控制共同的目标，这使得少数监管机构可以管理许多目标。一个有趣的问题是，给定这种组合监管，监管机构的数量如何与目标数量成比例？在这里，我们解决这个问题，通过建立和分析共调控（共转录和共磷酸化）网络，描述控制共同基因的监管机构之间的伙伴关系。我们对五种不同的物种进行了分析：大肠杆菌到人类。这些揭示了伙伴关系网络的许多性质，例如，尽管存在具有许多伙伴的节点，但不存在经典的幂律度分布。我们还发现，合作监管伙伴关系的数量遵循指数饱和曲线的目标数量。(For E.在大肠杆菌和枯草芽孢杆菌中，由于基因排列成操纵子，只有该曲线的开始线性部分是明显的。为了获得对饱和过程的直觉，我们将生物调节与更常见的社会背景联系起来，在这种背景下，少数个体可以在互联网上形成复杂的连接网络。事实上，我们发现，伙伴关系网络的规模饱和，即使其输出的复杂性增加。我们还提出了各种模型来解释饱和现象。特别是，我们开发了一个简单的分析模型，以显示新的伙伴关系是如何获得越来越多的目标基因，在一定的假设下，它再现了所观察到的饱和。然后，我们建立了一个更一般的模拟网络的增长，并找到协议与广泛的真实的网络。最后，我们对观测数据进行了各种下采样计算，以说明我们的结论的鲁棒性。调控网络由调控因子如转录因子或激酶组成，其控制其靶基因的表达或活性。几乎总是有多个监管机构合作控制他们的目标。与更常见的情况相比，这些监管者可以被认为是社会或公司环境中的管理者，控制着他们共同的下属。我们在这项研究中解决的一个有趣的问题是，管理监管机构的数量如何与受管理目标的数量成比例。我们建立和分析共调节（共转录和共磷酸化）网络，描述控制共同基因的调节器之间的伙伴关系。我们使用一个简单的框架，跨越五个物种，展示了广泛的进化：大肠杆菌到人类。分析揭示了伙伴关系网络的许多性质，并表明，共同监管伙伴关系的数量遵循指数饱和曲线与目标的数量。为了获得更多的直观性，我们探索了更常见的情境，发现指数饱和关系也存在于几个社交网络中。最后，我们提出了一个简单的模型来解释这种关系，也存在于一个模拟的进化环境。
Through combinatorial regulation, regulators partner with each other to control common targets and this allows a small number of regulators to govern many targets. One interesting question is that given this combinatorial regulation, how does the number of regulators scale with the number of targets? Here, we address this question by building and analyzing co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We carry out analyses across five diverse species: Escherichia coli to human. These reveal many properties of partnership networks, such as the absence of a classical power-law degree distribution despite the existence of nodes with many partners. We also find that the number of co-regulatory partnerships follows an exponential saturation curve in relation to the number of targets. (For E. coli and Bacillus subtilis, only the beginning linear part of this curve is evident due to arrangement of genes into operons.) To gain intuition into the saturation process, we relate the biological regulation to more commonplace social contexts where a small number of individuals can form an intricate web of connections on the internet. Indeed, we find that the size of partnership networks saturates even as the complexity of their output increases. We also present a variety of models to account for the saturation phenomenon. In particular, we develop a simple analytical model to show how new partnerships are acquired with an increasing number of target genes; with certain assumptions, it reproduces the observed saturation. Then, we build a more general simulation of network growth and find agreement with a wide range of real networks. Finally, we perform various down-sampling calculations on the observed data to illustrate the robustness of our conclusions. A regulatory network consists of regulators such as transcription factors or kinases that control the expression or activity of their target genes. Almost always, there are multiple regulators partnering together to control their targets. Compared to more commonplace contexts, these regulators can be thought of as managers in a social or corporate setting controlling their common subordinates. One interesting question that we address here in this study is how the number of governing regulators scales with the number of governed targets. We build and analyze co-regulation (co-transcription and co-phosphorylation) networks that describe partnerships between regulators controlling common genes. We use a simple framework across five species that demonstrate a wide range of evolution: Escherichia coli to human. The analysis reveals many properties of partnership networks and shows that the number of co-regulatory partnerships follows an exponential saturation curve with the number of targets. To gain more intuition, we explore more commonplace contexts and find that exponential saturation relationship also exists in several social networks. Finally, we propose a simple model to explain this relationship that also exists in a simulated evolutionary environment.
登录
查看更多内容
影响因子：
14.9
作者：
Blüthgen N;Kiełbasa SM;Herzel H
通讯作者：
Herzel H
影响因子：
64.8
作者：
Jeong, H;Mason, SP;Oltvai, ZN
通讯作者：
Oltvai, ZN
影响因子：
7
作者：
Lespinet, O;Wolf, YI;Aravind, L
通讯作者：
Aravind, L
影响因子：
64.8
作者：
Albert, R;Jeong, H;Barabási, AL
通讯作者：
Barabási, AL
影响因子：
30.8
作者：
Ihmels, J;Friedlander, G;Barkai, N
通讯作者：
Barkai, N