Nuclear Dynamics Laboratory, The Babraham Institute, Cambridge, UK.
PLoS Comput Biol. 2011 Jul;7(7):e1002094. doi: 10.1371/journal.pcbi.1002094. Epub 2011 Jul 7.
Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency- or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes.
基因在核内网络的共定位被认为在决定基因表达模式方面起着重要作用。基于实验数据，我们构建了一个动力学模型来测试纯扩散是否可以解释在一个定义的亚核区域内观察到的基因共定位。二维和三维的简单标准布朗运动模型表明，即使没有任何直接相互作用，共调控基因也可能发生优先共定位，这表明这种共定位可能是由于转录因子的数量有限所致。染色质运动的实验数据表明，分数布朗运动比标准布朗运动更适合于对基因迁移进行建模，我们使用亚扩散过程来检验我们的动力学模型，该过程使基因更容易共定位。此外，为了将我们的模型与最近获得的实验数据进行比较，我们研究了基因与因子之间的关联水平，并提供了支持该动态模型验证的数据。作为我们模型的进一步应用，我们将其应用于对更多生物学观察结果的测试。我们发现，增加转录因子的数量，而不是工厂的数量和细胞核的大小，可能是导致基因共定位减少的原因。在转录因子的频率或幅度调制的情况下，我们的模型预测频率调制可能会增加其靶向基因之间的共定位。
