论文标题

通过反向布朗动力学计算来自接触概率图的3D染色质配置

Computing 3D chromatin configurations from contact probability maps by Inverse Brownian Dynamics

论文作者

Kumari, K., Duenweg, B., Padinhateeri, R., Prakash, J. Ravi

论文摘要

染色质的三维组织在几个基因的长度尺度上对于确定染色质的功能状态 - 可访问性和基因表达的量至关重要。染色体构象捕获实验的最新进展提供了有关细胞种群中染色质组织的部分信息,即任何片段对之间的接触数,但没有导致这些接触计数的相互作用强度。但是,鉴于接触矩阵,确定整个染色质聚合物的完整3D组织是一个反问题。在目前的工作中,已经提出了一种基于粗粒的珠链链模型的新型逆性布朗动力学(IBD)方法来计算染色质不同段之间的最佳相互作用强度,以便满足实验测量的接触数概率约束。将此方法应用于两种不同的细胞类型的α-珠蛋白基因基因座,我们预测与基因座处的活性和抑制状态相对应的3D组织。我们表明,该区域的任何两个段之间的平均距离具有广泛的分布,并且不能仅基于接触概率作为简单的逆关系计算。针对多种归一化方法提出的结果表明,所有可测量的数量都可能取决于归一化的性质。我们认为,通过实验测量预测数量,可以推断出适当的归一化形式。

The three-dimensional organization of chromatin, on the length scale of a few genes, is crucial in determining the functional state - accessibility and the amount of gene expression - of the chromatin. Recent advances in chromosome conformation capture experiments provide partial information on the chromatin organization in a cell population, namely the contact count between any segment pairs, but not on the interaction strength that leads to these contact counts. However, given the contact matrix, determining the complete 3D organization of the whole chromatin polymer is an inverse problem. In the present work, a novel Inverse Brownian Dynamics (IBD) method based on a coarse grained bead-spring chain model has been proposed to compute the optimal interaction strengths between different segments of chromatin such that the experimentally measured contact count probability constraints are satisfied. Applying this method to the α-globin gene locus in two different cell types, we predict the 3D organizations corresponding to active and repressed states of chromatin at the locus. We show that the average distance between any two segments of the region has a broad distribution and cannot be computed as a simple inverse relation based on the contact probability alone. The results presented for multiple normalization methods suggest that all measurable quantities may crucially depend on the nature of normalization. We argue that by experimentally measuring predicted quantities, one may infer the appropriate form of normalization.

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