Haye Alexandre, Dehouck Yves, Kwasigroch Jean Marc, Bogaerts Philippe, Rooman Marianne
Unité de Bioinformatique Génomique et Structurale, CP 165/61, Université Libre de Bruxelles, Avenue Roosevelt 50, 1050 Bruxelles, Belgium.
Phys Biol. 2009 Jan 27;6(1):016004. doi: 10.1088/1478-3975/6/1/016004.
The time evolution of gene expression across the developmental stages of the host organism can be inferred from appropriate DNA microarray time series. Modeling this evolution aims eventually at improving the understanding and prediction of the complex phenomena that are the basis of life. We focus on the embryonic-to-adult development phases of Drosophila melanogaster, and chose to model the expression network with the help of a system of differential equations with constant coefficients, which are nonlinear in the transcript concentrations but linear in their logarithms. To reduce the dimensionality of the problem, genes having similar expression profiles are grouped into 17 clusters. We show that a simple linear model is able to reproduce the experimental data with very good precision, owing to the large number of parameters that represent the connections between the clusters. Remarkably, the parameter reduction allowed elimination of up to 80-85% of these connections while keeping fairly good precision. This result supports the low-connectivity hypothesis of gene expression networks, with about three connections per cluster, without introducing a priori hypotheses. The core of the network shows a few gene clusters with negative self-regulation, and some highly connected clusters involving proteins with crucial functions.
宿主生物体发育阶段基因表达的时间演变可从适当的DNA微阵列时间序列中推断出来。对这种演变进行建模最终旨在增进对作为生命基础的复杂现象的理解和预测。我们专注于黑腹果蝇从胚胎到成虫的发育阶段，并选择借助常系数微分方程组对表达网络进行建模，这些方程组在转录本浓度上是非线性但在其对数上是线性的。为降低问题的维度，将具有相似表达谱的基因归为17个簇。我们表明，由于大量表示簇间连接的参数，一个简单的线性模型能够以非常高的精度重现实验数据。值得注意的是，参数简化允许在保持相当高的精度的同时消除高达80 - 85%的这些连接。这一结果支持了基因表达网络的低连接性假设，即每个簇约有三个连接，且无需引入先验假设。网络的核心显示出一些具有负自调节的基因簇，以及一些涉及具有关键功能蛋白质的高度连接的簇。
