Zhou Xiaobo, Wang Xiaodong, Pal Ranadip, Ivanov Ivan, Bittner Michael, Dougherty Edward R
Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA.
Bioinformatics. 2004 Nov 22;20(17):2918-27. doi: 10.1093/bioinformatics/bth318. Epub 2004 May 14.
We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes.
We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.
我们假设，使用一种优化连通性的方法构建转录调控网络，将导致符合生物学预期的调控。一个关键的预期是，假设的网络应该产生少数几个非常强的吸引子，与原始观测结果高度相似，模拟生物状态的稳定性和确定性。另一个核心预期是，由于预期生物控制是分布式且相互强化的，对观测结果的解释应该导致非常少的连接方案。
我们提出了一种完全贝叶斯方法来构建强调网络拓扑结构的概率基因调控网络（PGRN）。该方法基于非线性感知器模型，使用可逆跳跃马尔可夫链蒙特卡罗（MCMC）技术，计算每个基因的可能父集、相应的预测因子和相关概率，并采用MCMC方法搜索网络配置，以找到具有最高贝叶斯分数的配置来构建PGRN。基于一组基因的观测行为构建了一个PGRN，这些基因的表达模式在一组黑色素瘤样本中有所不同，这些样本在细胞运动性和侵袭性方面表现出两种非常不同的表型。关键生物学特征在模型中得到了如实反映。其稳态分布包含与数据中观测到的状态相同或非常相似的吸引子，并且许多吸引子是单元素集，这模拟了生物稳定占据给定状态的倾向。最有趣的是，构成PGRN的最优生成网络的连通性规则非常相似，这与一个在分布式基础上运行且各组件之间有强相互作用的网络预期相符。
