Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland.
BMC Bioinformatics. 2010 Jan 27;11:59. doi: 10.1186/1471-2105-11-59.
The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.
This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.
Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.
高通量技术的发展，这些技术可以测量基因表达水平，为推断基因调控网络（也称为基因调控网络的反向工程）创建了一个数据库。然而，这些数据的性质使得这个过程非常困难。目前，已经存在几种从微阵列数据中以高精度发现基因之间定性因果关系的方法，但迄今为止，无法对真实生物数据集进行大规模定量分析，因为现有的方法不适合嘈杂和不足的真实微阵列数据。
本文对几种用于定量基因调控网络建模的现有进化算法进行了分析。目的是在通用框架下展示所使用的技术，并对方法进行全面比较。算法应用于来自 DNA 微阵列的合成和真实基因表达数据，并评估和比较了复制生物行为、可扩展性和对噪声的鲁棒性的能力。
提出了用于推断基因调控网络的进化算法的比较框架。确定了有前途的方法，并建立了适当的模型形式主义的开发平台。
