Bezerianos Anastasios, Maraziotis Ioannis A
Medical Physics Department, University of Patras, Rio, 22500, Greece.
Mol Biosyst. 2008 Oct;4(10):993-1000. doi: 10.1039/b800446n. Epub 2008 Jul 21.
The post-genomic era is flooded with data from high-throughput techniques such as cDNA microarrays. In the field of systems biology the reconstruction of gene regulatory networks from gene expression data is one of the major problems in understanding complex cell functions. Drawing conclusions from microarray data requires sophisticated computational analyses that will explore causal genetic relations. In this paper we provide a brief summary of some of the most recent and promising computational models and mathematical frameworks used to reconstruct, model and infer gene regulatory networks from data.
后基因组时代充斥着来自诸如cDNA微阵列等高通量技术的数据。在系统生物学领域，从基因表达数据重建基因调控网络是理解复杂细胞功能的主要问题之一。从微阵列数据得出结论需要复杂的计算分析，以探索因果遗传关系。在本文中，我们简要总结了一些用于从数据重建、建模和推断基因调控网络的最新且有前景的计算模型和数学框架。
