Exploring pathways from gene co-expression to network dynamics.
Exploring pathways from gene co-expression to network dynamics.
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DOI：
10.1007/978-1-59745-243-4_12
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Zhan, Ming
中科院分区：
文献类型：
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作者：
Li, Huai;Sun, Yu;Zhan, Ming
后基因组研究的主要挑战之一是了解表达基因的网络或连接如何产生生理和病理表型。为了解决这个问题，我们开发了两个计算算法，CoExMiner和Path Pro，以探索基因共表达的静态特征和基因网络的动态行为。CoExMiner基于B-Spline近似，然后是决定系数(CoD)估计，用于建模基因共表达模式。该算法允许探索转录反应，涉及编码在细胞内协同工作的蛋白质的基因的协调表达。Pathway Pro基于有限状态马尔可夫链模型，用于模拟转录网络的动态行为。该算法允许对广泛的网络反应进行定量评估，包括对疾病的易感性、给定药物的潜在有用性以及药物干预或热量限制等外部刺激的后果。我们通过检测肿瘤细胞和非肿瘤细胞中配体和受体的基因表达谱，以及白血病相关的bcr-abl通路的网络动力学，展示了CoExMiner和Path Pro的应用。这些检查揭示了与癌症发展相关的配体-受体相互作用的线性和非线性关系，确定了白血病的疾病和药物靶点，并为疾病的生物学提供了新的见解。使用这些新开发的算法进行的分析表明，计算系统生物学方法在生物学和医学研究中具有巨大的实用性。
One of major challenges in post genomic research is to understand how physiological and pathological phenotypes arise from the networks or connectivity of expressed genes. In addressing this issue, we have developed two computational algorithms, CoExMiner and PathwayPro, to explore static features of gene coexpression and dynamic behaviors of gene networks. CoExMiner is based on B-spline approximation followed by coefficient of determination (CoD) estimation for modeling gene coexpression patterns. The algorithm allows exploration of transcriptional responses that involve coordinated expression of genes encoding proteins that work in concert in the cell. PathwayPro is based on a finite-state Markov chain model for mimicking dynamic behaviors of a transcriptional network. The algorithm allows quantitative assessment of a wide range of network responses, including susceptibility to disease, potential usefulness of a given drug, and consequences of such external stimuli as pharmacological interventions or caloric restriction. We demonstrated the applications of CoExMiner and PathwayPro by examining gene expression profiles of ligands and receptors in cancerous and non-cancerous cells and network dynamics of the leukemia-associated BCR-ABL pathway. The examinations disclosed both linear and nonlinear relationships of ligand-receptor interactions associated with cancer development, identified disease and drug targets of leukemia, and provided new insights into biology of the diseases. The analysis using these newly developed algorithms show the great usefulness of computational systems biology approaches for biological and medical research.