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A Novel Knowledge-Driven Systems Biology Approach for Phenotype Prediction upon Genetic Intervention

机译:基于遗传干预的表型预测的新型知识驱动系统生物学方法

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Deciphering the biological networks underlying complex phenotypic traits, e.g., human disease is undoubtedly crucial to understand the underlying molecular mechanisms and to develop effective therapeutics. Due to the network complexity and the relatively small number of available experiments, data-driven modeling is a great challenge for deducing the functions of genes/proteins in the network and in phenotype formation. We propose a novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype. Edges in this network depict physical interactions between genes and/or proteins. A qualitative knowledge model first translates typical molecular interactions into constraints when resolving the DBN structure and parameters. Therefore, the uncertainty of the network is restricted to a subset of models which are consistent with the qualitative knowledge. All models satisfying the constraints are considered as candidates for the underlying network. These consistent models are used to perform quantitative inference. By in silico inference, we can predict phenotypic traits upon genetic interventions and perturbing in the network. We applied our method to analyze the puzzling mechanism of breast cancer cell proliferation network and we accurately predicted cancer cell growth rate upon manipulating (anti)cancerous marker genes/proteins.
机译:理解诸如人类疾病之类的复杂表型特征的生物学网络无疑对于理解潜在的分子机制和开发有效的治疗方法至关重要。由于网络的复杂性和可用实验的相对较少,数据驱动的建模对于推断网络中的基因/蛋白质和表型形成的功能是一个巨大的挑战。我们提出了一种新颖的知识驱动的系统生物学方法,该方法利用定性知识来构建动态贝叶斯网络(DBN),以表示基于特定表型的生物学网络。该网络中的边缘描述了基因和/或蛋白质之间的物理相互作用。定性知识模型在解析DBN结构和参数时首先将典型的分子相互作用转化为约束。因此,网络的不确定性仅限于与定性知识一致的模型子集。满足约束条件的所有模型均被视为基础网络的候选者。这些一致的模型用于执行定量推断。通过计算机推断,我们可以根据遗传干预和网络干扰来预测表型性状。我们应用了我们的方法来分析乳腺癌细胞增殖网络的令人困惑的机制,并通过操纵(抗)癌标志物基因/蛋白质来准确预测癌细胞的生长速度。

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