Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India.
Essays Biochem. 2018 Oct 26;62(4):549-561. doi: 10.1042/EBC20180005.
Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus-response and perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial-mesenchymal transition from the E2F1 molecular interaction map.In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.
由于患者之间存在遗传异质性，因此确定有效的疾病特征和治疗靶点具有挑战性。为了解决这一挑战，我们之前开发了一种基于网络的方法，该方法整合了生物信息的异构源，以识别特定于疾病的核心调节网络。具体来说，我们的工作流程使用多目标优化函数根据网络特性、生物医学和高通量表达数据为网络组件（例如反馈/前馈回路）计算排名分数。排名较高的网络组件被合并以识别核心调节网络，然后使用刺激-响应和扰动实验对其进行动力学分析，以识别疾病基因特征和治疗靶点。在案例研究中，我们从 E2F1 分子相互作用图实施了我们的工作流程，以识别膀胱癌和乳腺癌上皮-间充质转化的特定核心调节网络。在本研究中，我们回顾了我们的工作流程，并描述了它随着时间的推移如何发展，以了解疾病进展的机制和预测临床决策的特征。
