Attractor landscape analysis of the cardiac signaling network reveals mechanism-based therapeutic strategies for heart failure

cardiomyocytes 的 Apoptosis 和肥大是心失败(HF ) 的主要原因，死亡的一个全球带原因，并且通过复杂细胞内部的发信号网络被调整，由于它的复杂性限制有效处理的开发。为了在一个系统为 HF 识别有效治疗学的策略，铺平，我们由集成所有可得到的试验性的证据开发心脏的发信号网络的一个大规模全面数学模型。网络模型的引起注意的人风景分析识别有效地在 ischemic 下面压制 cardiomyocytes 的 apoptosis 和肥大或迫使导致超载的 HF 的控制节点的不同集合， HF 的二种主要类型。有趣地，我们的系统级的分析建议这些控制节点的干预可以为 HF 增加临床的药的功效并且，大多数重要性，控制节点的不同联合作为取决于 HF 的类型的潜在地有效的候选人药目标被建议。我们的学习提供为 HF 开发基于机制的治疗学的策略的一个系统的方法。

Apoptosis and hypertrophy of cardiomyocytes are the primary causes of heart failure （HF）, a global leading cause of death, and are regulated through the complicated intracellular signaling network, limiting the development of effective treatments due to its complexity. To identify effective therapeutic strategies for HF at a system level, we develop a large-scale comprehensive mathematical model of the cardiac signaling network by integrating all available experimental evidence. Attractor landscape analysis of the network model identifies distinct sets of control nodes that effectively suppress apoptosis and hypertrophy of cardiomyocytes under ischemic or pressure overload-induced HF, the two major types of HF. Intriguingly, our system-level analysis suggests that intervention of these control nodes may increase the efficacy of clinical drugs for HF and, of most importance, different combinations of control nodes are suggested as potentially effective candidate drug targets depending on the types of HF. Our study provides a systematic way of developing mechanism-based therapeutic strategies for HF.