As our ability to provide in-depth, patient-specific characterisation of the molecular alterations within tumours rapidly improves, it is becoming apparent that new approaches will be required to leverage the power of this data and derive the full benefit for each individual patient. Systems biology approaches are beginning to emerge within this field as a potential method of incorporating large volumes of network level data and distilling a coherent, clinically-relevant prediction of drug response. However, the initial promise of this developing field is yet to be realised. Here we argue that in order to develop these precise models of individual drug response and tailor treatment accordingly, we will need to develop mathematical models capable of capturing both the dynamic nature of drug-response signalling networks and key patient-specific information such as mutation status or expression profiles. We also review the modelling approaches commonly utilised within this field, and outline recent examples of their use in furthering the application of systems biology for a precision medicine approach to cancer treatment.

中文翻译：

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个性化信令网络模型在精密肿瘤学中的应用。

随着我们对肿瘤内分子变化的深入，特定于患者的特征的能力迅速提高，越来越明显的是，将需要新的方法来利用此数据的力量并为每位患者带来全部收益。系统生物学方法作为整合大量网络级数据并提取出连贯，与临床相关的药物反应预测的潜在方法，在该领域开始出现。但是，这一发展领域的最初希望尚未实现。在这里，我们认为，为了开发这些精确的个体药物反应模型并相应地调整治疗，我们将需要开发能够捕获药物反应信号网络的动态性质和关键患者特定信息（如突变状态或表达谱）的数学模型。我们还回顾了该领域中常用的建模方法，并概述了它们在促进系统生物学在癌症治疗的精确医学方法中的应用的最新实例。