The rapid accumulation of omics data from biological specimens has revolutionized the field of cancer research. The generation of computational techniques attempting to study these masses of data and extract the significant signals is at the forefront.

We suggest studying cancer from a thermodynamic-based point of view. We hypothesize that by modelling biological systems based on physico-chemical laws, highly complex systems can be reduced to a few parameters, and their behavior under varying conditions, including response to therapy, can be predicted.

Here we validate the predictive power of our thermodynamic-based approach, by uncovering the protein network structure that emerges in MCF10a human mammary cells upon exposure to epidermal growth factor (EGF), and anticipating the consequences of treating the cells with the Src family kinase inhibitor, dasatinib.

来自生物样本的组学数据的快速积累彻底改变了癌症研究领域。试图研究这些数据量并提取重要信号的计算技术已走在最前列。

我们建议从基于热力学的角度研究癌症。我们假设通过基于物理化学定律对生物系统进行建模，可以将高度复杂的系统简化为几个参数，并且可以预测它们在各种条件下的行为，包括对治疗的反应。

在这里，我们通过揭示暴露于表皮生长因子（EGF）的MCF10a人乳腺细胞中出现的蛋白质网络结构，并预测用Src家族激酶抑制剂处理细胞的后果，验证了基于热力学方法的预测能力，达沙替尼。