Tortolina Lorenzo, Duffy David J, Maffei Massimo, Castagnino Nicoletta, Carmody Aimée M, Kolch Walter, Kholodenko Boris N, De Ambrosi Cristina, Barla Annalisa, Biganzoli Elia M, Nencioni Alessio, Patrone Franco, Ballestrero Alberto, Zoppoli Gabriele, Verri Alessandro, Parodi Silvio
Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy.
Systems Biology Ireland, Conway Institute, University College Dublin, Belfield, Dublin, Ireland.
Oncotarget. 2015 Mar 10;6(7):5041-58. doi: 10.18632/oncotarget.3238.
The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.
转化生长因子β(TGFβ)、WNT和表皮生长因子(EGF)家族受体配体下游的相互连接的信号通路网络在结直肠癌发病机制中起重要作用。我们研究并实施了多个下游通路的动态模拟,并描述了被视为分子相互作用图谱(MIM)的信号网络部分。我们的模拟使用了常微分方程(ODE),其中涉及447种反应物及其相互作用。从初始的“生理状态”开始,该模型可以进行调整,以模拟通过相关癌蛋白的改变/突变实现的个体病理癌症状态。我们使用突变的结直肠癌系HCT116和HT29验证了一些突出的模型预测。我们测量了MYC和CCND1 mRNA以及AKT和ERK磷酸化蛋白的量,以响应个体或联合癌蛋白抑制剂治疗。实验结果与模拟结果相关性良好。我们的模型还预测了最近独立发表的结果。即使存在近似且不完整的信号网络信息,预测性动态建模似乎已经可行。一条重要的长期道路似乎已经开启,可以通过逐步推进,朝着更大且参数化更好的MIMs进一步探索。将信号蛋白抑制剂进行合理组合的个性化治疗策略可能会成为一个现实的目标。