Investigation of stochasticity in TRAIL signaling cancer model
Investigation of stochasticity in TRAIL signaling cancer model
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TRAIL 信号癌症模型的随机性研究
DOI：
10.1109/iccme.2012.6275648
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
& Selvarajoo K
中科院分区：
文献类型：
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作者：
Piras v;Hayashi K;Tomita M;& Selvarajoo K
在癌症中，细胞凋亡或程序性细胞死亡已经通过肿瘤坏死因子相关凋亡诱导配体（TRAIL）信号转导被证明。因此，基于TRAIL的疗法已被广泛研究以对抗癌症。然而，几种恶性癌症类型仍然对TRAIL具有抗性。最近，我们开发了一个动态计算模型来研究TRAIL刺激的人纤维肉瘤（HT 1080）细胞的耐药机制。基于质量作用定律和信号通量守恒的宏观平均细胞反应模型成功模拟了野生型和几种突变体（FADD、RIP 1和TRAF 2敲低或KD）中细胞存活（IκB、JNK、p38）和凋亡（caspase-8和-3）分子的半定量时间曲线。然而，已知癌症群体是高度异质性的，并且各种研究已经证明了随机性和变异性对于相同细胞之间的表型多样性的重要性。在这里，我们扩展了我们原来的模型，通过采用信号反应的概率，通过吉莱斯皮算法来研究这种波动对TRAIL信号反应的影响。值得注意的是，当我们刺激模型1000次以指示在具有不同随机性水平的所有4种实验条件下1000个单细胞应答的可变性时，我们注意到TRAF 2 KD产生最可变的信号传导应答。这种差异随后影响了通过细胞存活度量（CSM）分析的细胞凋亡水平。我们的工作突出了理解细胞信号反应对不同水平随机性的可变反应的必要性。因此，在实际开发杀死癌细胞的潜在药物靶标之前，可以通过动态模型研究随机方差的影响。
In cancer, apoptosis or programmed cell death has been demonstrated through the tumor necrosis factor related apoptosis-inducing ligand (TRAIL) signal transduction. As a result, TRAIL-based therapies have been widely investigated to fight cancers. However, several malignant cancer types still remain resistant to TRAIL. Recently, we developed a dynamic computational model to investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells. The macroscopic average-cell response model, based on the law of mass action and signaling flux conservation, successfully simulates the semi-quantitative temporal profiles of cell survival (IκB, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several mutants (FADD, RIP1 and TRAF2 knockdowns or KD). However, cancer populations are known to be highly heterogeneous, and various studies have demonstrated the importance of stochasticity and variability for phenotypic diversity between identical cells. Here, we extend our original model to investigate the effect of such fluctuations on TRAIL signaling response by adopting probabilities of signaling reactions through the Gillespie algorithm. Notably, when we stimulated the model 1000 times to indicate the variability of 1000 single cell responses in all 4 experimental conditions with different levels of stochasticity, we notice that TRAF2 KD produced the most variable signaling response. This variance subsequently affected the level of cellular apoptosis analysed through the cell-survival metric (CSM). Our work highlights the necessity to understand variable responses of cell signaling reactions to different levels of stochasticity. Thus, prior to the actual development of potential drug targets for killing cancer cells, the effect of stochastic variance could be investigated through dynamic models.