Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.
Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.
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DOI：
10.1371/journal.pcbi.1000340
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发表时间：
2009-04
影响因子：
4.3
通讯作者：
Lauffenburger DA
中科院分区：
文献类型：
--
作者：
Aldridge BB;Saez-Rodriguez J;Muhlich JL;Sorger PK;Lauffenburger DA
在对细胞信号网络建模时，必须在机制细节和易于解释之间取得平衡。在本文中，我们应用模糊逻辑框架来分析一个大型的，系统的数据集描述的动态细胞信号转导下游的TNF，EGF和胰岛素受体在人结肠癌细胞。基于模糊逻辑的模拟概括了数据的大部分特征，并生成了涉及通路串扰和调节的几个预测。我们发现了MK2和ERK通路之间的关系，这可能是MK2先前确定的促生存影响的原因。我们还发现EGF治疗后IKK的意外抑制，可能是由于自分泌信号的下调。更一般地说，模糊逻辑模型是灵活的，能够结合定性和噪声数据，并强大到足以产生定量预测和新的生物学见解的信号网络的运作。细胞使用相互作用的蛋白质网络来解释细胞内状态和细胞外信号，并执行细胞命运决定。即使在分子水平上很好地理解了单个蛋白质，但网络作为一个整体的动力学和行为却很难理解。然而，破译这种网络的运作是理解疾病过程和治疗机会的关键。作为一种手段来研究信号网络，我们已经修改和应用模糊逻辑方法最初开发的工业控制。我们使用模糊逻辑模型的结肠癌细胞培养的促生存和促死亡的细胞因子的组合的反应，使其能够解释定量数据的背景下，从文献中提取的抽象信息。我们的工作表明，模糊逻辑可以用来理解复杂的信号通路方面的多因子活性为基础的蛋白质数据和先验知识。
When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks. Cells use networks of interacting proteins to interpret intra-cellular state and extra-cellular cues and to execute cell-fate decisions. Even when individual proteins are well understood at a molecular level, the dynamics and behavior of networks as a whole are harder to understand. However, deciphering the operation of such networks is key to understanding disease processes and therapeutic opportunities. As a means to study signaling networks, we have modified and applied a fuzzy logic approach originally developed for industrial control. We use fuzzy logic to model the responses of colon cancer cells in culture to combinations of pro-survival and pro-death cytokines, making it possible to interpret quantitative data in the context of abstract information drawn from the literature. Our work establishes that fuzzy logic can be used to understand complex signaling pathways with respect to multi-factorial activity-based protein data and prior knowledge.
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DOI：
10.1126/stke.3442006re6
发表时间：
2006-07-18
期刊：
Science's STKE : signal transduction knowledge environment
影响因子：
--
作者：
Hlavacek, William S;Faeder, James R;Fontana, Walter
通讯作者：
Fontana, Walter
影响因子：
1.7
作者：
Gat-Viks, I;Tanay, A;Shamir, R
通讯作者：
Shamir, R
影响因子：
2
作者：
GLASS, L;KAUFFMAN, SA
通讯作者：
KAUFFMAN, SA
影响因子：
5.8
作者：
Bulashevska, S;Eils, R
通讯作者：
Eils, R
影响因子：
32.4
作者：
Kelliher, MA;Grimm, S;Leder, P
通讯作者：
Leder, P