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  • ACY-1215 The mitogen activated protein kinase MAPK pathway


    The mitogen-activated protein kinase (MAPK) pathway is a highly conserved module that mediates the transduction of signals from the cell surface to the nucleus (M. Köbel et al., 2005), meanwhile, it is known to be activated by a wide array of signals ranging from growth and differentiation factors to stress signals perceived by different types of receptors (Schaeffer and Weber, 1999). Among the MAPK kinase cascades, ERK is the first recognized and the central member of the MAPKs family and is primarily activated by various factors including platelet derived growth factor, thromboxane A2, angiotensin, transforming growth factor, insulin, osmotic stress, adhesive reaction and so on. (G. Pearson et al., 2001). In the ERK transduction networks, the epidermal growth factor (EGF)-activated signal transduction is always a center of attention, not only at the single-molecule level, but also at the systems biology level (G. Pearson et al., 2001). The complexity of the signal network severely increases the difficulty to uncover the network mechanisms only by means of “wet experiments”. In order to fill this gap, computational models and methods have become a new focus of research in just recent years on various signal transductions and biological pathways (X. Wang et al., 2011; Y. Wang et al., 2007; C. Widmann et al., 1999). The first mathematical model of ERK network was published by Huang and Ferrell in 1996 (Huang and Ferrell, 1996), suggesting that the ERK cascade exhibited an ultra-sensitivity with a non-linear sigmoid activation curve. Subsequently, many typical models of ERK network were developed by Kholodenko etc. in 1999 (B.N. Kholodenko et al., 1999), Brightman etc. in 2000 (Brightman and Fell, 2000b), Schoebert etc in 2002 (B. Schoeberl et al., 2002) and Orton etc. in 2005 (R.J. Orton et al., 2005). These mathematical models mainly focused on investigating the properties and behaviors of the ERK core signaling cascade, and the possible effects regulated by other cellular components such as miRNAs (were not yet fully elucidated). As miRNAs are able to knock down protein ACY-1215 specifically, the hypothesis that several miRNAs play important roles in the regulation of the strength and specificity of MAPK signaling network through directly controlling the concentration of network components at post-transcriptional and translational levels has been well demonstrated (Q. Cui et al., 2006). Among them, miR-21 might be one of the most interesting examples that participate in this pathway. It is suggested that miR-21 acts as a positive feedback regulator for the MAPK-ERK signaling pathway, because miR-21 can be induced by the activation of ERK1 ⁄ 2 and can reversely stimulate ERK1⁄2 (T. Thum et al., 2008). Kefas et al. (B. Kefas et al., 2008) showed that miR-7 is another potential tumor suppressor in glioblastoma, which potently inhibits EGFR expression. In lung tumor tissues, reducing level of let-7 significantly increases the level of Ras protein relative to normal lung tissue, suggesting that let-7 can negatively regulate the expression of Ras (S.M. Johnson et al., 2005), while miR-34a inhibits cell proliferation by repressing MEK during megakaryocytic differentiation of K562 (A. Ichimura et al., 2010). Traditionally, the computational modeling and simulation of biochemical reactions usually rely on the deterministic methods to describe the average behavior of the populations with a set of ordinary differential equations (ODE). For example, in our previous work, we have investigated the dynamic mechanisms of the miRNA biogenesis process and miRNA-mediated circadian clock system (W. Zhou et al., 2011). Although deterministic simulation approach can predict the average network behavior, it fails to capture certain emergent phenomena which is inherently random. Thereby the same biological systems can be alternatively modeled in a stochastic way, using concentrations of discrete numbers to take a mesoscopic view of the system and get the possibility after running the model repeatedly. Moreover, it is still unclear how miRNAs might orchestrate their regulation of ERK signaling network and how this regulation contributes to the biological functions of miRNAs. Therefore, we aim to study the Ras/Raf/MEK/ERK signaling network and have addressed these questions by analyzing the precise mechanisms with a bevy of miRNAs involved in the pathway.