The mitogen-activated protein kinase (MAPK) (also called extracellular signal-regulated kinase [ERK]) pathway has been implicated in malignant transformation and in the regulation of cellular growth and proliferation of several tumor types, but its expression and function in Hodgkin disease (HD) are unknown. We report here that the active phosphorylated form of MAPK/ERK is aberrantly expressed in cultured and primary HD cells. Inhibition of the upstream MAPK kinase (also called MEK) by the small molecule UO126 inhibited the phosphorylation of ERK and demonstrated a dose-and time-dependent antiproliferative activity in HD cell lines. UO126 modulated the levels of several intracellular proteins including B-cell lymphoma protein 2 (Bcl-2), myeloid cell leukemia-1 (Mcl-1) and caspase 8 ho-molog FLICE-inhibitory protein (cFLIP), and induced G 2 M cell-cycle arrest or apo-ptosis. Furthermore, UO126 potentiated the activity of apoliprotein 2/tumor necro-sis factor-related apoptosis-inducing li-gand (APO2L/TRAIL) and chemotherapy-induced cell death. Activation of CD30, CD40, and receptor activator of nuclear kappa (RANK) receptors in HD cells by their respective ligands increased ERK phosphorylation above the basal level and promoted HD cell survival. UO126 inhibited basal and ligand-induced ERK phosphorylation, and inhibited ligand-induced cell survival of HD cell lines. These findings provide a proof-of-principle that inhibition of the MEK/ERK pathway may have therapeutic value in HD.

A method is developed for model predictive control of nonlinear stochastic partial differential equations (PDEs) to regulate the state variance, which physically represents the roughness of a surface in a thin film growth process, to a desired level. Initially a nonlinear stochastic PDE is formulated into a system of infinite nonlinear stochastic ordinary differential equations by using Galerkin's method. A finite‐dimensional approximation is then derived that captures the dominant mode contribution to the state variance. A model predictive control problem is formulated, based on the finite‐dimensional approximation, so that the future state variance can be predicted in a computationally efficient way. To demonstrate the method, the model predictive controller is applied to the stochastic Kuramoto‐Sivashinsky equation, and the kinetic Monte Carlo model of a sputtering process to regulate the surface roughness at a desired level. © 2008 American Institute of Chemical Engineers AIChE J, 2008

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