Investigating the Role of cAMP-Signaling in the Regulation of Vascular Endothelial Cell Adaptive Responses to Fluid Shear Stress

The circulating blood exerts a force on the vascular endothelium, termed fluid shear stress (FSS), which directly impacts numerous vascular endothelial cell (VEC) functions. For example, high rates of linear and undisturbed (i.e. laminar) blood flow maintains a protective and quiescent VEC phenotype. Meanwhile, deviations in blood flow, which can occur at vascular branchpoints and large curvatures, create areas of low, and/or oscillatory FSS, and promote a pro-inflammatory, pro-thrombotic and hyperpermeable phenotype. Indeed, it is known that these areas are prone to the development of atherosclerotic lesions. Herein, we show that cyclic nucleotide phosphodiesterase (PDE) 4D (PDE4D) activity is increased by FSS in human arterial endothelial cells (HAECs) and that this activation regulates the activity of cAMP-effector protein, Exchange Protein-activated by cAMP-1 (EPAC1), in these cells. Importantly, we also show that these events directly and critically impact HAEC responses to FSS, especially when FSS levels are low. Both morphological events induced by FSS, as measured by changes in cell alignment and elongation in the direction of FSS, and the expression of critical FSS-regulated genes, including Krüppel-like factor 2 (KLF2), endothelial nitric oxide synthase (eNOS) and thrombomodlin (TM), are mediated by EPAC1/PDE4D signaling. At a mechanistic level, we show that EPAC1/PDE4D acts through the vascular endothelial-cadherin (VECAD)/ platelet-cell adhesion molecule-1 (PECAM1)/vascular endothelial growth factor receptor 2 (VEGFR2) mechanosensor to activate downstream signaling though Akt. Given the critical role of PDE4D in mediating these effects, we also investigated the impact of various patterns of FSS on the expression of individual PDE genes in HAECs. Notably, PDE2A was significantly upregulated in response to high, laminar FSS, while PDE3A was upregulated under low, oscillatory FSS conditions only. These data may provide novel therapeutic targets to limit FSS-dependent endothelial cell dysfunction (ECD) and atherosclerotic development.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.