Network-wide traffic monitoring is of interest to network operators. With constantly changing traffic characteristics and measurement objectives, existing techniques for traffic monitoring tend to be sub-optimal due to poor choice of monitor deployment locations. Routing-assisted network monitoring mechanisms have successfully catered to these needs and are able to maximize the overall traffic monitoring utility of the network by strategically re-directing selected traffic sub -populations over existing deployed monitoring devices. Both the traffic measurement gain of the network and the load-balancing of measurement workloads across distributed monitoring devices are important performance metrics in the design of efficient routing-assisted traffic monitoring mechanisms. This thesis focuses on the design of gain driven routing-assisted monitoring mechanisms where maximizing the overall traffic measurement gain is our primary design objective. This problem is tackled using two different approaches. First, novel centralized optimal and heuristic routing solutions are proposed for jointly optimizing monitor placement and dynamic routing strategy to achieve maximum measurement gain of the network. Next, we consider the load-balancing problem about how to distribute the network measurement workload across monitoring devices without compromising on the overall traffic measurement gain of the network. Providing effective load-balancing is important since previously- placed monitoring devices may be easily overwhelmed with ever-increasing link rates and increasingly sophisticated measurement tasks. We present an optimization framework called LEISURE (Load- EqualIzed meaSUREment) for load- balancing network measurement workloads across distributed monitors. Finally, a distributed measurement-aware traffic engineering protocol is proposed based on a game-theoretic re-routing policy that attempts to optimally utilize existing monitor locations for maximizing the traffic measurement gain of the network while ensuring that the traffic load distribution across the network satisfies some traffic engineering constraint. It guarantees not only a provable Nash equilibrium, but also a quick convergence without significant oscillations to an equilibrium state in which the measurement utility of the network is close to the maximum achievable gain using offline, centralized routing-assisted network monitoring mechanisms. Both these centralized and distributed routing -assisted approaches improve the overall traffic measurement utility of the network significantly while ensuring low computation complexity

Network-wide traffic measurement is of interest to network operators to uncover global network behavior for the management tasks of traffic accounting, debugging or troubleshooting, security, and traffic engineering. Increasingly, sophisticated network measurement tasks such as anomaly detection and security forensic analysis are requiring in-depth fine-grained flow-level measurements. However, performing in-depth per-flow measurements (e.g., detailed payload analysis) is often an expensive process. Given the fast-changing Internet traffic landscape and large traffic volume, a single monitor is not capable of accomplishing the measurement tasks for all applications of interest due to its resource constraint. Moreover, uncovering global network behavior requires network-wide traffic measurements at multiple monitors across the network since traffic measured at any single monitor only provides a partial view and may not be sufficient or accurate. These factors call for coordinated measurements among multiple distributed monitors. In this paper, we present a centralized optimization framework, LEISURE (Load- EqualIzed mea SUREment), for load-balancing network measurement workloads across distributed monitors. Specifically, we consider various load-balancing problems under different objectives and study their extensions to support both fixed and flexible monitor deployment scenarios. We formulate the latter flexible monitor deployment case as an MILP (Mixed Integer Linear Programming) problem and propose several heuristic algorithms to approximate the optimal solution and reduce the computation complexity. We evaluate LEISURE via detailed simulations on Abilene and GEANT network traces to show that LEISURE can achieve much better load-balanced performance (e.g., 4.75 × smaller peak workload and 70 × smaller variance in workloads) across all coordinated monitors in comparison to a naive solution (uniform assignment) to accomplish network-wide traffic measurement tasks under the fixed monitor deployment scenario. We also show that under the flexible monitor deployment setting, our heuristic solutions can achieve almost the same load-balancing performance as the optimal solution while reducing the computation times by a factor up to 22.5 × in Abilene and 800 × in GEANT.

Extracellular matrix (ECM) has emerged as a dynamic tissue component, providing not only structural support, but also functionally participating in a wide range of signaling events during development, injury, and disease remodeling. Investigation of dynamic changes in cardiac ECM proteome is challenging due to the relative insolubility of ECM proteins, which results from their macromolecular nature, extensive post-translational modification (PTM), and tendency to form protein complexes. Finally, the relative abundance of cellular and mitochondrial proteins in cardiac tissue further complicates cardiac ECM proteomic approaches. Recent developments of various techniques to enrich and analyze ECM proteins are playing a major role in overcoming these challenges. Application of cardiac ECM proteomics in disease tissues can further provide spatial and temporal information relevant to disease diagnosis, prognosis, treatment, and engineering of therapeutic candidates for cardiac repair and regeneration.

Rationale

Both β-adrenergic receptor (β-AR) and Gq-coupled receptor (GqR) agonist-driven signaling play key roles in the events, leading up to and during cardiac dysfunction. How these stimuli interact at the level of protein kinase D (PKD), a nodal point in cardiac hypertrophic signaling, remains unclear.

Objective

To assess the spatiotemporal dynamics of PKD activation in response to β-AR signaling alone and on coactivation with GqR-agonists. This will test our hypothesis that compartmentalized PKD signaling reconciles disparate findings of PKA facilitation and inhibition of PKD activation.

Methods and results

We report on the spatial and temporal profiles of PKD activation using green fluorescent protein-tagged PKD (wildtype or mutant S427E) and targeted fluorescence resonance energy transfer-based biosensors (D-kinase activity reporters) in adult cardiomyocytes. We find that β-AR/PKA signaling drives local nuclear activation of PKD, without preceding sarcolemmal translocation. We also discover pronounced interference of β-AR/cAMP/PKA signaling on GqR-induced translocation and activation of PKD throughout the cardiomyocyte. We attribute these effects to direct, PKA-dependent phosphorylation of PKD-S427. We also show that phosphomimetic substitution of S427 likewise impedes GqR-induced PKD translocation and activation. In neonatal myocytes, S427E inhibits GqR-evoked cell growth and expression of hypertrophic markers. Finally, we show altered S427 phosphorylation in transverse aortic constriction-induced hypertrophy.

Conclusions

β-AR signaling triggers local nuclear signaling and inhibits GqR-mediated PKD activation by preventing its intracellular translocation. PKA-dependent phosphorylation of PKD-S427 fine-tunes the PKD responsiveness to GqR-agonists, serving as a key integration point for β-adrenergic and Gq-coupled stimuli.