The studies in this thesis are mainly focused on the effects that the chaperonin mechanisms have on a bound target protein. Earlier studies have shown that the bacterial chaperonin GroEL plays an active role in unfolding a target protein during the initial binding. Here, the effects of the eukaryotic chaperonin TRiC’s mechanical action on a bound target protein were studied by fluorescence resonance energy transfer (FRET) measurements by attaching the fluorophore fluorescein to specific positions in the structure of the target protein, β-actin. Actin is an abundant eukaryotic protein and is dependent on TRiC to reach its native state. It was found that at the initial binding to TRiC, the actin structure is stretched, particularly across the nucleotide-binding site. This finding led to the conclusion that the binding-induced unfolding mechanism is conserved through evolution. Further studies indicated that in a subsequent step of the chaperonin cycle, the actin molecule collapses. This collapse leads to rearrangements of the structure at the nucleotide-binding cleft, which is also narrowed as a consequence. As a comparison to the productive folding of actin in the TRiC chaperonin system, FRET studies were also performed on actin interacting with GroEL. This is a non-productive interaction in terms of guiding actin to its native state. The study presents data indicating that the nucleotide-binding cleft in actin is not rearranged by GroEL in the same way as it is rearranged during the TRiC interaction. Thus, it could be concluded that although the general unfolding mechanism is conserved through the evolution of the chaperonins, an additional and specific binding to distinct parts of the actin molecule has evolved in TRiC. This specific binding leads to a directed unfolding and rearrangement of the nucleotide-binding cleft, which is vital for actin to reach its native state. The differences in the chemical properties of the actin-GroEL and the actin-TRiC complexes were also determined by measurements of fluorescein anisotropies and AEDANS emission shifts for probes attached to positions spread throughout the actin structure. The evolutionary aspects of the chaperonin mechanisms and the target protein binding were further investigated in another study. In this study, the prokaryotic homologue to actin, MreB, was shown to bind to both TRiC and GroEL. MreB was also shown to bind to the co-chaperonin GroES. In a separate study, the interaction between actin and the chaperone prefoldin was investigated. In vivo prefoldin interacts with non-native actin and transfers it to TRiC for subsequent and proper folding. In this homo-FRET study, it was shown that actin binds to prefoldin in a stretched conformation, similar to the initial binding of actin to TRiC. / On the day of the defence date the satus of article I was: In press.

The Bound Chronicles is a fictional story that chronicles the journey of three Irish monks who travel to Britain in 892 AD, the time of the Anglo-Saxons. There, they encounter King Alfred, Vikings, poisonings, but, more harrowing, must face their inner selves and the consequences of their choices.

In this dissertation, we examine specific network connectivity problems, and achieve improved approximation algorithm and integrality gap results for them. We introduce an important new, highly useful and applicable, network connectivity problem - the Vital Core Connectivity Problem (VCC). Despite its many practical uses, this problem has not been previously studied. We present the first constant factor approximation algorithm for VCC, and provide an upper bound on the integrality gap of its linear programming relaxation. We also introduce a new, useful, extension of the minimum spanning tree problem, called the Extended Minimum Spanning Tree Problem (EMST), that is based on a special case of VCC; and provide both a polynomial-time algorithm and a complete linear description for it. Furthermore, we show how to generalize this new problem to handle numerous disjoint vital cores, providing the first complete linear description of, and polynomial-time algorithm for, the generalized problem. We examine the Survivable Network Design Problem (SNDP) with multiple copies of edges allowed in the solution (multi-SNDP), and present a new approximation algorithm for which the approximation guarantee is better than that of the current best known for certain cases of multi-SNDP. With our method, we also obtain improved bounds on the integrality gap of the linear programming relaxation of the problem. Furthermore, we show the application of these results to variations of SNDP. We investigate cases where the optimal values of multi-SNDP and SNDP are equal; and we present an improvement on the previously best known integrality gap bound and approximation guarantee for the special case of SNDP with metric costs and low vertex connectivity requirements, as well as for the similar special case of the Vertex Connected Survivable Network Design Problem (VC-SNDP). The quality of the results that one can obtain for a given network design problem often depends on its integer linear programming formulation, and, in particular, on its linear programming relaxation. In this connection, we investigate formulations for the Steiner Tree Problem (ST). We propose two new formulations for ST, and investigate their strength in terms of their associated integrality gaps.

Majority of the studies in the scheduling literature is devoted to time based performance measures. In this thesis, we develop a model that considers monetary issues in single machine scheduling environments. We assume all the jobs should be completed by a common due date. An early revenue is earned if the completion time is before or on the due date, and a tardy revenue is gained if the job is completed after the due date. We consider restricted and unrestricted due date versions of the problem. Our objective is the maximization of the net present value of all revenues. We first investigate some special cases of the problem, and present polynomial time algorithms to solve them. Then, we develop branch and bound algorithms with lower and upper bounding mechanisms. Computational experiments have shown that the branch and bound algorithms can solve large-sized problems in reasonable times.

In this thesis work, we have mainly worked on two topics of GPU performance analysis. First, we have developed an analytical method and a timing estimation tool (TEG) to predict CUDA application's performance for GT200 generation GPUs. TEG can predict GPU applications' performance in cycle-approximate level. Second, we have developed an approach to estimate GPU applications' performance upper bound based on application analysis and assembly code level benchmarking. With the performance upper bound of an application, we know how much optimization space is left and can decide the optimization effort. Also with the analysis we can understand which parameters are critical to the performance.

The problems we study in this thesis arise in computer science, extremal set theory and quantum computing. The first common feature of these problems is that each can be reduced to characterizing the independent sets of maximum size in a suitable graph. A second common feature is that the size of these independent sets meets an eigenvalue bound due to Delsarte and Hoffman. Thirdly, the graphs that arise belong to association schemes that have already been studied in other contexts. Our first problem involves covering arrays on graphs, which arises in computer science. The goal is to find a smallest covering array on a given graph <i>G</i>. It is known that this is equivalent to determining whether <i>G</i> has a homomorphism into a <i>covering array graph</i>, <i>CAG(n,g)</i>. Thus our question: Are covering array graphs cores? A covering array graph has as vertex set the partitions of <i>{1,. . . ,n}</i> into <i>g</i> cells each of size at least <i>g</i>, with two vertices being adjacent if their meet has size <i>g²</i>. We determine that <i>CAG(9,3)</i> is a core. We also determine some partial results on the family of graphs <i>CAG(g²,g)</i>. The key to our method is characterizing the independent sets that meet the Delsarte-Hoffman bound---we call these sets <i>ratio-tight</i>. It turns out that <i>CAG(9,3)</i> sits inside an association scheme, which will be useful but apparently not essential. We then turn our attention to our next problem: the Erdos-Ko-Rado theorem and its <i>q</i>-analogue. We are motivated by a desire to find a unifying proof that will cover both versions. The EKR theorem gives the maximum number of pairwise disjoint <i>k</i>-sets of a fixed <i>v</i>-set, and characterizes the extremal cases. Its <i>q</i>-analogue does the same for <i>k</i>-dimensional subspaces of a fixed <i>v</i>-dimensional space over <i>GF(q)</i>. We find that the methods we developed for covering array graphs apply to the EKR theorem. Moreover, unlike most other proofs of EKR, our argument applies equally well to the <i>q</i>-analogue. We provide a proof of the characterization of the extremal cases for the <i>q</i>-analogue when <i>v=2k</i>; no such proof has appeared before. Again, the graphs we consider sit inside of well-known association schemes; this time the schemes play a more central role. Finally, we deal with the problem in quantum computing. There are tasks that can be performed using quantum entanglement yet apparently are beyond the reach of methods using classical physics only. One particular task can be solved classically if and only if the graph &Omega;(<i>n</i>) has chromatic number <i>n</i>. The graph &Omega;(<i>n</i>) has as vertex set the set of all <i>± 1</i> vectors of length <i>n</i>, with two vertices adjacent if they are orthogonal. We find that <i>n</i> is a trivial upper bound on the chromatic number, and that this bound holds with equality if and only if the Delsarte-Hoffman bound on independent sets does too. We are thus led to characterize the ratio-tight independent sets. We are then able to leverage our result using a recursive argument to show that <i>&chi;</i>(&Omega;(<i>n</i>)) > <i>n</i> for all <i>n</i> > 8. It is notable that the reduction to independent sets, the characterization of ratio-tight sets, and the recursive argument all follow from different proofs of the Delsarte-Hoffman bound. Furthermore, &Omega;(<i>n</i>) also sits inside a well-known association scheme, which again plays a central role in our approach.

Digital communication systems invariably employ an underlying analog communication channel. At the transmitter, data is modulated to obtain an analog waveform which is input to the channel. At the receiver, the output of the channel needs to be mapped back into the discrete domain. To this effect, the continuous-time received waveform is sampled at instants chosen by the timing recovery block. Therefore, timing recovery is an essential component of digital communication systems. A widely used timing recovery method is based on a phase-locked loop (PLL), which updates its timing estimates based on a decision-directed device. Timing recovery performance is a strong function of the reliability of decisions, and hence, of the channel signal-to-noise ratio (SNR). Iteratively decodable error-control codes (ECCs) like turbo codes and LDPC codes allow operation at SNRs lower than ever before, thus exacerbating timing recovery. We propose iterative timing recovery, where the timing recovery block, the equalizer and the ECC decoder exchange information, giving the timing recovery block access to decisions that are much more reliable than the instantaneous ones. This provides significant SNR gains at a marginal complexity penalty over a conventional turbo equalizer where the equalizer and the ECC decoder exchange information. We also derive the Cramer-Rao bound, which is a lower bound on the estimation error variance of any timing estimator, and propose timing recovery methods that outperform the conventional PLL and achieve the Cramer-Rao bound in some cases. At low SNR, timing recovery suffers from cycle slips, where the receiver drops or adds one or more symbols, and consequently, almost always the ECC decoder fails to decode. Iterative timing recovery has the ability to corrects cycle slips. To reduce the number of iterations, we propose cycle slip detection and correction methods. With iterative timing recovery, the PLL with cycle slip detection and correction recovers most of the SNR loss of the conventional receiver that separates timing recovery and turbo equalization.

Monotonic optimization consists of minimizing or maximizing a monotonic objective function over a set of constraints defined by monotonic functions. Many optimization problems in economics and engineering often have monotonicity while lacking other useful properties, such as convexity. This thesis is concerned with the development and application of global optimization algorithms for monotonic optimization problems. First, we propose enhancements to an existing outer-approximation algorithm | called the Polyblock Algorithm | for monotonic optimization problems. The enhancements are shown to significantly improve the computational performance of the algorithm while retaining the convergence properties. Next, we develop a generic branch-and-bound algorithm for monotonic optimization problems. A computational study is carried out for comparing the performance of the Polyblock Algorithm and variants of the proposed branch-and-bound scheme on a family of separable polynomial programming problems. Finally, we study an important class of monotonic optimization problems | probabilistically constrained linear programs. We develop a branch-and-bound algorithm that searches for a global solution to the problem. The basic algorithm is enhanced by domain reduction and cutting plane strategies to reduce the size of the partitions and hence tighten bounds. The proposed branch-reduce-cut algorithm exploits the monotonicity properties inherent in the problem, and requires the solution of only linear programming subproblems. We provide convergence proofs for the algorithm. Some illustrative numerical results involving problems with discrete distributions are presented.

The time/cost trade-off models in project management aim to compress the project completion time by accelerating the activity durations at an expense of additional resources. The budget problem in discrete time/cost trade-off scheduling selects the time/cost mode -among the discrete set of specified modes- for each activity so as to minimize the project completion time without exceeding the available budget. There may be alternative modes that solve the budget problem optimally, however each solution may have a different total cost value. In this study we aim to find the minimum cost solution among the optimal solutions of the budget problem. We analyze the structure of the problem together with its linear programming relaxation and derive some mechanisms for reducing the problem size. We solve the reduced problem by linear programming relaxation and branch and bound based approximation and optimization algorithms. We find that our branch and bound algorithm finds optimal solutions for medium-sized problem instances in reasonable times and the approximation algorithms produce high quality solutions. We also discuss the way our algorithms could be used to construct the time/cost trade-off curve.

In this thesis, we consider the Multi Resource Agent Bottleneck Generalized Assignment Problem. We aim to minimize the maximum load over all agents. We study the Linear Programming (LP) relaxation of the problem. We use the optimal LP relaxation solutions in our Branch and Bound algorithm while defining lower and upper bounds and branching schemes. We find that our Branch and Bound algorithm returns optimal solutions to the problems with up to 60 jobs when the number of agents is 5, and up to 30 jobs when the number of agents is 10, in less than 20 minutes. To find approximate solutions, we define a tabu search algorithm and an &amp / #945 / approximation algorithm. Our computational results have revealed that these procedures can find high quality solutions to large sized instances very quickly.