Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

ORGANOSILICON BIOTECHNOLOGY: A BIO-INSPIRED APPROACH TO THE HYDROLYSIS OF ALKOXYSILANES and THE LIPASE-CATALYZED SYNTHESIS OF SILOXANE-CONTAINING POLYESTERS AND POLYAMIDES

The first part of this thesis studied the capacity of amino acids and enzymes to catalyze the hydrolysis and condensation of tetraethoxysilane and phenyltrimethoxysilane. Selected amino acids were shown to accelerate the hydrolysis and condensation of tetraethoxysilane under ambient temperature, pressure and at neutral pH (pH 7±0.02). The nature of the side chain of the amino acid was important in promoting hydrolysis and condensation. Several proteases were shown to have a capacity to hydrolyze tri- and tet-ra- alkoxysilanes under the same mild reaction conditions. The second part of this thesis employed an immobilized Candida antarctica lipase B (Novozym-435, N435) to produce siloxane-containing polyesters, polyamides, and polyester amides under solvent-free conditions. Enzymatic activity was shown to be temperature dependent, increasing until enzyme denaturation became the dominant pro-cess, which typically occurred between 120-130ᵒC. The residual activity of N435 was, on average, greater than 90%, when used in the synthesis of disiloxane-containing polyesters, regardless of the polymerization temperature except at the very highest temperatures, 140-150ᵒC. A study of the thermal tolerance of N435 determined that, over ten reaction cycles, there was a decrease in the initial rate of polymerization with each consecutive use of the catalyst. No change in the degree of monomer conversion after a 24 hour reaction cycle was found.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.