8th Annual Research Week- Event Proceedings

8th Annual Research Wee

OPTIMIZING PRODUCTION METHODS FOR ARTIFICAL SILK PROTEINS THROUGH BIOREACTOR AND PURIFICATION STUDIES OF RECOMBINANT PROTEINS EXPRESSED FROM Pichia pastoris

Advancements in the field of biomaterials are being made to produce an artificial silk fiber. A gene was constructed which utilized components from both the dragline silk of Nephila clavipes and nematode collagen. For this engineered protein, the yeast strain Pichia pastoris was chosen to be the host organism. Previous research has shown P. pastoris to have comparatively low amounts of specific protein productivity. Therefore, this problem must be compensated for by obtaining extremely high cell densities. The main focus of this study was to optimize the fermentation parameters of transgenic yeast cultures within a bioreactor in order to increase the yield of the recombinant protein. Through media improvement and feed pump control, cell densities of 350 optical density (OD) were obtained. Concentration and purification methods revealed insight into this material\u27s potential for future processing. Additionally, comparative studies with natural spider silk revealed temperature fluctuations within the spinneret region

Advances in Bio-Inspired Robots

This book covers three major topics, specifically Biomimetic Robot Design, Mechanical System Design from Bio-Inspiration, and Bio-Inspired Analysis on A Mechanical System. The Biomimetic Robot Design part introduces research on flexible jumping robots, snake robots, and small flying robots, while the Mechanical System Design from Bio-Inspiration part introduces Bioinspired Divide-and-Conquer Design Methodology, Modular Cable-Driven Human-Like Robotic Arm andWall-Climbing Robot. Finally, in the Bio-Inspired Analysis on A Mechanical System part, research contents on the control strategy of Surgical Assistant Robot, modeling of Underwater Thruster, and optimization of Humanoid Robot are introduced

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty