A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems

Estimation of Distribution Algorithms (EDAs) use global statistical information effectively to sample offspring disregarding the location information of the locally optimal solutions found so far. Evolutionary Algorithm with Guided Mutation (EAG) combines global statistical information and location information to sample offspring, aiming that this hybridization improves the search and optimization process. This paper discusses a comparative study of Population-Based Incremental Learning (PBIL), a representative of EDAs, and EAG on large-scale global optimization problems. We implemented PBIL and EAG to build an experimental setup upon which simulations were run. The performance of these algorithms was analyzed in terms of solution quality and computational cost. We found that EAG performed better than PBIL in attaining a good quality solution, but the latter performed better in terms of computational cost. We also compared the performance of EAG and PBIL with MA-SW-Chains, the winner of CEC’2010, and found that the overall performance of EAG is comparable to MA-SW-Chains

Stimulating antibiotic development by targeting virulence and facilitating natural product discovery

Antibiotics are a cornerstone of modern medicine and have drastically reduced the burden of infectious diseases. Unfortunately, resistance to all clinically used antibiotics has become a major challenge that is exacerbated by numerous difficulties surrounding the development of new drugs. However, inventive strategies to overcome resistance as well as discover novel antibiotics are increasingly being explored. Whereas traditional antibiotics were generally designed to directly kill as many species of bacteria as possible, several new approaches have focused on narrower spectrum agents that have significant potential benefits. Antibiotics active against only one or a small group of pathogens would spare the microbiome, which may decrease the risk of secondary infections and slow the spread of resistance. One such narrow-spectrum strategy is to target the virulence factors employed by pathogens during an infection. In chapter 2, I demonstrate that the FDA approved HIV protease inhibitor nelfinavir can be repurposed as an inhibitor of the biosynthesis of the Streptococcus pyogenes cytolytic toxin streptolysin S. Nelfinavir was utilized to explore the proteolytic processing step in streptolysin S biosynthesis and was also shown to inhibit toxin production in other pathogens known to harbor similar biosynthetic clusters. Another approach to the problem of finding new antibiotics can be found in facilitating natural product discovery. Many antibiotics are derived from natural products but continuing to find new compounds has become increasingly difficult, especially due to rediscovery of known natural products. To help circumvent this problem, I developed a probe for identifying natural products containing aldehydes and ketones from microbial extracts based on the chemical reactivity of those carbonyl functional groups (chapter 3). This method is agnostic to the activity of the product and allows for the rapid identification of low abundance compounds that may be missed through activity-based screening. I demonstrate the utility of this probe by screening a collection of bacterial extracts, leading to the discovery of an analog of the protease inhibitor antipain