This thesis presents the design and analysis of Spasiba, a context-aware mobile advisor. We argue that current context-aware mobile applications exhibit significant flaws with respect to (1) limited use of context information, (2) incomplete or irrelevant content generation, and (3) low usability. The proposed model attempts to tackle these limitations by advancing the usage and manipulation of context information, automating the back-end systems in terms of self-management and seamless extensibility, and shifting the logic away from the client side. A distinguishing characteristic of Spasiba is the proactive approach to notifying the user of information of interest. In this proactive approach, the user subscribes to the service and receives content updates as the context changes. This proposed model is realised in a proof-of-concept prototype that uses a Nokia Web Runtime widget as the client application. The widget, which sports an elegant, touch-optimised interface, collects multiple context parameters to deliver high-quality results. The server-side architecture employs the publish/subscribe paradigm for managing the active users and Comet—for proactively notifying the clients of updated information of interest. IRS-III, a Semantic Web Services broker, handles the process of content generation. The prototype employs nine data sources, seven of which are open API web services and two of which are regular web pages, to deliver diverse and complete results. A simple autonomic element, implemented with the help of aspect-oriented programming, ensures partial self-management of the back-end systems. Spasiba is evaluated by means of a case study that involves a tourist couple visiting Victoria. The application assists the tourist couple with finding attractions, relevant stores, and places serving food.

This work was aimed at developing novel tools that utilize HINT, an empirical forcefield capable of quantitating both hydrophobic and hydrophilic (hydropathic) interactions, for implementation in theoretical biology and drug discovery/design. The role of hydrophobicity in determination of macromolecular structure and formation of complexes in biological molecules is undeniable and has been the subject of research across several decades. Hydrophobicity is introduced, with a review of its history and contemporary theories. This is followed by a description of various methods that quantify this all-pervading phenomenon and their use in protein folding and contemporary drug design projects – including a detailed overview of the HINT forcefield. The specific aim of this dissertation is to introduce our attempts at developing new methods for use in the study of antibacterial drug resistance and antiviral drug discovery. Multidrug efflux is commonly regarded as a fast growing problem in the field of medicine. Several species of microbes are known to have developed resistance against almost all classes of antibiotics by various modes-of-action, which include multidrug transporters (a.k.a. efflux pumps). These proteins are present in both gram-positive and gram-negative bacteria and extrude molecules of various classes. They protect the efflux pump-expressing bacterium from harmful effects of exogenous agents by simply evacuating the latter. Perhaps the best characterized mechanism amongst these is that of the AcrA-AcrB-TolC efflux pump. Data is available in literature and perhaps also in proprietary databases available with pharmaceutical companies, characterizing this pump in terms of the minimum inhibitory concentration ratios (MIC ratios) for various antibiotics. We procured a curated dataset of 32 β-lactam and 12 antibiotics of other classes from this literature. Initial attempts at studying the MIC ratios of β-lactam antibiotics as a function of their three dimensional topology via 3D-quantitative structure activity relationship (3D-QSAR) technology yielded seemingly good models. However, this methodology is essentially designed to address single receptor-ligand interactions. Molecules being transported by the efflux pump must undoubtedly be involved in multiple interactions with the same. Notably, such methods require a pharmacophoric overlap of ligands prior to the generation of models, thereby limiting their applicability to a set of structurally-related compounds. Thus, we designed a novel method that takes various interactions between antibiotic agents and the AcrA-AcrB-TolC pump into account in conjunction with certain properties of the drugs. This method yielded mathematical models that are capable of predicting high/low efflux with significant efficiency (>93% correct). The development of this method, along with the results from its validation, is presented herein. A parallel aim being pursued by us is to discover inhibitors for hemagglutinin-neuraminidase (HN) of human parainfluenza virus type 3 (HPIV3) by in silico screening. The basis for targeting HN is explored, along with commentary on the methodology adopted during this effort. This project yielded a moderate success rate of 34%, perhaps due to problems in the computational methodology utilized. We highlight one particular problem – that of emulating target flexibility – and explore new avenues for overcoming this obstacle in the long run. As a starting point towards enhancing the tools available to us for virtual screening in general (and for discovering antiviral compounds in specific), we explored the compatibility between sidechain rotamer libraries and the HINT scoring function. A new algorithm was designed to optimize amino acid residue sidechains, if provided with the backbone coordinates, by generating sidechain positions using the Dunbrack and Cohen backbone-dependent rotamer library and scoring them with the HINT scoring function. This rotamer library was previously used by its developers previously to design a very successful sidechain optimization algorithm called SCWRL. Output structures from our algorithm were compared with those from SCWRL and showed extraordinary similarities as well as significant differences, which are discussed herein. This successful implementation of HINT in our sidechain optimization algorithm establishes the compatibility between this forcefield and sidechain rotamer libraries. Future aims in this project include enhancement of our current algorithm and the design of a new algorithm to explore partial induced-fit in targets aimed at improving current docking methodology. This work shows significant progress towards the implementation of our hydropathic force field in theoretical modeling of biological systems in order to enhance our ability to understand atomistic details of inter- and intramolecular interactions which must form the basis for a wide variety of biological phenomena. Such efforts are key to not only to understanding the said phenomena, but also towards a solid basis for efficient drug design in the future.