Nucleic acids store genetic information in the sequence of nucleobases. Through duplex formation and template directed synthesis, the information stored in nucleic acids determines their three-dimensional structure and function. Nucleic acids are essential molecules for biological processes and have been used in nanotechnology. Modified nucleic acids have been synthesised that still form duplexes and can be tolerated by enzymes, suggesting that it is possible to construct a synthetic system comparable to nucleic acids, orthogonal to nucleic acids. This thesis describes the synthesis of a new class of synthetic information molecule, characterisation of the duplex forming properties, and attempts at templated oligomerisation reactions. The new synthetic information molecule is based on the phenylacetylene oligomer framework developed by Moore and co-workers. Recognition was achieved via a base-pair that is made from a single point high affinity H-bond, with phenol as the H-bond donor (D) and phosphine oxide as the H-bond acceptor (A). The Sonogashira coupling was used to construct the phenylacetylene oligomer backbone. The AA, DD and AD 2-mers were synthesised and complementary 2-mers showed cooperative duplex formation. No intramolecular H-bonding due to folding was observed in the AD mixed 2-mer. Longer oligomers were synthesised using a method of oligomerisation and chromatographic separation by reverse-phase preparatory HPLC. Homo-oligomers up to the 7-mer were isolated and binding studies between complementary all donor, all acceptor homo-oligomers showed increasing duplex stability with each additional recognition unit in the oligomer chain. Oligomers containing both acceptor and donor recognition modules in the same chain were synthesised and NMR dilution studies were used to investigate their ability to fold. Preliminary experiments were carried out to evaluate the ability of these information molecules to template oligomerisation reactions, but when reactions were carried out at concentrations low enough for a significant template effect, no coupling reactions were observed.

The rapid increase in urbanization poses challenges in diverse areas such as energy, transportation, pandemic planning, and disaster response. Planning for urbanization is a big challenge because cities are complex systems consisting of human populations, infrastructures, and interactions and interdependence among them. This dissertation focuses on a synthetic information-based approach for modeling human activities and behaviors for two urban science applications, epidemiology and disaster planning, and with associated analytics. Synthetic information is a data-driven approach to create a detailed, high fidelity representation of human populations, infrastructural systems and their behavioral and interaction aspects. It is used in developing large-scale simulations to model what-if scenarios and for policy making. Big cities have a large number of visitors visiting them every day. They often visit crowded areas in the city and come into contact with each other and the area residents. However, most epidemiological studies have ignored their role in spreading epidemics. We extend the synthetic population model of the Washington DC metro area to include transient populations, consisting of tourists and business travelers, along with their demographics and activities, by combining data from multiple sources. We evaluate the effect of including this population in epidemic forecasts, and the potential benefits of multiple interventions that target transients. In the next study, we model human behavior in the aftermath of the detonation of an improvised nuclear device in Washington DC. Previous studies of this scenario have mostly focused on modeling physical impact and simple behaviors like sheltering and evacuation. However, these models have focused on optimal behavior, not naturalistic behavior. In other words, prior work is focused on whether it is better to shelter-in-place or evacuate, but has not been informed by the literature on what people actually do in the aftermath of disasters. Natural human behaviors in disasters, such as looking for family members or seeking healthcare, are supported by infrastructures such as cell-phone communication and transportation systems. We model a range of behaviors such as looking for family members, evacuation, sheltering, healthcare-seeking, worry, and search and rescue and their interactions with infrastructural systems. Large-scale and complex agent-based simulations generate a large amount of data in each run of the simulation, making it hard to make sense of results. This leads us to formulate two new problems in simulation analytics. First, we develop algorithms to summarize simulation results by extracting causally-relevant state sequences - state sequences that have a measurable effect on the outcome of interest. Second, in order to develop effective interventions, it is important to understand which behaviors lead to positive and negative outcomes. It may happen that the same behavior may lead to different outcomes, depending upon the context. Hence, we develop an algorithm for contextual behavior ranking. In addition to the context mentioned in the query, our algorithm also identifies any additional context that may affect the behavioral ranking. / Ph. D.