Thesis (Ph. D.)--University of Chicago, Dept. of Biochemistry and Molecular Biology, August 2002. / Includes bibliographical references (p. 262-280). Also available on the Internet.

Honors Project--Smith College, Northampton, Mass., 2010. / Includes bibliographical references (p. 60-64).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

No description available.

Assisted living means to undergo drastic changes in life. Most significant is the change from a familiar domestic environment to a care based situation that includes incremental levels of medical support, often in spaces that are modeled after hospitals. The core of this thesis is grounded in the believe that an architectural familiarity of space should be retained, whereas the overall architecture should be able to silently absorb the necessities of medical support. As a place of habitat, the constellation of rooms proposes for an individual to quickly develop a new community that analogous to the typical domestic communities. / Master of Architecture / The population of the elderly is very large in today's society, which results in urgent needs of assisted living institutions. But some design of assisted living institutions tend to be similar to hospitals, which can not provide comfortable living environment for the elderly. Therefore, the thesis focus to find a possible way for the assisted living institution design to meet both the medical needs and the living needs of the elderly. The main idea is to build a small community for the elderly where they can have their daily life as usual and to provide necessary medical care which will be separated from the living part.

Nesta dissertação apresentamos o desenvolvimento de ferramentas computacionais dedicadas a racionalizar processos que envolvem simulações Monte Carlo e análises de suas aplicações; são evocados conceitos pertinentes às principais áreas envolvidas (computação, tecnologia da informação, matemática e física). São introduzidas e discutidas técnicas de simulação computacional distribuída e o método Monte Carlo, com ênfase à aplicação em heteropolímeros. Exemplos ilustrativos de aplicação da ferramenta também são providos, mediante simulações e análise dos resultados de três tipos de cadeias heteropoliméricas em rede regular: cadeia polar (todos monômeros polares); cadeia hidrofóbica (todos monômeros apolares); cadeia com mescla de monômeros polares e apolares (modelo HP). O propósito motivador deste trabalho é o estudo do problema de folding de heteropolímeros, o que inclui proteínas. Contudo, a ferramenta em questão, poderá ser generalizada e aplicada a praticamente todos os tipos de polímeros lineares em rede, pois o usuário poderá definir e implementar o modelo de cadeia que desejar / We present the development of computational tools dedicated to streamlining processes involving Monte Carlo simulations and analysis of some applications. Relevant concepts to the main areas involved (computation, information technology, mathematics and physics) are evoked. We introduce and discuss techniques of distributed computer simulation and Monte Carlo method with emphasis on heteropolymers. Illustrative examples of the application of the tool are also provided through simulations and analysis of results from three types of polymeric chains: polar chain (all polar monomers); hydrophobic chain (all nonpolar monomers); and a chain with mixture of polar and nonpolar monomers (HP model). The motivating purpose of this work is the study of the folding problem of heteropolymers, which includes proteins. However, the tool in question, can be generalized and applied to virtually all types of linear polymers network, once users can define and implement the chain models they want

Going down the folding funnel, proteins may sample a wide variety of conformations, some being outright detrimental to the organism. Yet, the vast majority of polypeptide molecules avoid such pitfalls. Not only do they reach the native minimum of the energy landscape; they do so via blazingly fast, biased, routes. This specificity and speed is remarkable, as the surrounding solution is filled to the brim with other molecules that could potentially interact with the protein and in doing so stabilise non-native, potentially toxic, conformations. How such incidents are avoided while maintaining native structure and function is not understood.  This doctoral thesis argues that protein structure and function can be separated in the folding code of natural protein sequences by use of multiple partly uncoupled factors that act in a concerted fashion. More specifically, we demonstrate that: i) Evolutionarily conserved functional and regulatory elements can be excised from a present day protein, leaving behind an independently folded protein scaffold. This suggests that the dichotomy between functional and structural elements can be preserved during the course of protein evolution. ii) The ubiquitous charges on soluble protein surfaces are not required for protein folding in biologically relevant timescales, but are critical to intermolecular interaction. Monomer folding can be driven by hydrophobicity and hydrogen bonding alone, while functional and structural intermolecular interaction depends on the relative positions of charges that are not required for the native bias inherent to the folding mechanism. It is possible that such uncoupling reduces the probability of evolutionary clashes between fold and function. Without such a balancing mechanism, functional evolution might pull the carpet from under the feet of structural integrity, and vice versa. These findings have implications for both de novo protein design and the molecular mechanisms behind diseases caused by protein misfolding. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>

RNA and protein molecules undergo a dynamic folding process that is important to their function. Computational methods are critical for studying this folding pro- cess because it is difficult to observe experimentally. In this work, we introduce new computational techniques to study RNA and protein energy landscapes, includ- ing a method to approximate an RNA energy landscape with a coarse graph (map) and new tools for analyzing graph-based approximations of RNA and protein energy landscapes. These analysis techniques can be used to study RNA and protein fold- ing kinetics such as population kinetics, folding rates, and the folding of particular subsequences. In particular, a map-based Master Equation (MME) method can be used to analyze the population kinetics of the maps, while another map analysis tool, map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways from the map. To validate the results, I compared our methods with other computational meth- ods and with experimental studies of RNA and protein. I first compared our MMC and MME methods for RNA with other computational methods working on the com- plete energy landscape and show that the approximate map captures the major fea- tures of a much larger (e.g., by orders of magnitude) complete energy landscape. Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental findings. I present comparisons with two experimental cases to show how I can pre- dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their mutants using our MME and MMC tools respectively. I also show that the MME and MMC tools can be applied to map-based approximations of protein energy energy landscapes and present kinetics analysis results for several proteins.

At first glance, robots and proteins have little in common. Robots are commonly thought of as tools that perform tasks such as vacuuming the floor, while proteins play essential roles in many biochemical processes. However, the functionality of both robots and proteins is highly dependent on their motions. In order to study motions in these two divergent domains, the same underlying algorithmic framework can be applied. This method is derived from probabilistic roadmap methods (PRMs) originally developed for robotic motion planning. It builds a graph, or roadmap, where configurations are represented as vertices and transitions between configurations are edges. The contribution of this work is a set of intelligent methods applied to PRMs. These methods facilitate both the modeling and analysis of motions, and have enabled the study of complex and high-dimensional problems in both robotic and molecular domains. In order to efficiently study biologically relevant molecular folding behaviors we have developed new techniques based on Monte Carlo solution, master equation calculation, and non-linear dimensionality reduction to run simulations and analysis on the roadmap. The first method, Map-based master equation calculation (MME), extracts global properties of the folding landscape such as global folding rates. On the other hand, another method, Map-based Monte Carlo solution (MMC), can be used to extract microscopic features of the folding process. Also, the application of dimensionality reduction returns a lower-dimensional representation that still retains the principal features while facilitating both modeling and analysis of motion landscapes. A key contribution of our methods is the flexibility to study larger and more complex structures, e.g., 372 residue Alpha-1 antitrypsin and 200 nucleotide ColE1 RNAII. We also applied intelligent roadmap-based techniques to the area of robotic motion. These methods take advantage of unsupervised learning methods at all stages of the planning process and produces solutions in complex spaces with little cost and less manual intervention compared to other adaptive methods. Our results show that our methods have low overhead and that they out-perform two existing adaptive methods in all complex cases studied.

A crease pattern is an embedded planar graph on a piece of paper. An m × n map is a rectangular piece of paper with a crease pattern that partitions the paper into an m × n regular grid of unit squares. If a map has a configuration such that all the faces of the map are stacked on a unit square and the paper does not self-intersect, then it is flat foldable, and the linear ordering of the faces is called a valid linear ordering. Otherwise, the map is unfoldable. In this thesis, we show that, given a linear ordering of the faces of an m × n map, we can decide in linear time whether it is a valid linear ordering or not. We also define a class of unfoldable 2 × n crease patterns for every n ≥ 5. / Graduate / 0984