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Network Models for Materials and Biological SystemsJanuary 2011 (has links)
abstract: The properties of materials depend heavily on the spatial distribution and connectivity of their constituent parts. This applies equally to materials such as diamond and glasses as it does to biomolecules that are the product of billions of years of evolution. In science, insight is often gained through simple models with characteristics that are the result of the few features that have purposely been retained. Common to all research within in this thesis is the use of network-based models to describe the properties of materials. This work begins with the description of a technique for decoupling boundary effects from intrinsic properties of nanomaterials that maps the atomic distribution of nanomaterials of diverse shape and size but common atomic geometry onto a universal curve. This is followed by an investigation of correlated density fluctuations in the large length scale limit in amorphous materials through the analysis of large continuous random network models. The difficulty of estimating this limit from finite models is overcome by the development of a technique that uses the variance in the number of atoms in finite subregions to perform the extrapolation to large length scales. The technique is applied to models of amorphous silicon and vitreous silica and compared with results from recent experiments. The latter part this work applies network-based models to biological systems. The first application models force-induced protein unfolding as crack propagation on a constraint network consisting of interactions such as hydrogen bonds that cross-link and stabilize a folded polypeptide chain. Unfolding pathways generated by the model are compared with molecular dynamics simulation and experiment for a diverse set of proteins, demonstrating that the model is able to capture not only native state behavior but also partially unfolded intermediates far from the native state. This study concludes with the extension of the latter model in the development of an efficient algorithm for predicting protein structure through the flexible fitting of atomic models to low-resolution cryo-electron microscopy data. By optimizing the fit to synthetic data through directed sampling and context-dependent constraint removal, predictions are made with accuracies within the expected variability of the native state. / Dissertation/Thesis / Ph.D. Physics 2011
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Generating a CBLS Invariant Structure from a FlatZinc ModelPerea Düring, Max January 2021 (has links)
Constraint-Based Local Search (CBLS) is a technology used to solve computationally hard optimisation problems. A model written in a solver-independent modelling language needs to be processed before it can be solved by a CBLS solver. In this processing step, it is necessary to identify invariants and create an invariant structure. How to best obtain such a structure, or even how to identify a good structure, is not clear. The purpose of this project is to develop a framework for evaluating invariant structures and structure identification schemes. To do this, we introduce a set of metrics, which are also evaluated. The evaluation shows that these metrics are useful for evaluating invariant structures and structure identification schemes. We introduce a notion of optimal invariant structures and show that these can in many cases be produced by simple structure identification schemes. Finally, we present a strategy that improves on these schemes and yields optimal invariant structures in even more cases.
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