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MODELING AND PARTITIONING THE NUCLEOTIDE EVOLUTIONARY PROCESS FOR PHYLOGENETIC AND COMPARATIVE GENOMIC INFERENCE
Title
MODELING AND PARTITIONING THE NUCLEOTIDE EVOLUTIONARY PROCESS FOR PHYLOGENETIC AND COMPARATIVE GENOMIC INFERENCE.
Creator
Castoe, Todd, Parkinson, Christopher, University of Central Florida
Abstract / Description
The transformation of genomic data into functionally relevant information about the composition of biological systems hinges critically on the field of computational genome biology, at the core of which lies comparative genomics. The aim of comparative genomics is to extract meaningful functional information from the differences and similarities observed across genomes of different organisms. We develop and test a novel framework for applying complex models of nucleotide evolution to solve...
Show moreThe transformation of genomic data into functionally relevant information about the composition of biological systems hinges critically on the field of computational genome biology, at the core of which lies comparative genomics. The aim of comparative genomics is to extract meaningful functional information from the differences and similarities observed across genomes of different organisms. We develop and test a novel framework for applying complex models of nucleotide evolution to solve phylogenetic and comparative genomic problems, and demonstrate that these techniques are crucial for accurate comparative evolutionary inferences. Additionally, we conduct an exploratory study using vertebrate mitochondrial genomes as a model to identify the reciprocal influences that genome structure, nucleotide evolution, and multi-level molecular function may have on one another. Collectively this work represents a significant and novel contribution to accurately modeling and characterizing patterns of nucleotide evolution, a contribution that enables the enhanced detection of patterns of genealogical relationships, selection, and function in comparative genomic datasets. Our work with entire mitochondrial genomes highlights a coordinated evolutionary shift that simultaneously altered genome architecture, replication, nucleotide evolution and molecular function (of proteins, RNAs, and the genome itself). Current research in computational biology, including the advances included in this dissertation, continue to close the gap that impedes the transformation of genomic data into powerful tools for the analysis and understanding of biological systems function.
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Date Issued
2007
Identifier
CFE0001548, ucf:47138
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001548
Network Partitioning in Distributed Agent-Based Models
Title
Network Partitioning in Distributed Agent-Based Models.
Creator
Petkova, Antoniya, Deo, Narsingh, Hughes, Charles, Bassiouni, Mostafa, Shaykhian, Gholam, University of Central Florida
Abstract / Description
Agent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical.The real-time requirement...
Show moreAgent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical.The real-time requirement necessitates the use of in-memory computing, as it is dif?cult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use parallel and distributed computing platforms, such as MPI-enabled CPU clusters. Consequently, the agent population must be "partitioned" across different CPUs in a cluster. Further, the typically high volume of interactions among agents can quickly become a signi?cant bottleneck for real-time or large-scale simulations. The problem is exacerbated if the underlying ABM network is dynamic and the inter-process communication evolves over the course of the simulation. Therefore, it is critical to develop topology-aware partitioning mechanisms to support such large simulations.In this dissertation, we demonstrate that distributed agent-based model simulations bene?t from the use of graph partitioning algorithms that involve a local, neighborhood-based perspective. Such methods do not rely on global accesses to the network and thus are more scalable. In addition, we propose two partitioning schemes that consider the bottom-up individual-centric nature of agent-based modeling. The ?rst technique utilizes label-propagation community detection to partition the dynamic agent network of an ABM. We propose a latency-hiding, seamless integration of community detection in the dynamics of a distributed ABM. To achieve this integration, we exploit the similarity in the process flow patterns of a label-propagation community-detection algorithm and self-organizing ABMs.In the second partitioning scheme, we apply a combination of the Guided Local Search (GLS) and Fast Local Search (FLS) metaheuristics in the context of graph partitioning. The main driving principle of GLS is the dynamic modi?cation of the objective function to escape local optima. The algorithm augments the objective of a local search, thereby transforming the landscape structure and escaping a local optimum. FLS is a local search heuristic algorithm that is aimed at reducing the search space of the main search algorithm. It breaks down the space into sub-neighborhoods such that inactive sub-neighborhoods are removed from the search process. The combination of GLS and FLS allowed us to design a graph partitioning algorithm that is both scalable and sensitive to the inherent modularity of real-world networks.
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Date Issued
2017
Identifier
CFE0006903, ucf:51706
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006903