The inevitable aging process can be partially attributed to the accumulation of oxidative damage that results from the action of free radicals. Methionine sulfoxide reductases (Msr) are a class of enzymes that repair oxidized methionine residues. The two known forms of Msr are MsrA and MsrB which reduce the R- and S- enantiomers of methionine sulfoxide, respectively. Our lab has created the first genetic animal model that is fully deficient for any Msr activity. Previously our lab showed that these animals exhibit a 20 hour delay in development of the third instar larvae (unpublished data). My studies have further shown that the prolonged third-instar stage is due to a reduced growth rate associated with slower food intake and a markedly slower motility. These Msr-deficient animals also exhibit decreased egg-laying that can be attributed to a lack of female receptivity to mating. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

La Leucoencefalopatía Megalencefálica con Quistes Subcorticales (MLC) es un tipo raro de leucodistrofia vacuolizante, que presenta como principales características clínicas macrocefalia, deterioro de las funciones motoras, epilepsia y retraso mental medio. Sin embargo, el diagnóstico de MLC se confirma mediante imágenes de resonancia magnética, donde el encéfalo se presenta atrofiado e hinchado, muestra una sustancia blanca anormalmente difusa y hay presencia de quistes subcorticales. Desde el punto de vista fisiopatológico, una biopsia obtenida de un paciente de MLC muestra la presencia de numerosas vacuolas situadas en las láminas más externas de la mielina. Se ha encontrado un primer gen responsable de la enfermedad en el 75% de los pacientes afectados, denominado MLC1. Se han descrito alrededor de 60 mutaciones, aunque existen pacientes que manifiestan las características clínicas de la enfermedad pero no presentan mutaciones en MLC1 ni presentan ligamiento con su locus, sugiriendo que existe al menos otro gen involucrado en la enfermedad. En el 25% de pacientes restantes, la enfermedad se manifiesta de dos maneras diferentes: en un caso, los enfermos presentan las mismas características clínicas que los pacientes con mutaciones en MLC1; y en el otro, presentan síntomas transitorios y los pacientes mejoran, llegando incluso a que la enfermedad remitiera. El gen MLC1 codifica para una proteína transmembrana que lleva el mismo nombre. Su función es todavía desconocida. Aunque muestra un bajo grado de homología con el canal de potasio Kv1.1 no se ha podido detectar actividad de canal iónico en diferentes sistemas heterólogos. No obstante, dicha homología, su confinamiento en la membrana plasmática y el fenotipo característico vacuolizante de los pacientes sugieren que la proteína podría estar mediando la translocación de iones a través de la superficie celular. El total desconocimiento del rol preciso de la proteína MLC1 ha imposibilitado el entendimiento del mecanismo patofisiológico de la enfermedad, y por ello, no se ha podido desarrollar ningún tratamiento efectivo para los pacientes afectados. Es por ello que nuestro grupo quiso apostar por estrategias innovadoras (combinación de bioquímica y genética) para poder encontrar otros genes responsables de la enfermedad. Usando técnicas de purificación por afinidad combinada con métodos de proteómica cuantitativa encontramos a GlialCAM como una proteína que estaba asociada con MLC1. Es por eso que decidimos estudiar (en colaboración) si los pacientes que no tenían mutaciones en MLC1 podían presentar mutaciones en GLIALCAM. Tras el análisis de 40 de estos pacientes encontramos que cuando los enfermos tenían las características clínicas típicas de MLC presentaban dos mutaciones en GLIALCAM (herencia recesiva); mientras que en el caso de aquellos que mejoraban a lo largo del tiempo, éstos solo presentaban una mutación (herencia dominante), demostrando que GLIALCAM es el segundo gen de MLC. En este estudio también se ha podido determinar que mutaciones dominantes en GLIALCAM podían también causar otras enfermedades como la macrocefalia familiar benigna y la macrocefalia con retraso mental, con o sin autismo. Estudios bioquímicos posteriores han permitido avanzar en el entendimiento de la relación que existe entre MLC1 y GlialCAM. Así se ha demostrado que GlialCAM actúa como una molécula escolta, necesaria para localizar específicamente a MLC1 en uniones celulares. De esta forma pudimos descubrir que las mutaciones en GLIALCAM provocaban un defecto en el tráfico de la proteína debido a una deficiente oligomerización. Como consecuencia, estas mutaciones provocaban la deslocalización de los complejos de MLC1-GlialCAM en las uniones astrocitarias. De forma interesante, GlialCAM permite estabilizar la proteína MLC1, sugiriendo nuevas aproximaciones terapéuticas para los pacientes afectos con MLC. Tras el descubrimiento de GlialCAM como segundo gen de MLC gracias a la aproximación proteómica, y tras comprobar que no todo GlialCAM estaba asociado a MLC1, nos planteamos volver a realizar estudios de proteómica para intentar encontrar posibles proteínas que pudiesen estar interaccionando con GlialCAM. De esta manera encontramos que el canal de cloruro ClC-2, estaba asociado con GlialCAM, y pudimos comprobar que GlialCAM también actuaba como molécula escolta para localizar específicamente a ClC-2 en las uniones entre células. Además, también era capaz de modificar sus propiedades de canal, así como aumentar su función, demostrándose interacción directa entre ambas proteínas. Igualmente que para el caso de MLC1, las mutaciones encontradas en GLIALCAM fallaban en la capacidad de concentrar a ClC-2 en las uniones astrocitarias. Por tanto, la función de GlialCAM podría ser necesaria para agrupar tanto a MLC1 como a ClC-2 en tales uniones, particularmente en los pies terminales astrocitarios, donde podrían estar llevando a cabo su función. ClC-2 podría ser necesario para desarrollar un flujo de Cl- transcelular o para compensar gradientes electroquímicos iónicos que pueden estar ocurriendo en dichas uniones durante cambios en la osmolaridad. El descubrimiento de GlialCAM como una subunidad auxiliar de ClC-2 incrementa la compleja regulación de este canal y proporciona nuevas ideas acerca del papel que ClC-2 puede estar desempeñando en las células gliales así como se sugiere que pueda estar involucrado en la fisiopatología de MLC. / Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a leukodystrophy characterized by early-onset macrocephaly and delayed-onset neurological deterioration. Recessive MLC1 mutations are observed in 75% of patients with MLC. Genetic-linkage studies failed to identify another gene. We have showed that some patients without MLC1 mutations display the classical phenotype; others improve or become normal but retain macrocephaly. To find another MLC-related gene, we used quantitative proteomic analysis of affinity-purified MLC1 as an alternative approach and found that GlialCAM, an IgG-like cell adhesion molecule, is a direct MLC1-binding partner. Analysis of 40 MLC patients without MLC1 mutations revealed multiple different GLIALCAM mutations. Patients with the classical phenotype had two mutations, and patients with the improving phenotype had one mutation. In addition, patients with dominant GLIALCAM mutations, could also had macrocephaly and mental retardation with or without autism. Therefore, we found that GLIALCAM is the second gene found to be mutated in MLC. Furthermore, we demonstrated that GlialCAM functions as an MLC1 beta-subunit, needed for proper localization of MLC1 in cell-cell junctions. We also demonstrated that MLC1 and GlialCAM form homo- and hetero-complexes and that MLC-causing mutations in GLIALCAM mainly reduce the formation of GlialCAM homo-complexes, leading to a defect in the trafficking of GlialCAM alone to cell junctions. GLIALCAM mutations also affect the trafficking of its associated molecule MLC1, explaining why GLIALCAM and MLC1 mutations lead to the same disease: MLC. In this thesis, we also identify GlialCAM as a chloride channel ClC-2 binding partner. GlialCAM and ClC-2 colocalize in Bergmann glia, in astrocyteastrocyte junctions at astrocytic end-feet around blood vessels, and in myelinated fiber tracts. GlialCAM targets ClC-2 to cell junctions, increases ClC- 2 mediated currents, and changes its functional properties. Disease-causing GLIALCAM mutations abolish the targeting of the channel to cell junctions. Hence, we describes the first auxiliary subunit of ClC-2 and suggests that ClC-2 may play a role in the pathology of MLC disease.

In the breeding of grain sorghum (Sorghum bicolour L. Moench) towards improved nutrition and agronomic performance, new methodologies are required to increase genetic diversity and lower the inputs required to track and screen breeding populations. Near-infrared calibration models were developed by partial least squares (PLS) and test-set validation on 364 sorghum samples to predict crude protein and moisture content on whole-grain and milled flour samples. Models using milled flour spectra were more accurately predictive than those from whole grain spectra for all constituents (eg. Protein: R2 = 0.986 on flour vs R2 = 0.962 on whole grain). Discriminant calibrations were established to classify grain colour using partial least squares discriminant analysis (PLS-DA) based upon CIE L*a*b* reference values and visual ranking. Preliminary calibrations were developed for quantities of 18 amino acids, fat and apparent metabolisable energy (AME) on 40 samples using cross-validation, highlighting potential for reliable calibration for these parameters in sorghum. An investigation into the potential of 12C6+ heavy-ion beam mutagenesis of sorghum seed was undertaken by treatment at RIKEN Accelerator Research Facility (Saitama, Japan) and subsequent breeding at Ukulinga research farm and analysis at the Department of Plant Pathology, University of KwaZulu-Natal, Pietermaritzburg, South Africa. Dosage rates of 75, 100 and 150 Gy were compared in seven sorghum varieties to establish optimal dose treatments as determined by germination and survival rates, visible morphological changes and field data over two seasons of field trials. Crude protein variation within the M2 generation was analysed to compare dose rate effects. The need for higher dose rates was indicated by few quantified differences between treatments and control although good correlations between protein deviation and treatment dose rate were elucidated. Differences in varietal response suggest a need to optimize dose rate for specific varieties in future endeavours. In addition, all mutagenized populations were screened for crude protein content using near-infrared spectroscopy (NIRS). Significant differences in protein levels and standard deviations were observed between treated self-pollinated M2 generations and untreated control populations. Individual plants displaying significantly different protein levels were isolated. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.

The Human Genome Project, initiated in 1990, creates an enormous amount of excitement in human genetics—a field of study that seeks answers to the understanding of human evolution, diseases and development, gene therapy, and preventive medicine. The first completion of a human genome in 2003 and the breakthroughs of sequencing technologies in the past few years deliver the promised benefits of genome studies, especially in the roles of genomic variability and human health. However, intensive resource requirements and the associated costs make it infeasible to experimentally verify the effect of every genetic variation. At this stage of genome studies, in silico predictions play an important role in identifying putative functional variants. The most common practice for genome variant evaluation is based on the evolutionary conservation at the mutation site. Nonetheless, sequence conservation is not the absolute predictor for deleteriousness since phylogenetic diversity of aligned sequences used to construct the prediction algorithm has substantial effects on the analysis. This dissertation aims at overcoming the weaknesses of the conservation-based assumption for predicting the variant effects. The dissertation describes three different integrative computational approaches to identify a subset of high-priority amino acid mutations, derived from human genome data. The methods investigate variant-function relationships in three aspects of genome studies—personal genomics, genomics of epilepsy disorders, and genomics of variable drug responses. For genetic variants found in genomes of healthy individuals, an eight-level variant classification scheme is implemented to rank variants that are important towards individualized health profiles. For candidate genetic variants of epilepsy disorders, a novel 3-dimensional structure-based assessment protocol for amino acid mutations is established to improve discrimination between neutral and causal variants at less conserved sites, and to facilitate variant prioritization for experimental validations. For genomic variants that may affect inter-individual variability in drug responses, an explicit structure-based predictor for structural disturbances is developed to efficiently evaluate unknown variants in pharmacogenes. Overall, the three integrative approaches provide an opportunity for examining the effects of genomic variants from multiple perspectives of genome studies. They also introduce an efficient way to catalog amino acid variants on a large scale genome data.

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007