Matching Items (5)
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Simon Edward Fisher studied the genes that control speech and language in England and the Netherlands in the late twentieth and early twenty-first centuries. In 2001, Fisher co-discovered the FOXP2 gene with Cecilia Lai, a gene related to language acquisition in humans and vocalization in other mammals. When damaged, the human version of the gene leads to language disorders that disrupt language and speech skills. Fisher's discovery validated the hypothesis that genes influence language, resulting in further investigations of language disorders and their heritability. Fisher's research enabled scientists to better study how genetics play a role in speech, language, and human behavior.
ContributorsFowler, Kat (Author) / Chou, Cecilia (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2017-04-27
Description
In 1969, Roy J. Britten and Eric H. Davidson published Gene Regulation for Higher Cells: A Theory, in Science. A Theory proposes a minimal model of gene regulation, in which various types of genes interact to control the differentiation of cells through differential gene expression. Britten worked at the Carnegie Institute of Washington in Washington, D.C., while Davidson worked at the California Institute of Technology in Pasadena, California. Their paper was an early theoretical and mechanistic description of gene regulation in higher organisms.
ContributorsWolter, Justin (Author) / Sutherland, Alison (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2013-09-10
Description
In 2002 Eric Davidson and his research team published 'A Genomic Regulatory Network for Development' in Science. The authors present the first experimental verification and systemic description of a gene regulatory network. This publication represents the culmination of greater than thirty years of work on gene regulation that began in 1969 with 'A Gene Regulatory Network for Development: A Theory' by Roy Britten and Davidson. The modeling of a large number of interactions in a gene network had not been achieved before. Furthermore, this model revealed behaviors of the gene networks that could only be observed at the levels of biological organization above that of the gene.
ContributorsWolter, Justin M. (Author) / Sutherland, Alison (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2013-10-11
Description
Vitellogenin (Vg) is an ancient and highly conserved multifunctional protein. It is primarily known for its role in egg-yolk formation but also serves functions pertaining to immunity, longevity, nutrient storage, and oxidative stress relief. In the honey bee (Apis mellifera), Vg has evolved still further to include important social functions that are critical to the maintenance and proliferation of colonies. Here, Vg is used to synthesize royal jelly, a glandular secretion produced by a subset of the worker caste that is fed to the queen and young larvae and which is essential for caste development and social immunity. Moreover, Vg in the worker caste sets the pace of their behavioral development as they transition between different tasks throughout their life. In this dissertation, I make several new discoveries about Vg functionality. First, I uncover a colony-level immune pathway in bees that uses royal jelly as a vehicle to transfer pathogen fragments between nestmates. Second, I show that Vg is localized and expressed in the honey bee digestive tract and suggest possible immunological functions it may be performing there. Finally, I show that Vg enters to nucleus and binds to deoxyribonucleic acid (DNA), acting as a potential transcription factor to regulate expression of many genes pertaining to behavior, metabolism, and signal transduction pathways. These findings represent a significant advance in the understanding of Vg functionality and honey bee biology, and set the stage for many future avenues of research.
ContributorsHarwood, Gyan (Author) / Amdam, Gro V (Thesis advisor) / Kusumi, Kenro (Committee member) / Rabeling, Christian (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2019