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Articles 1 - 3 of 3
Full-Text Articles in Medicine and Health Sciences
Circadian Rhythmicity By Autocatalysis, Arun Mehra, Christian I. Hong, Mi Shi, Jennifer J. Loros, Jay C. Dunlap, Peter Ruoff
Circadian Rhythmicity By Autocatalysis, Arun Mehra, Christian I. Hong, Mi Shi, Jennifer J. Loros, Jay C. Dunlap, Peter Ruoff
Dartmouth Scholarship
The temperature compensated in vitro oscillation of cyanobacterial KaiC phosphorylation, the first example of a thermodynamically closed system showing circadian rhythmicity, only involves the three Kai proteins (KaiA, KaiB, and KaiC) and ATP. In this paper, we describe a model in which the KaiA- and KaiB-assisted autocatalytic phosphorylation and dephosphorylation of KaiC are the source for circadian rhythmicity. This model, based upon autocatalysis instead of transcription-translation negative feedback, shows temperature-compensated circadian limit-cycle oscillations with KaiC phosphorylation profiles and has period lengths and rate constant values that are consistent with experimental observations.
A Role For Cetp Taqib Polymorphism In Determining Susceptibility To Atrial Fibrillation: A Nested Case Control Study, Folkert W. Asselbergs, Jason H. Moore, Maarten P. Van Den Berg, Eric B. Rimm
A Role For Cetp Taqib Polymorphism In Determining Susceptibility To Atrial Fibrillation: A Nested Case Control Study, Folkert W. Asselbergs, Jason H. Moore, Maarten P. Van Den Berg, Eric B. Rimm
Dartmouth Scholarship
Studies investigating the genetic and environmental characteristics of atrial fibrillation (AF) may provide new insights in the complex development of AF. We aimed to investigate the association between several environmental factors and loci of candidate genes, which might be related to the presence of AF. A nested case-control study within the PREVEND cohort was conducted. Standard 12 lead electrocardiograms were recorded and AF was defined according to Minnesota codes. For every case, an age and gender matched control was selected from the same population (n = 194). In addition to logistic regression analyses, the multifactor-dimensionality reduction (MDR) method and interaction …
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Dartmouth Scholarship
The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.