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Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying interactions into a single static map. Conventional algorithms are limited to analyzing evolving networks by(More)
Pseudomonas aeruginosa, a gram-negative bacterium of clinical importance, forms more robust biofilm during anaerobic respiration, a mode of growth presumed to occur in abnormally thickened mucus layer lining the cystic fibrosis (CF) patient airway. However, molecular basis behind this anaerobiosis-triggered robust biofilm formation is not clearly defined(More)
BACKGROUND Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a(More)
Cancer cells exhibit a common phenotype of uncontrolled cell growth, but this phenotype may arise from many different combinations of mutations. By inferring how cells evolve in individual tumors, a process called cancer progression, we may be able to identify important mutational events for different tumor types, potentially leading to new therapeutics and(More)
The mechanisms by which mutations of the purinergic housekeeping gene hypoxanthine guanine phosphoribosyltransferase (HPRT) cause the severe neurodevelopmental Lesch Nyhan Disease (LND) are poorly understood. The best recognized neural consequences of HPRT deficiency are defective basal ganglia expression of the neurotransmitter dopamine (DA) and aberrant(More)