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The coupling of cyclin dependent kinases (CDKs) to an intrinsically oscillating network of transcription factors has been proposed to control progression through the cell cycle in budding yeast, Saccharomyces cerevisiae. The transcription network regulates the temporal expression of many genes, including cyclins, and drives cell-cycle progression, in part,(More)
MOTIVATION To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and(More)
Due to the variety and importance of roles performed by signalling networks, understanding their function and evolution is of great interest. Signalling networks allow organisms to process and react to changes in their internal and external environment. Current estimates suggest that two to three percent of all genomes code for proteins involved in(More)
MOTIVATION Researchers studying large or complex biochemical networks would benefit from the ability to automatically create lucid visualizations and store them in a portable and widely accepted format. SUMMARY Two modules, SBMLSupportLayout and SBWAutoLayout, support reading, creating, manipulating and writing layout information for biochemical models.(More)
Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there(More)
Methods We present in this paper a novel method, SW1PerS, for quantifying periodicity in time series data. The measurement is performed directly, without presupposing a particular shape or pattern, by evaluating the circularity of a high-dimensional representation of the signal. SW1PerS is compared to other algorithms using synthetic data and performance is(More)
We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior(More)
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