A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.
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We present a flexible branching process model for cell population dynamics in synchrony/time-series experiments used to study important cellular processes. Its formulation is constructive, based on an accounting of the unique cohorts in the population as they arise and evolve over time, allowing it to be written in closed form. The model can attribute effects to subsets of the population, providing flexibility not available using the models historically applied to these populations. It provides a tool for in silico synchronization of the population and can be used to deconvolve population-level experimental measurements, such as temporal expression profiles. It also allows for the direct comparison of assay measurements made from multiple experiments. The model can be fit either to budding index or DNA content measurements, or both, and is easily adaptable to new forms of data. The ability to use DNA content data makes the model applicable to almost any organism. We describe the model and illustrate its utility and flexibility in a study of cell cycle progression in the yeast Saccharomyces cerevisiae.
Published Version (Please cite this version)10.1214/09-AOAS264
Publication InfoOrlando, David A; Iversen, Edwin S; Hartemink, Alexander J; & Haase, Steven B (2009). A branching process model for flow cytometry and budding index measurements in cell synchrony experiments. Ann Appl Stat, 3(4). pp. 1521-1541. 10.1214/09-AOAS264. Retrieved from https://hdl.handle.net/10161/13267.
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Associate Professor of Biology
Our group is broadly interested in understanding the biological clock mechanisms that control the timing of events during the cell division cycle. In 2008, the Haase group proposed a new model in which a complex network of sequentially activated transcription factors regulates the precise timing of gene expression during the cell-cycle, and functions as a robust time-keeping oscillator. Greater than a thousand genes are expressed at distinct phases of the cycle, and the control network itself
Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection
Research Professor of Statistical Science
Bayesian statistical modeling with application to problems in genetic epidemiology and cancer research; models for epidemiological risk assessment, including hierarchical methods for combining related epidemiological studies; ascertainment corrections for high risk family data; analysis of high-throughput genomic data sets.
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