The past decades have seen rapid development of numerous high-throughput
technologies to observe biomolecular phenomena. High-throughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrate inputs to arrive at an observable phenotype. Approaches are needed to not only analyze heterogeneous biological data, but also model the complex
experimental observation procedures.
We first present an algorithm for learning dynamic cell cycle
transcriptional regulatory networks from gene expression and
transcription factor binding data. We learn regulatory networks using
dynamic Bayesian network inference algorithms that combine evidence from
gene expression data through the likelihood and evidence from binding data through an informative structure prior.
We next demonstrate how analysis of cell cycle measurements like gene
expression data are obstructed by sychrony loss in synchronized cell
populations. Due to synchrony loss, population-level cell cycle
measurements are convolutions of the true measurements that would have
been observed when monitoring individual cells. We introduce a fully
parametric, probabilistic model, CLOCCS, capable of characterizing multiple
sources of asynchrony in synchronized cell populations. Using CLOCCS, we
formulate a constrained convex optimization deconvolution algorithm that recovers single cell estimates from observed population-level measurements.
Our algorithm offers a solution for monitoring individual cells rather than
a population of cells that lose synchrony over time. Using our
deconvolution algorithm, we provide a global high resolution view of cell
cycle gene expression in budding yeast, right from an initial cell
progressing through its cell cycle, to across the newly created mother and
daughter cell.
Proteins, and not gene expression, are responsible for all cellular
functions, and we need to understand how proteins and protein complexes
operate. We introduce PROCTOR, a statistical approach capable of learning
the hidden interaction topology of protein complexes from direct
protein-protein interaction data and indirect co-complexed protein
interaction data. We provide a global view of the budding yeast interactome
depicting how proteins interact with each other via their interfaces to
form macromolecular complexes.
We conclude by demonstrating how our algorithms, utilizing
information from heterogeneous biological data, can provide a dynamic view of regulatory control in the budding yeast cell cycle. / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/615 |
| Date | 24 April 2008 |
| Creators | Bernard, Allister P. |
| Contributors | Hartemink, Alexander J. |
| Source Sets | Duke University |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 34281805 bytes, application/pdf |
Page generated in 0.0018 seconds