Most high-throughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrated inputs to arrive at an observable phenotype. Approaches are needed to not only analyze such heterogeneous data, but also model their complex experimental observation procedures.
We first present a graphical model approach for learning dynamic cell cycle regulatory networks. Our algorithm combines evidence from gene expression data through a likelihood term and protein-DNA binding data through an informative structure prior.
We next demonstrate how analysis of cell cycle measurements from a synchronized population of cells are obstructed by synchrony loss. We introduce a probabilistic model,
, capable of characterizing multiple sources of asynchrony in synchronized cell populations. Using
, we formulate a convex optimization deconvolution procedure 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 losing synchrony over time. Using our deconvolution algorithm, we provide a global high resolution view of cell cycle gene expression in budding yeast, beginning from the cell cycle of an initial cell, to right across the newly created mother and daughter cell.
Understanding any cellular process is incomplete without knowledge of the activity of proteins and protein complexes. We introduce
, 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. Using
, we provide a global view of the physical interactome in budding yeast.
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.