Browsing by Subject "regulatory networks"

Now showing 1 - 3 of 3

Open Access
Bifurcation Analysis of Gene Regulatory Circuits Subject to Copy Number Variation
(2010) Mileyko, Yuriy; Weitz, Joshua S
Gene regulatory networks are comprised of many small gene circuits. Understanding expression dynamics of gene circuits for broad ranges of parameter space may provide insight into the behavior of larger regulatory networks as well as facilitate the use of circuits as autonomous units performing specific regulatory tasks. In this paper, we consider three common gene circuits and investigate the dependence of gene expression dynamics on the circuit copy number. In particular, we perform a detailed bifurcation analysis of the circuits' corresponding nonlinear gene regulatory models restricted to protein-only dynamics. Employing a geometric approach to bifurcation theory, we are able to derive closed form expressions for conditions which guarantee existence of saddle-node bifurcations caused by variation in the circuit copy number or copy number concentration. This result shows that the drastic effect of copy number variation on equilibrium behavior of gene circuits is highly robust to variation in other parameters in the circuits. We discuss a possibility of extending the current results to higher dimensional models which incorporate more details of the gene regulatory process.
Open Access
Modeling Biological Systems from Heterogeneous Data
(2008-04-24) Bernard, Allister P.
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.
Open Access
Regulation of Global Transcription Dynamics During Cell Division and Root Development
(2009) Orlando, David Anthony
The successful completion of many critical biological processes depends on the proper execution of complex spatial and temporal gene expression programs. With the advent of high-throughput microarray technology, it is now possible to measure the dynamics of these expression programs on a genome-wide level. In this thesis we present work focused on utilizing this technology, in combination with novel computational techniques, to examine the role of transcriptional regulatory mechanisms in controlling the complex gene expression programs underlying two fundamental biological processes---the cell cycle and the development and differentiation of an organ.
We generate a dataset describing the genomic expression program which occurs during the cell division cycle of Saccharomyces cerevisiae. By concurrently measuring the dynamics in both wild-type and mutant cells that do not express either S-phase or mitotic cyclins we quantify the relative contributions of cyclin-CDK complexes and transcriptional regulatory networks in the regulation the cell cell expression program. We show that CDKs are not the sole regulators of periodic transcription as contrary to previously accepted models; and we hypothesize an oscillating transcriptional regulatory network which could work independent of, or in tandem with, the CDK oscillator to control the cell cell expression program.
To understand the acquisition of cellular identity, we generate a nearly complete gene expression map of the Arabidopsis Thaliana root at the resolution of individual cell-types and developmental stages. An analysis of this data reveals a representative set of dominant expression patterns which are used to begin defining the spatiotemporal transcriptional programs that control development within the root.
Additionally, we develop computational tools that improve the interpretability and power of these data. We present CLOCCS, a model for the dynamics of population synchrony loss in time-series experiments. We demonstrate the utility of CLOCCS in integrating disparate datasets and present a CLOCCS based deconvolution of the cell-cycle expression data. A deconvolution method is also developed for the Arabidopsis dataset, increasing its resolution to cell-type/section subregion specificity. Finally, a method for identifying biological processes occurring on multiple timescales is presented and applied to both datasets.
It is through the combination of these new genome-wide expression studies and computational tools that we begin to elucidate the transcriptional regulatory mechanisms controlling fundamental biological processes.