COMPUTATIONAL ANALYSIS OF G-PROTEIN COUPLED RECEPTOR SCREENING, DIMERIZATION, AND DESENSITIZATION

Mechanistic models of G-protein coupled receptor (GPCR) signaling are used to gain insight into how changes in drug properties affect cellular response. Broadly, this work is divided in to three areas focusing on drug screening, desensitization, and receptor dimerization. First, ordinary differential equation models are used to examine biases in drug screening assays such as those used in drug discovery. It is shown that some screens should be innately biased against detecting inverse agonists and as such may miss pharmaceutically valuable drug leads. However, the results also suggest ways in which the screening assay can be modified to correct this bias. Second, Monte Carlo simulations of protein diffusion and reaction are used to determine the effects of drug properties on GPCR activation and desensitization. For most GPCRs, drugs cause an initial burst of activity (activation) followed by an attenuation of the signal over long times (desensitization). Simulations of this activation and desensitization process show that the mean drug-receptor lifetime can affect desensitization in a way that allows receptor activation and desensitization to be partially decoupled. Third, Monte Carlo simulations of receptor dimerization and diffusion are used to show how dimerization can affect membrane organization. Many membrane bound proteins, including GPCRs, form transient dimers, but the physiological reason for dimerization is not clear. The simulations show that dimerization under diffusion limited conditions can lead to the formation of extended clusters. These clusters, in turn, can alter the receptor internalization rate and the degree of cross-talk among receptors, in agreement with experimental findings. Overall, this work has a variety of implications. Pharmacologically, this work presents a new way of making drug discovery a more rational process by focusing assays toward drugs with desirable efficacies and improved desensitization profiles. Similarly, receptor dimerization could also provide a novel mechanism for affecting drug signaling. For basic biology, the modeling work presented here suggests that dimerization could provide a new way to control protein organization within the cell membrane. Together this work helps us to provide us with a more mechanistic understanding of how cells communicate via GPCRs.http://deepblue.lib.umich.edu/bitstream/2027.42/133962/1/woolf.thesis.pdfDescription of woolf.thesis.pdf : Peter Woolf Thesis Documen

Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful.</p> <p>Results</p> <p>Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using <it>E. coli </it>expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for <it>E. coli </it>was integrated with 266 <it>E. coli </it>gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data.</p> <p>Conclusion</p> <p>POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.</p

poolMC: Smart pooling of mRNA samples in microarray experiments

Background: Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements. Results: A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Conclusions: The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Important conditions for its successful implementation include linearity of measurements, sparsity in data, and large experiment size.

Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

Background: The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge. Results: We introduce a new general-purpose analytic method called Mechanistic Bayesian Networks (MBNs) that allows for the integration of gene expression data and known constraints within a signal or regulatory pathway to predict new downstream pathway targets. The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL). We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as Gas1 and Gli1, along with a short list of likely targets such as Mig12. Conclusions: The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.Molecular and Cellular BiologyStem Cell and Regenerative Biolog

Sensitivity of ferry services to the Western Isles of Scotland to changes in wave and wind climate

PublishedJournal ArticleThis is the final version of the article. Available from AMS via the DOI in this record.The roughness of the seas is rarely mentioned as a major factor in the economic or social welfare of a region. In this study, the relationship between the ocean wave climate and the economy of the Western Isles of Scotland is examined. This sparsely populated region has a high dependency on marine activities, and ferry services provide vital links between communities. The seas in the region are among the roughest in the world during autumn and winter, however, making maintenance of a reliable ferry service both difficult and expensive. A deterioration in wave and wind climate either in response to natural variability or as a regional response to anthropogenic climate change is possible. Satellite altimetry and gale-frequency data are used to analyze the contemporary response of wave and wind climate to the North Atlantic Oscillation (NAO). The sensitivity of wave climate to the NAO extends to ferry routes that are only partially sheltered and are exposed to ocean waves; thus, the reliability of ferry services is sensitive to NAO. Any deterioration of the wave climate will result in a disproportionately large increase in ferry-service disruption. The impacts associated with an unusually large storm event that affected the region in January 2005 are briefly explored to provide an insight into vulnerability to future storm events. © 2013 American Meteorological Society.This research was largely supported by the Tyndall Centre for Climate Change Research project “Toward a vulnerability assessment for the UK coastline” (IT 1.15)

Time series gene expression profiling and temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization

Background: BMP6 mediated osteoblast differentiation plays a key role in skeletal development and bone disease. Unfortunately, the signaling pathways regulated by BMP6 are largely uncharacterized due to both a lack of data and the complexity of the response. Results: To better characterize the signaling pathways responsive to BMP6, we conducted a time series microarray study to track BMP6 induced osteoblast differentiation and mineralization. These temporal data were analyzed using a customized gene set analysis approach to identify temporally coherent sets of genes that act downstream of BMP6. Our analysis identified BMP6 regulation of previously reported pathways, such as the TGF-beta pathway. We also identified previously unknown connections between BMP6 and pathways such as Notch signaling and the MYB and BAF57 regulatory modules. In addition, we identify a super-network of pathways that are sequentially activated following BMP6 induction. Conclusion: In this work, we carried out a microarray-based temporal regulatory pathway analysis of BMP6 induced osteoblast differentiation and mineralization using GAGE method. This novel temporal analysis is more informative and powerful than the classical static pathway analysis in that: (1) it captures the interconnections between signaling pathways or functional modules and demonstrates the even higher level organization of molecular biological systems; (2) it describes the temporal perturbation patterns of each pathway or module and their dynamic roles in osteoblast differentiation. The same set of experimental and computational strategies employed in our work could be useful for studying other complex biological processes

Advanced characterization and simulation of SONNE: a fast neutron spectrometer for Solar Probe Plus

SONNE, the SOlar NeutroN Experiment proposed for Solar Probe Plus, is designed to measure solar neutrons from 1-20 MeV and solar gammas from 0.5-10 MeV. SONNE is a double scatter instrument that employs imaging to maximize its signal-to-noise ratio by rejecting neutral particles from non-solar directions. Under the assumption of quiescent or episodic small-flare activity, one can constrain the energy content and power dissipation by fast ions in the low corona. Although the spectrum of protons and ions produced by nanoflaring activity is unknown, we estimate the signal in neutrons and γ−rays that would be present within thirty solar radii, constrained by earlier measurements at 1 AU. Laboratory results and simulations will be presented illustrating the instrument sensitivity and resolving power

GAGE: generally applicable gene set enrichment for pathway analysis

<p>Abstract</p> <p>Background</p> <p>Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.</p> <p>Results</p> <p>To address these limitations, we present a new GSA method called Generally Applicable Gene-set Enrichment (GAGE). We successfully apply GAGE to multiple microarray datasets with different sample sizes, experimental designs and profiling techniques. GAGE shows significantly better results when compared to two other commonly used GSA methods of GSEA and PAGE. We demonstrate this improvement in the following three aspects: (1) consistency across repeated studies/experiments; (2) sensitivity and specificity; (3) biological relevance of the regulatory mechanisms inferred.</p> <p>GAGE reveals novel and relevant regulatory mechanisms from both published and previously unpublished microarray studies. From two published lung cancer data sets, GAGE derived a more cohesive and predictive mechanistic scheme underlying lung cancer progress and metastasis. For a previously unpublished BMP6 study, GAGE predicted novel regulatory mechanisms for BMP6 induced osteoblast differentiation, including the canonical BMP-TGF beta signaling, JAK-STAT signaling, Wnt signaling, and estrogen signaling pathways–all of which are supported by the experimental literature.</p> <p>Conclusion</p> <p>GAGE is generally applicable to gene expression datasets with different sample sizes and experimental designs. GAGE consistently outperformed two most frequently used GSA methods and inferred statistically and biologically more relevant regulatory pathways. The GAGE method is implemented in R in the "gage" package, available under the GNU GPL from <url>http://sysbio.engin.umich.edu/~luow/downloads.php</url>.</p