Conferences, Lectures, & Seminars
Events for June
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Application of Functional Genomics to Improve Chlorinated Ethane ....
Mon, Jun 11, 2007 @ 02:00 PM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Application of Functional Genomics to Improve Chlorinated Ethene Bioremediation ProcessesSpeaker:David R. Johnson,
Department of Civil and Environmental Engineering,
University of California, BerkeleyAbstractChlorinated ethenes are among the most prevalent contaminants of groundwater resources and pose a significant threat to human and ecological health. Remediating these resources with traditional pump-and-treat strategies is both technically challenging and costly. Fortunately, strategies that utilize natural microorganisms to degrade these pollutants in situ have now been developed and applied with success. Of particular interest is to utilize members of the Dehalococcoides group of bacteria because they are the only known organisms that can completely degrade fully chlorinated ethenes to non-toxic end products. Although significant progress has been made, there is now a need for effective methods to both optimize and monitor the performance of Dehalococcoides-based bioremediation systems.
To begin to address these needs, this research applied functional genomics tools to improve our understanding of Dehalococcoides ethenogenes strain 195. Specifically, transcriptomics were analyzed by whole-genome microarrays while proteomics were analyzed by liquid chromatography coupled with tandem mass spectrometry. These tools were applied during periods of maximal and repressed activity in order to identify factors that can potentially limit dechlorination. This approach successfully identified cobalamin (vitamin B12) as a key factor controlling dechlorination activity and revealed novel strategies for minimizing cobalamin deficiencies within bioremediation systems. In addition, these studies identified mRNA and peptide biomarkers that could be used to quantitatively assess the physiological state of strain 195 within uncharacterized systems.
The results of this research demonstrate that functional genomics can dramatically accelerate our understanding of reductive dechlorinating bacteria important for bioremediation applications. There is now a need for more collaborative efforts between the fields of genome sciences and environmental problems.
Location: Kaprielian Hall (KAP) - rielian Hall, 203
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Inversion of probabilistic models of structures using measured transfer functions
Fri, Jun 22, 2007 @ 12:30 PM - 02:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker:
Maarten Arnst
Department of Civil and Environmental Engineering,
University of Southern California,
3620SVermontAvenue, KAP130,
phone: (213) 740-9165,
mail: arnst@usc.eduAbstract:
Predictive models for the dynamical behavior of complex structures are inevitably confronted to data uncertainties and modeling errors. Uncertain data include material properties, geometric parameters and boundary conditions. Modeling errors are introduced by the assumptions and approximations made in the modeling process. The data uncertainties and modeling errors may sometimes result in significant uncertainties in the model predictions. Probabilistic models then are desirable, since theyprovide a way to quantify the impact of the data uncertainties and modeling errors on the predictions.
Probabilistic mechanics is nowadays a rich and well-developed domain of research, in which a wide variety of methods for constructing probabilistic structural models has been proposed, see e.g. [1, 2, 3, 4]. Compared to deterministic structural models, an important difficulty is that probabilistic models introduce supplementary parameters, e.g. dispersion levels, spatial correlations lengths, or coefficients of chaos decompositions.An active areaof researchis thereforethedevelopmentof methodologies guidingthe choice of these parameters.
This presentation concerns the experimental identification of probabilistic structural models from measured transfer functions. The classical methods of estimation from the theory of mathematical statistics, such as the method of maximum likelihood, see e.g. [5], are not well-adapted to formulate and solve this inverse problem. In particular,numerical difficulties and conceptual problems due to model misspecification prohibit the application of the classical methods. In this talk, we propose to formulate the inversion alternatively as the minimization, with respect to the parameters to be identified, of an objective function measuring a distance between the experimental data and the probabilistic model. Two principles of construction for the definition of this distance are proposed, based on either the loglikelihood function, or the relative entropy, see e.g. [6]. The limitation of the distance to low-order marginal laws is shown to allow circumventing the aforementioned difficulties. The methodology is applied to simple illustrative examples featuring simulated data and to a civil and environmental engineering case history featuring real experimental data.
This work has been performed in the frame of a PhD thesis at Ecole Centrale Paris in France, under the supervision of Dr. Didier Clouteau and Dr. Marc Bonnet. The manuscript can be downloaded from www.mssmat.ecp.fr/rubrique.php3?id rubrique=354.
Audiences: Everyone Is Invited
Contact: Evangeline Reyes