http://hdl.handle.net/2144/975 Department of Mechanical Engineering Wed, 19 Jun 2013 03:22:21 GMT 2013-06-19T03:22:21Z http://hdl.handle.net/2144/3455 Culturing Aerobic and Anaerobic Bacteria and Mammalian Cells with a Microfluidic Differential Oxygenator Lam, Raymond H. W.; Kim, Min-Cheol; Thorsen, Todd In this manuscript, we report on the culture of anaerobic and aerobic species within a disposable multilayer polydimethylsiloxane (PDMS) microfluidic device with an integrated differential oxygenator. A gas-filled microchannel network functioning as an oxygen−nitrogen mixer generates differential oxygen concentration. By controlling the relative flow rate of the oxygen and nitrogen input gases, the dissolved oxygen (DO) concentration in proximal microchannels filled with culture media are precisely regulated by molecular diffusion. Sensors consisting of an oxygen-sensitive dye embedded in the fluid channels permit dynamic fluorescence-based monitoring of the DO concentration using low-cost light-emitting diodes. To demonstrate the general utility of the platform for both aerobic and anaerobic culture, three bacteria with differential oxygen requirements (E. coli, A. viscosus, and F. nucleatum), as well as a model mammalian cell line (murine embryonic fibroblast cells (3T3)), were cultured. Growth characteristics of the selected species were analyzed as a function of eight discrete DO concentrations, ranging from 0 ppm (anaerobic) to 42 ppm (fully saturated). Thu, 11 Jun 2009 00:00:00 GMT http://hdl.handle.net/2144/3455 2009-06-11T00:00:00Z http://hdl.handle.net/2144/3225 Hematopoietic Gene Promoters Subjected to a Group-Combinatorial Study of DNA Samples: Identification of a Megakaryocytic Selective DNA Signature Hazony, Yehonathan; Lu, Jun; St. Hilaire, Cynthia; Ravid, Katya Identification of common sub-sequences for a group of functionally related DNA sequences can shed light on the role of such elements in cell-specific gene expression. In the megakaryocytic lineage, no one single unique transcription factor was described as linage specific, raising the possibility that a cluster of gene promoter sequences presents a unique signature. Here, the megakaryocytic gene promoter group, which consists of both human and mouse 5′ non-coding regions, served as a case study. A methodology for group-combinatorial search has been implemented as a customized software platform. It extracts the longest common sequences for a group of related DNA sequences and allows for single gaps of varying length, as well as double- and multiple-gap sequences. The results point to common DNA sequences in a group of genes that is selectively expressed in megakaryocytes, and which does not appear in a large group of control, random and specific sequences. This suggests a role for a combination of these sequences in cell-specific gene expression in the megakaryocytic lineage. The data also point to an intrinsic cross-species difference in the organization of 5′ non-coding sequences within the mammalian genomes. This methodology may be used for the identification of regulatory sequences in other lineages. Sat, 26 Aug 2006 00:00:00 GMT http://hdl.handle.net/2144/3225 2006-08-26T00:00:00Z http://hdl.handle.net/2144/3041 Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes Shen, Yang; Paschalidis, Ioannis Ch.; Vakili, Pirooz; Vajda, Sandor Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 Å ligand interface Cα root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods. Author SummaryProtein–protein interactions play a central role in various aspects of the structural and functional organization of the cell, and their elucidation is crucial for a better understanding of processes such as metabolic control, signal transduction, and gene regulation. Genomewide proteomics studies, primarily yeast two-hybrid assays, will provide an increasing list of interacting proteins, but only a small fraction of the potential complexes will be amenable to direct experimental analysis. Thus, it is important to develop computational docking methods that can elucidate the details of specific interactions at the atomic level. Protein–protein docking generally starts with a rigid body search that generates a large number of docked conformations with good shape, electrostatic, and chemical complementarity. The conformations are clustered to obtain a manageable number of models, but the current methods are unable to select the most likely structure among these models. Here we describe a refinement algorithm that, applied to the individual clusters, improves the quality of the models. The better models are suitable for higher-accuracy energy calculation, thereby increasing the chances that near-native structures can be identified, and thus the refinement increases the reliability of the entire docking algorithm. Fri, 10 Oct 2008 00:00:00 GMT http://hdl.handle.net/2144/3041 2008-10-10T00:00:00Z http://hdl.handle.net/2144/1490 EVALUATION OF HARMONIC MOTION ELASTOGRAPHY AND ACOUSTO-­OPTIC IMAGING FOR MONITORING LESION FORMATION BY HIGH INTENSITY FOCUSED ULTRASOUND Draudt, Andrew Bruce Malignant or benign tumors may be ablated with high‐intensity focused ultrasound (HIFU). This technique, known as focused ultrasound surgery (FUS), has been actively investigated for decades, but slow to be implemented and difficult to control due to lack of real‐time feedback during ablation. Two methods of imaging and monitoring HIFU lesions during formation were implemented simultaneously, in order to investigate the efficacy of each and to increase confidence in the detection of the lesion. The first, Acousto‐Optic Imaging (AOI) detects the increasing optical absorption and scattering in the lesion. The intensity of a diffuse optical field in illuminated tissue is mapped at the spatial resolution of an ultrasound focal spot, using the acousto‐optic effect. The second, Harmonic Motion Imaging (HMI), detects the changing stiffness in the lesion. The HIFU beam is modulated to force oscillatory motion in the tissue, and the amplitude of this motion, measured by ultrasound pulse‐echo techniques, is influenced by the stiffness. Experiments were performed on store‐bought chicken breast and freshly slaughtered bovine liver. The AOI results correlated with the onset and relative size of forming lesions much better than prior knowledge of the HIFU power and duration. For HMI, a significant artifact was discovered due to acoustic nonlinearity. The artifact was mitigated by adjusting the phase of the HIFU and imaging pulses. A more detailed model of the HMI process than previously published was made using finite element analysis. The model showed that the amplitude of harmonic motion was primarily affected by increases in acoustic attenuation and stiffness as the lesion formed and the interaction of these effects was complex and often counteracted each other. Further biological variability in tissue properties meant that changes in motion were masked by sample‐to‐sample variation. The HMI experiments predicted lesion formation in only about a quarter of the lesions made. In simultaneous AOI/HMI experiments it appeared that AOI was a more robust method for lesion detection. Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/2144/1490 2012-01-01T00:00:00Z