Nonlinear dynamics of eucaryotic pyruvate dehydrogenase multienzyme complex: Decarboxylation rate, oscillations, and multiplicity

Pyruvate conversion to acetyl-CoA by the pyruvate dehydrogenase (PDH) multienzyme complex is known as a key node in affecting the metabolic fluxes of animal cell culture. However, its possible role in causing possible nonlinear dynamic behavior such as oscillations and multiplicity of animal cells has received little attention. In this work, the kinetic and dynamic behavior of PDH of eucaryotic cells has been analyzed by using both in vitro and simplified in vivo models. With the in vitro model the overall reaction rate (v(1)) of PDH is shown to be a nonlinear function of pyruvate concentration, leading to oscillations under certain conditions. All enzyme components affect v, and the nonlinearity of PDH significantly, the protein X and the core enzyme dihydrolipoamide acyltransferase (E2) being mostly predominant. By considering the synthesis rates of pyruvate and PDH components the in vitro model is expanded to emulate in vivo conditions. Analysis using the in vivo model reveals another interesting kinetic feature of the PDH system, namely, multiple steady states. Depending on the pyruvate and enzyme levels or the operation mode, either a steady state with high pyruvate decarboxylation rate or a steady state with significantly lower decarboxylation rate can be achieved under otherwise identical conditions. In general, the more efficient steady state is associated with a lower pyruvate concentration. A possible time delay in the substrate supply and enzyme synthesis can also affect the steady state to be achieved and lead's to oscillations under certain conditions. Overall, the predictions of multiplicity for the PDH system agree qualitatively well with recent experimental observations in animal cell cultures. The model analysis gives some hints for improving pyruavte metabolism in animal cell culture.

SEC-MALDI-TOF mass spectral characterization of a hyperbranched polyether prepared via melt transetherification

An AB(2) monomer, 1-(2-hydroxyethoxy)-3,5-bis-(methoxymethyl)-2,4,6-trimethylbenzene, was synthesized from mesitol and melt-polycondensed in the presence of an acid catalyst via a transetherification process at 145-150 degreesC to yield a soluble, moderately high molecular weight hyperbranched polyether. The degree of branching in the polymer was calculated to be 0.78 by a comparison of its NMR spectrum with that of an appropriately designed model compound. The weight-average molecular weight of the hyperbranched polymer was determined to be 64,600 (weight-average molecular weight/number-average molecular weight = 5.2) by size exclusion chromatography (SEC) in CHCl3, with polystyrene standards. The origin of the broad molecular weight distribution, which could either be intrinsic to such hyperbranched structures or be due to structural heterogeneity, was further probed by the fractionation of the samples by SEC and by the subjection of each fraction to matrix-assisted laser desorption/ionization time-of-flight mass spectral analysis. The mass spectral analysis suggested the presence of two primary types of species: one corresponding to the simple branched structure and the other to macrocyclics. Interestingly, from the relative intensities of the two peaks, it was apparent that cyclization became favorable at higher conversions in the melt transetherification process. (C) 2002 Wiley Periodicals, Inc.

Structural basis for poor uracil excision from hairpin DNA

Two-dimensional NMR and molecular dynamics simulations have been used to determine the three-dimensional structures of two hairpin DNA structures: d-CTAGAG GATCCUTTTGGATCCT (abbreviated as U1-hairpin) and d-CTAGAGGATCCTTUTGGATCCT (abbreviated as U3-hairpin). The (1) H resonances of both of these hairpin structures have been assigned almost completely. NMR restrained molecular dynamics and energy minimization procedures have been used to describe the three-dimensional structures of these hairpins. This study and concurrent NMR structural studies on two other d-CTAGAGGA TCCTUTTGGATCCT (abbreviated as U2-hairpin) and d-CTAGAGGATCCTTTUGGATCCT (abbreviated as U4-hairpin) have shed light upon various interactions reported between Echerichia coli uracil DNA glycosylase (UDG) and uracil-containing DNA. The backbone torsion angles, which partially influence the local conformation of U12 and U14 in U1 and U3-hairpins, respectively, are probably locked in the trans conformation as in the case of U-13 in the U2-hairpin. Such a stretched-out backbone conformation in the vicinity of U-12 and U-14 is thought to be the reason why the K-m value is poor for U1- and U3-hairpins as it is for the U2-hairpin. Furthermore, the bases U-12 and U-14 in both U1- and U3-hairpins adopt an anti conformation, in contrast with the base conformation of U-13 in the U2-hairpin, which adopts a syn conformation. The clear discrepancy observed in the U-base orientation with respect to the sugar moieties could explain why the V-max value is 10- to 20-fold higher for the U1- and U3-hairpins compared with the U2-hairpin. Taken together, these observations support our interpretation that the unfavourable backbone results in a poor K-m value, whereas the unfavourable nucleotide conformation results in a poor V-max value. These two parameters therefore make the U1- and U3-hairpins better substrates for UDG compared with the U2-hairpin, as reported earlier [Kumar, N. V. & Varshney, U. (1997) Nucleic Acids Res. 25, 2336-2343.].

Synthesis, properties, and comparative analyses on the chain flexibility of an ultrastable vinyl polyperoxide

The most stable vinyl polyperoxide, namely, poly(indene peroxide) (PINDP), an alternating copolymer of indene and oxygen, was synthesized by the oxidative polymerization of indene. It was characterized by H-1-NMR and C-13-NMR, Fourier transform infrared, differential scanning calorimetry, direct pyrolysis mass spectrometry, and gas chromatography mass spectrometry studies. The overall activation energy for the degradation and the glass-transition temperature of PINDP were 47 kcal/mol and 327 K respectively, which were very high compared to those of other vinyl polyperoxides. (C) 2002 Wiley Periodicals, Inc.

Techniques for realistic visualization of fluids: A survey

Visualization of fluids has wide applications in science, engineering and entertainment. Various methodologies Of visualizing fluids have evolved which emphasize on capturing different aspects of the fluids accurately. In this survey the existing methods for realistic visualization of fluids are reviewed. The approaches are classified based on the key concept they rely on for fluid modeling. This classification allows for easy selection of the method to be adopted for visualization given an application. It also enables identification of alternative techniques for fluid modeling.

Precoder Designs for MIMO Broadcast Channels with Imperfect CSI

[1] D. Tse and P. Viswanath, Fundamentals of Wireless Communication.Cambridge University Press, 2006. [2] H. Bolcskei, D. Gesbert, C. B. Papadias, and A.-J. van der Veen, Spacetime Wireless Systems: From Array Processing to MIMO Communications.Cambridge University Press, 2006. [3] Q. H. Spencer, C. B. Peel, A. L. Swindlehurst, and M. Haardt, “An introduction to the multiuser MIMO downlink,” IEEE Commun. Mag.,vol. 42, pp. 60–67, Oct. 2004. [4] K. Kusume, M. Joham,W. Utschick, and G. Bauch, “Efficient tomlinsonharashima precoding for spatial multiplexing on flat MIMO channel,”in Proc. IEEE ICC’2005, May 2005, pp. 2021–2025. [5] R. Fischer, C. Windpassinger, A. Lampe, and J. Huber, “MIMO precoding for decentralized receivers,” in Proc. IEEE ISIT’2002, 2002, p.496. [6] M. Schubert and H. Boche, “Iterative multiuser uplink and downlink beamforming under SINR constraints,” IEEE Trans. Signal Process.,vol. 53, pp. 2324–2334, Jul. 2005. [7] ——, “Solution of multiuser downlink beamforming problem with individual SINR constraints,” IEEE Trans. Veh. Technol., vol. 53, pp.18–28, Jan. 2004. [8] A. Wiesel, Y. C. Eldar, and Shamai, “Linear precoder via conic optimization for fixed MIMO receivers,” IEEE Trans. Signal Process., vol. 52,pp. 161–176, Jan. 2006. [9] N. Jindal, “MIMO broadcast channels with finite rate feed-back,” in Proc. IEEE GLOBECOM’2005, Nov. 2005. [10] R. Hunger, F. Dietrich, M. Joham, and W. Utschick, “Robust transmit zero-forcing filters,” in Proc. ITG Workshop on Smart Antennas, Munich,Mar. 2004, pp. 130–137. [11] M. B. Shenouda and T. N. Davidson, “Linear matrix inequality formulations of robust QoS precoding for broadcast channels,” in Proc.CCECE’2007, Apr. 2007, pp. 324–328. [12] M. Payaro, A. Pascual-Iserte, and M. A. Lagunas, “Robust power allocation designs for multiuser and multiantenna downlink communication systems through convex optimization,” IEEE J. Sel. Areas Commun.,vol. 25, pp. 1392–1401, Sep. 2007. [13] M. Biguesh, S. Shahbazpanahi, and A. B. Gershman, “Robust downlink power control in wireless cellular systems,” EURASIP Jl. Wireless Commun. Networking, vol. 2, pp. 261–272, 2004. [14] B. Bandemer, M. Haardt, and S. Visuri, “Liner MMSE multi-user MIMO downlink precoding for users with multple antennas,” in Proc.PIMRC’06, Sep. 2006, pp. 1–5. [15] J. Zhang, Y. Wu, S. Zhou, and J. Wang, “Joint linear transmitter and receiver design for the downlink of multiuser MIMO systems,” IEEE Commun. Lett., vol. 9, pp. 991–993, Nov. 2005. [16] S. Shi, M. Schubert, and H. Boche, “Downlink MMSE transceiver optimization for multiuser MIMO systems: Duality and sum-mse minimization,”IEEE Trans. Signal Process., vol. 55, pp. 5436–5446, Nov.2007. [17] A. Mezghani, M. Joham, R. Hunger, and W. Utschick, “Transceiver design for multi-user MIMO systems,” in Proc. WSA 2006, Mar. 2006. [18] R. Doostnejad, T. J. Lim, and E. Sousa, “Joint precoding and beamforming design for the downlink in a multiuser MIMO system,” in Proc.WiMob’2005, Aug. 2005, pp. 153–159. [19] N. Vucic, H. Boche, and S. Shi, “Robust transceiver optimization in downlink multiuser MIMO systems with channel uncertainty,” in Proc.IEEE ICC’2008, Beijing, China, May 2008. [20] A. Ben-Tal and A. Nemirovsky, “Selected topics in robust optimization,”Math. Program., vol. 112, pp. 125–158, Feb. 2007. [21] D. Bertsimas and M. Sim, “Tractable approximations to robust conic optimization problems,” Math. Program., vol. 107, pp. 5–36, Jun. 2006. [22] P. Ubaidulla and A. Chockalingam, “Robust Transceiver Design for Multiuser MIMO Downlink,” in Proc. IEEE Globecom’2008, New Orleans, USA, Dec. 2008, to appear. [23] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004. [24] G. H. Golub and C. F. V. Loan, Matrix Computations. The John Hopkins University Press, 1996.