Preparation of lithium amalgam

Lithium amalgam is useful in effecting Wurtz type intramolecular and intermolecular coupling reactions.

An inexpensive all glass sealed stirrer

An inexpensive all glass sealed stirrer may be constructed using a surgical syringe of about 10-ml capacity.

Physically consistent simulation of mesoscale chemical kinetics: The non-negative FIS-alpha method

Biochemical pathways involving chemical kinetics in medium concentrations (i.e., at mesoscale) of the reacting molecules can be approximated as chemical Langevin equations (CLE) systems. We address the physically consistent non-negative simulation of the CLE sample paths as well as the issue of non-Lipschitz diffusion coefficients when a species approaches depletion and any stiffness due to faster reactions. The non-negative Fully Implicit Stochastic alpha (FIS alpha) method in which stopped reaction channels due to depleted reactants are deleted until a reactant concentration rises again, for non-negativity preservation and in which a positive definite Jacobian is maintained to deal with possible stiffness, is proposed and analysed. The method is illustrated with the computation of active Protein Kinase C response in the Protein Kinase C pathway. (C) 2011 Elsevier Inc. All rights reserved.

Chemical Shifts to Metabolic Pathways: Identifying Metabolic Pathways Directly from a Single 2D NMR Spectrum

Identifying cellular processes in terms of metabolic pathways is one of the avowed goals of metabolomics studies. Currently, this is done after relevant metabolites are identified to allow their mapping onto specific pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. We propose here a new method: ChemSMP (Chemical Shifts to Metabolic Pathways), which facilitates rapid analysis by identifying the active metabolic pathways directly from chemical shifts obtained from a single two-dimensional (2D) C-13-H-1] correlation NMR spectrum without the need for identification and assignment of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a ``uniqueness score'' and a ``coverage score''. Our method is demonstrated on metabolic pathways data from the Small Molecule Pathway Database (SMPDB) and chemical shifts from the Human Metabolome Database (HMDB). Benchmarks show that ChemSMP has a positive prediction rate of >90% in the presence of deduttered data and can sustain the same at 60-70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of pathways. When used on data obtained from the cell lysate of an unexplored oncogenic cell line, it revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells. Our unique tool is thus expected to significantly enhance analysis of NMIR-based metabolomics data by reducing existing impediments.