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Microsoft Word
Microsoft Word

... reductive removal of aliphatic nitro group has been of interest in organic chemistry, because the nitro group serves as an excellent activating group for C-C bond formation in the molecule. With this fact we explored a new method for reductive removal of -NO2 group. A number of examples were studied ...
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Ch. 6: Chemical Reactions Study Guide
Ch. 6: Chemical Reactions Study Guide

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Chemistry - University of Kashmir

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Syllabus - Chemistry

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Biotransformation Problem Statement - ACE

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Problem Set 2 - University of Ottawa

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... in the same conformation) and the binding of each ligand increases the probability that all subunits in that molecule are converted to the R-state (with a high activity). All-or-none model. 1.5.4 The interplay between these different ligand-binding sites is mediated primarily by changes in quaternar ...
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Multi-state modeling of biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behaviour of biological molecules or complexes that can adopt a large number of possible functional states.Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: The problem of how to describe and specify a multi-state system (the ""specification problem"") and the problem of how to use a computer to simulate the progress of the system over time (the ""computation problem""). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states, and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators further fall into two categories: Non-spatial simulators such as StochSim, DYNSTOC, RuleMonkey, and NFSim and spatial simulators, including Meredys, SRSim and MCell. Modelers can thus choose from a variety of tools; the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.
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