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chemeqohnotes18f2005
chemeqohnotes18f2005

... activation energy: energy needed to start a reaction ...
Review session for exam-I
Review session for exam-I

... The state of ionization of several amino acid side chains is affected by pH and the activity of many enzymes requires that certain of the amino acid residue side chains be in a specific ionization state. ...
An endosperm enzyme catalyzes the formation of phosphotriester
An endosperm enzyme catalyzes the formation of phosphotriester

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A-level Paper 3 Practice Paper 3 - A
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Metabolic Flux Profiling of Reaction Modules in Liver Drug
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ppt - UCLA Chemistry and Biochemistry

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... photosynthesis. An understanding of redox chemistry is essential in the design of new kinds of batteries, increasing efficiency in fuel combustion, the prevention of corrosion, etc. Recall from Chem 161 (Sec. 4.5 in Silberberg), the oxidation number of an atom is the ―apparent‖ charge the atom would ...
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Lec 15: Nitrogen in biochemistry

... Mo‐dependent Nitrogenase • Nitrogenases are multi‐subunit polypeptide complex coded by the nif genes • The most studied type of nitrogenase is Mo‐dependent nitrogenase • Other types of nitrogenases include Vanadium and iron only • Different nitrogenases produce different levels of byproduct H2. • M ...
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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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