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Journal of Physical and Chemical Reference Data
Journal of Physical and Chemical Reference Data

... all the possible heats of reaction. To get around this problem we define for each substance a standard reaction and tabulate its associated heat of reaction. These reactions and their associated heats of reaction can then be used to calculate the heats of other reactions. This standard reaction is k ...
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... domain of humans and E-coli illustrating that the database available on Apicaeae and Fabaceae family are sparsely populated with sequences. FFPred predicted its functions in 2 categories: 1) Biological Process: phosphate-containing compound metabolic process with a probability of 95.9% and 2) Molecu ...
hexose mono phosphate shunt
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... • Reduced glutathione, a tripeptide-thiol (γglutamylcysteinylglycine) present in most cells, can chemically detoxify hydrogen peroxide ...
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... • Reduced glutathione, a tripeptide-thiol (γglutamylcysteinylglycine) present in most cells, can chemically detoxify hydrogen peroxide ...
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Computational Biochemistry - Structural Bioinformatics and
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Poster - Department of Computer Science

... based on angular rotation and translation capabilities. Furthermore, the crossover operations in current generation are enhanced by the use of the best parents selected from previous generations. In addition, we employ a knowledge-based novel energy function, 3DIGARS3.0, which can differentiate the ...
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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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