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PDF | 816.8KB - New Jersey Center for Teaching and Learning
PDF | 816.8KB - New Jersey Center for Teaching and Learning

... 7. What sequence of events would occur in a lake as winter progresses, if solid water were more dense the liquid water, as is the case with most molecules? 8. Describe the two parts that make up a solution. 9. Living organisms use buffers to help maintain a steady pH in their internal environment. H ...
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Packet #7- Chemical Reactions

... New substances are formed by chemical reactions. When elements react together to form compounds their atoms join to other atoms using chemical bonds. For example, iron and sulfur react together to form a compound called iron sulfide. Compounds usually have different properties from the elements they ...
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An Introduction to Redox

... Redox reactions are sometimes divided into half‐reactions to separate and clarify the electron transfer  process.  The species that loses electrons is said to be oxidized  and is often referred to as the  reducing agent.  The species that gains electrons is said to be reduced and is referred to as t ...
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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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