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Midterm for Bio98B A1 (1) Enzymes accelerate reactions by
Midterm for Bio98B A1 (1) Enzymes accelerate reactions by

... F1 (2) You want to make a pH = 7 phosphate buffer. You have 0.1M KH2PO4. What concentration of K2HPO4 do you need? HPO4-2 + H+ ...
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... Proteins are the main structural and growth components of cells in tissues such as skin, hair, muscle and blood. Other proteins serve in regulatory capacity as enzymes and hormones. Proteins always contain nitrogen in addition to carbon, hydrogen and oxygen. Phosphorus and sulfur are also found in m ...
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... alanine, be specific. Think “R-Group” (you will have to look this up). Cysteine’s R-group is CH2-SH while Alanine is CH3 14. Diagram the joining of 2 amino acids together through dehydration synthesis to form a dipeptide with a peptide bond. Highlight the peptide bond. ...
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G Protein Coupled Receptors

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Friends or Foes? - Princeton University

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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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