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Signal Amplification by the Generation of Protein Polymer Networks
Signal Amplification by the Generation of Protein Polymer Networks

... Traditional bacterial detecting technologies, such as the plate count technique, enzyme-linked immunosorbent assay1 (ELISA), biochemical tests, and the polymerase chain reaction (PCR) are time-consuming and labor-intensive. The protein conglomeration based amplification method (PCBAM) might change t ...
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... Inferring direct couplings to unveil coevolutionary signals in protein 3D structure, interactions and recognition in signaling networks. Modern sequencing technologies provide us with a rich source of data about the evolutionary history of proteins. Inferring a joint probability distribution of amin ...
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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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