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基因调控网络: -数学模型与仿真
基因调控网络: -数学模型与仿真

... – Can we predict the evolution of the network? – How to predict the evolution of the network? ...
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Molekylært Simulering

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D. E. Shaw Research is seeking postdoctoral fellows of exceptional... Postdoctoral Fellowships at D. E. Shaw Research

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Abstract Pengembangan perangkat lunak simulasi komputer

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Early Research Award

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... The association of molecules is of central importance in biology and pharmacology. Methods to predict the affinity of such associations would be of great practical value. However, entropic changes upon binding are difficult to estimate and are often neglected. Here we apply our recently developed me ...
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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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