Multi-state modeling of biomolecules | Wikipedia audio article
Channel: wikipedia tts
Date: 2019-05-02

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomolecules


00:02:27 1 Introduction
00:02:37 1.1 Multi-state biomolecules in signal transduction
00:04:20 1.2 Examples of combinatorial explosion
00:10:13 1.3 Specification vs computation
00:12:07 2 The specification problem
00:12:18 2.1 Explicit specification
00:14:09 2.2 Rule-based model specification
00:24:37 3 The computation problem
00:25:20 3.1 Population-based rule evaluation
00:30:12 3.2 Particle-based rule evaluation
00:32:34 3.3 Non-spatial particle-based methods
00:36:28 3.4 Spatial particle-based methods
00:40:16 4 Examples of multi-state models in biology
00:40:43 5 See also



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SUMMARY
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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.