**Multi-state Modeling of Biomolecules**

MI Stefan and TM Bartol and TJ Sejnowski and MB Kennedy, PLOS COMPUTATIONAL BIOLOGY, 10, e1003844 (2014).

DOI: 10.1371/journal.pcbi.1003844

Multi-state modeling of biomolecules refers to a series of techniques
used to represent and compute the behavior 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 k-calculus
**1**, BioNetGen **2-5**, the Allosteric Network Compiler **6**, and others
**7,8**. 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 **9,10**. Given current computing technology,
particle-based methods are sometimes the only possible option. Particle-
based simulators fall into two further categories: nonspatial
simulators, such as StochSim **11**, DYNSTOC **12**, RuleMonkey **9,13**, and
the Network-Free Stochastic Simulator (NFSim) **14**, and spatial
simulators, including Meredys **15**, SRSim **16,17**, and MCell **18-20**.
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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