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Published September 2014 | Published + Supplemental Material
Journal Article Open

Multi-state Modeling of Biomolecules


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 κ-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.

Additional Information

© 2014 Stefan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published September 25, 2014. Funding: This work was supported by CRCNS grant DA030749 from NIH (to MBK and TJS) and the Gordon and Betty Moore Foundation ''Moore Center for Integrative Study of Cell Regulation'' at Caltech. MIS has been supported by a long-term fellowship from EMBO. TMB and TJS acknowledge funding from the Center for Theoretical Biological Physics (NSF PHY-0822283), NIH (P41-GM103712, MH079076, GM086883), and HHMI. The funders had no role in the preparation of this manuscript. Competing Interests: The authors have declared that no competing interests exist. The version history of the text file and the peer reviews (and response to reviews) are available as supporting information in Text S1 and S2.

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Published - journal.pcbi.1003844.pdf

Supplemental Material - journal.pcbi.1003844.s001.docx

Supplemental Material - journal.pcbi.1003844.s002.xml

Supplemental Material - journal.pcbi.1003844.s003.xml


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September 15, 2023
September 15, 2023