Multi-state Modeling of Biomolecules

Melanie I. Stefan*, Thomas M. Bartol, Terrence J. Sejnowski, Mary B. Kennedy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1003844
Number of pages9
JournalPLoS Computational Biology
Volume10
Issue number9
DOIs
Publication statusPublished - 25 Sep 2014

Keywords

  • BIOLOGY MARKUP LANGUAGE
  • RULE-BASED MODELS
  • SYSTEMS BIOLOGY
  • STOCHASTIC SIMULATION
  • BIOCHEMICAL SYSTEMS
  • SIGNAL-TRANSDUCTION
  • DIFFUSION
  • MOLECULES
  • COMPLEXES
  • BIONETGEN

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