State-space reduction and equivalence class sampling for a molecular self-assembly model

Daniel M. Packwood, Patrick Han, Taro Hitosugi

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving ‘target information’ from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of aMarkov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

    Original languageEnglish
    Article number18
    Pages (from-to)20
    Number of pages1
    JournalRoyal Society Open Science
    Volume3
    Issue number7
    DOIs
    Publication statusPublished - 2016 Jul 20

    Keywords

    • Markov chain Monte Carlo
    • Model reduction
    • Self-assembly

    ASJC Scopus subject areas

    • General

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