Modular Composition of Gene Transcription Networks

Andras Gyorgy, Domitilla Del Vecchio

    Research output: Contribution to journalArticle

    Abstract

    Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.

    Original languageEnglish (US)
    Article numbere1003486
    JournalPLoS Computational Biology
    Volume10
    Issue number3
    DOIs
    StatePublished - Jan 1 2014

    Fingerprint

    Synthetic Biology
    Systems Biology
    Gene Regulatory Networks
    Transcription
    Homeostasis
    transcription (genetics)
    Genes
    Guidelines
    Gene
    Module
    gene
    Chemical analysis
    matrix
    genes
    topology
    synthetic biology
    autoregulation
    Scaling
    Internal
    Interconnection

    ASJC Scopus subject areas

    • Ecology, Evolution, Behavior and Systematics
    • Modeling and Simulation
    • Ecology
    • Molecular Biology
    • Genetics
    • Cellular and Molecular Neuroscience
    • Computational Theory and Mathematics

    Cite this

    Modular Composition of Gene Transcription Networks. / Gyorgy, Andras; Del Vecchio, Domitilla.

    In: PLoS Computational Biology, Vol. 10, No. 3, e1003486, 01.01.2014.

    Research output: Contribution to journalArticle

    Gyorgy, Andras ; Del Vecchio, Domitilla. / Modular Composition of Gene Transcription Networks. In: PLoS Computational Biology. 2014 ; Vol. 10, No. 3.
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