A two-stage inference algorithm for gene regulation network models

Alexandru Mizeranschi, Huiru Zheng, Paul Thompson, Werner Dubitzky

    Research output: Contribution to journalArticle

    Abstract

    Modelling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations. An important and unsolved problem in this area is the automated inference (reverseengineering) of dynamic mechanistic GRN models from gene-expression timecourse data. The conventional single-stage algorithm determines the values of all model parameters simultaneously, whereas recent two-stage algorithms can potentially improve the performance (accuracy) of single-stage approaches. The objective of this study is to compare the performance of the conventional singlestage and a novel version of the modern two-stage algorithm.We based this study on our implementation of a multi-swarm particle swarm optimisation process. A particular focus of this study is placed on the comparison of the computational performance of the single-stage vs. two-stage algorithm. Our results suggest that the 2-stage approach outperforms the single-stage methods by far in terms of model inference speed without loss of accuracy.

    LanguageEnglish
    Pages6-24
    Number of pages19
    JournalInternational Journal of Computational Biology and Drug Design
    Volume9
    Issue number1-2
    DOIs
    Publication statusPublished - 1 Jan 2016

    Fingerprint

    Gene Regulatory Networks
    Gene expression
    Genes
    Systems Biology
    Particle swarm optimization (PSO)
    Gene Expression

    Keywords

    • Gene regulation
    • Gene-regulatory network
    • Model inference
    • Reverse-engineering
    • System biology
    • Two-stage algorithm

    Cite this

    @article{fdfd07f17891490cb50971a796533893,
    title = "A two-stage inference algorithm for gene regulation network models",
    abstract = "Modelling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations. An important and unsolved problem in this area is the automated inference (reverseengineering) of dynamic mechanistic GRN models from gene-expression timecourse data. The conventional single-stage algorithm determines the values of all model parameters simultaneously, whereas recent two-stage algorithms can potentially improve the performance (accuracy) of single-stage approaches. The objective of this study is to compare the performance of the conventional singlestage and a novel version of the modern two-stage algorithm.We based this study on our implementation of a multi-swarm particle swarm optimisation process. A particular focus of this study is placed on the comparison of the computational performance of the single-stage vs. two-stage algorithm. Our results suggest that the 2-stage approach outperforms the single-stage methods by far in terms of model inference speed without loss of accuracy.",
    keywords = "Gene regulation, Gene-regulatory network, Model inference, Reverse-engineering, System biology, Two-stage algorithm",
    author = "Alexandru Mizeranschi and Huiru Zheng and Paul Thompson and Werner Dubitzky",
    year = "2016",
    month = "1",
    day = "1",
    doi = "10.1504/IJCBDD.2016.074981",
    language = "English",
    volume = "9",
    pages = "6--24",
    journal = "International Journal of Computational Biology and Drug Design",
    issn = "1756-0756",
    number = "1-2",

    }

    A two-stage inference algorithm for gene regulation network models. / Mizeranschi, Alexandru; Zheng, Huiru; Thompson, Paul; Dubitzky, Werner.

    In: International Journal of Computational Biology and Drug Design, Vol. 9, No. 1-2, 01.01.2016, p. 6-24.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - A two-stage inference algorithm for gene regulation network models

    AU - Mizeranschi, Alexandru

    AU - Zheng, Huiru

    AU - Thompson, Paul

    AU - Dubitzky, Werner

    PY - 2016/1/1

    Y1 - 2016/1/1

    N2 - Modelling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations. An important and unsolved problem in this area is the automated inference (reverseengineering) of dynamic mechanistic GRN models from gene-expression timecourse data. The conventional single-stage algorithm determines the values of all model parameters simultaneously, whereas recent two-stage algorithms can potentially improve the performance (accuracy) of single-stage approaches. The objective of this study is to compare the performance of the conventional singlestage and a novel version of the modern two-stage algorithm.We based this study on our implementation of a multi-swarm particle swarm optimisation process. A particular focus of this study is placed on the comparison of the computational performance of the single-stage vs. two-stage algorithm. Our results suggest that the 2-stage approach outperforms the single-stage methods by far in terms of model inference speed without loss of accuracy.

    AB - Modelling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations. An important and unsolved problem in this area is the automated inference (reverseengineering) of dynamic mechanistic GRN models from gene-expression timecourse data. The conventional single-stage algorithm determines the values of all model parameters simultaneously, whereas recent two-stage algorithms can potentially improve the performance (accuracy) of single-stage approaches. The objective of this study is to compare the performance of the conventional singlestage and a novel version of the modern two-stage algorithm.We based this study on our implementation of a multi-swarm particle swarm optimisation process. A particular focus of this study is placed on the comparison of the computational performance of the single-stage vs. two-stage algorithm. Our results suggest that the 2-stage approach outperforms the single-stage methods by far in terms of model inference speed without loss of accuracy.

    KW - Gene regulation

    KW - Gene-regulatory network

    KW - Model inference

    KW - Reverse-engineering

    KW - System biology

    KW - Two-stage algorithm

    UR - http://www.scopus.com/inward/record.url?scp=84960091289&partnerID=8YFLogxK

    U2 - 10.1504/IJCBDD.2016.074981

    DO - 10.1504/IJCBDD.2016.074981

    M3 - Article

    VL - 9

    SP - 6

    EP - 24

    JO - International Journal of Computational Biology and Drug Design

    T2 - International Journal of Computational Biology and Drug Design

    JF - International Journal of Computational Biology and Drug Design

    SN - 1756-0756

    IS - 1-2

    ER -