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Numbers on the edges: A simplified and scalable method for quantifying the Gene Regulation Function

Authors

  • Raul Fernandez-Lopez,

    1. Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC-IDICAN, Cardenal Herrera Oria s/n, 39011 Santander, Spain
    2. Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
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  • Irene del Campo,

    1. Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC-IDICAN, Cardenal Herrera Oria s/n, 39011 Santander, Spain
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  • Raúl Ruiz,

    1. Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC-IDICAN, Cardenal Herrera Oria s/n, 39011 Santander, Spain
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  • Val Lanza,

    1. Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC-IDICAN, Cardenal Herrera Oria s/n, 39011 Santander, Spain
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  • Luis Vielva,

    1. Departamento de Ingeniería de Comunicaciones, Universidad de Cantabria, Avda. de Los Castros, 39005 Santander, Spain
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  • Fernando de la Cruz

    Corresponding author
    1. Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), Universidad de Cantabria-CSIC-IDICAN, Cardenal Herrera Oria s/n, 39011 Santander, Spain
    • C. Herrera Oria s/n, Santander, Cantabria, 39011 Spain
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Abstract

The gene regulation function (GRF) provides an operational description of a promoter behavior as a function of the concentration of one of its transcriptional regulators. Behind this apparently trivial definition lies a central concept in biological control: the GRF provides the input/output relationship of each edge in a transcriptional network, independently from the molecular interactions involved. Here we discuss how existing methods allow direct measurement of the GRF, and how several trade-offs between scalability and accuracy have hindered its application to relatively large networks. We discuss the theoretical and technical requirements for obtaining the GRF. Based on these requirements, we introduce a simplified and easily scalable method that is able to capture the significant parameters of the GRF. The GRF is able to predict the behavior of a simple genetic circuit, illustrating how addressing the quantitative nature of gene regulation substantially increases our comprehension on the mechanisms of gene control.

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