Premium
Numbers on the edges: A simplified and scalable method for quantifying the Gene Regulation Function
Author(s) -
FernandezLopez Raul,
del Campo Irene,
Ruiz Raúl,
Lanza Val,
Vielva Luis,
de la Cruz Fernando
Publication year - 2010
Publication title -
bioessays
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.175
H-Index - 184
eISSN - 1521-1878
pISSN - 0265-9247
DOI - 10.1002/bies.200900164
Subject(s) - scalability , function (biology) , computer science , gene regulatory network , computational biology , gene , transcriptional regulation , enhanced data rates for gsm evolution , simple (philosophy) , distributed computing , biology , biological system , genetics , transcription factor , artificial intelligence , gene expression , philosophy , epistemology , database
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.