Sequence-based model of gap gene regulatory network

BMC Genomics

Research

Ivan Kulakovskiy¹  Vitaly Gursky²  Maria Samsonova³  Konstantin Kozlov³ 

[1] Engelhardt Institute of Molecular Biology, RAS, Vavilov 32, 119991, Moscow, Russia;Ioffe Physical-Technical Institute, RAS, Polytekhnicheskaya 26, 194021, St.Petersburg, Russia;St.Petersburg State Polytechnical University, Polytekhnicheskaya 29, 195251, St.Petersburg, Russia;

关键词: Identifiability Analysis;    Cleavage Cycle;    Basal Transcriptional Machinery;    Previous Modeling Result;    Segmentation Gene Expression;   

DOI  :  10.1186/1471-2164-15-S12-S6

来源: Springer

【 摘 要 】

BackgroundThe detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency.ResultsWe developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded.ConclusionsThe reconstructed topology of the gap gene network is in agreement with previous modeling results and data from literature. We showed that 1) the regulatory weights of transcription factor binding sites show very weak correlation with their PWM score; 2) sites with low regulatory weight are important for the model output; 3) functional important sites are not exclusively located in cis-regulatory elements, but are rather dispersed through regulatory region. It is of importance that some of the sites with high functional impact in hb, Kr and kni regulatory regions coincide with strong sites annotated and verified in Dnase I footprint assays.

【 授权许可】

Unknown   
© Kozlov et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.