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EGIA–evolutionary optimisation of gene regulatory networks, an integrative approach

Sîrbu, Alina and Crane, Martin and Ruskin, Heather J. (2013) EGIA–evolutionary optimisation of gene regulatory networks, an integrative approach. In: 5th Workshop on Complex Networks - CompleNet 2014, 12-14 Mar 2014, Bologna, Italy. ISBN 978-3-319-05400-1

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Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored, to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Additional Information:Corresponding Author: Alina Sırbu, Institute for Scientific Interchange Foundation, Turin, Italy,, and SCI-SYM, DCU
Uncontrolled Keywords:Gene Regulatory Networks (GRNs); Noise; Time Series
Subjects:Biological Sciences > Bioinformatics
Computer Science > Machine learning
Computer Science > Artificial intelligence
Physical Sciences > Statistical physics
Computer Science > Computer simulation
DCU Faculties and Centres:Research Initiatives and Centres > Scientific Computing and Complex Systems Modelling (Sci-Sym)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in:Complex Networks V. Studies in Computational Intelligence 549. Springer International Publishing. ISBN 978-3-319-05400-1
Publisher:Springer International Publishing
Official URL:
Copyright Information:© 2014 Springer The original publication is available at
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:IRCSET EMBARK Programme, EU RD contract IST-265432.
ID Code:19946
Deposited On:15 May 2014 11:09 by Martin Crane. Last Modified 14 Oct 2016 17:02

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