Literature DB >> 19348645

Replaying the evolutionary tape: biomimetic reverse engineering of gene networks.

Daniel Marbach1, Claudio Mattiussi, Dario Floreano.   

Abstract

In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge into the reverse-engineering procedure, thus biasing the search toward biologically plausible solutions. To this end, we propose an evolutionary method that abstracts and mimics the natural evolution of gene regulatory networks. Our method can be used with a wide range of nonlinear dynamical models. This allows us to explore novel model types such as the log-sigmoid model introduced here. We apply the biomimetic method to a gold-standard dataset from an in vivo gene network. The obtained results won a reverse engineering competition of the second DREAM conference (Dialogue on Reverse Engineering Assessments and Methods 2007, New York, NY).

Entities:  

Mesh:

Year:  2009        PMID: 19348645     DOI: 10.1111/j.1749-6632.2008.03944.x

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  11 in total

Review 1.  New insights into bacterial adaptation through in vivo and in silico experimental evolution.

Authors:  Thomas Hindré; Carole Knibbe; Guillaume Beslon; Dominique Schneider
Journal:  Nat Rev Microbiol       Date:  2012-03-27       Impact factor: 60.633

2.  Revealing strengths and weaknesses of methods for gene network inference.

Authors:  Daniel Marbach; Robert J Prill; Thomas Schaffter; Claudio Mattiussi; Dario Floreano; Gustavo Stolovitzky
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-22       Impact factor: 11.205

3.  Reverse engineering validation using a benchmark synthetic gene circuit in human cells.

Authors:  Taek Kang; Jacob T White; Zhen Xie; Yaakov Benenson; Eduardo Sontag; Leonidas Bleris
Journal:  ACS Synth Biol       Date:  2013-03-28       Impact factor: 5.110

Review 4.  Integrated inference and analysis of regulatory networks from multi-level measurements.

Authors:  Christopher S Poultney; Alex Greenfield; Richard Bonneau
Journal:  Methods Cell Biol       Date:  2012       Impact factor: 1.441

5.  Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.

Authors:  Matthieu Vignes; Jimmy Vandel; David Allouche; Nidal Ramadan-Alban; Christine Cierco-Ayrolles; Thomas Schiex; Brigitte Mangin; Simon de Givry
Journal:  PLoS One       Date:  2011-12-27       Impact factor: 3.240

6.  Assessment of network inference methods: how to cope with an underdetermined problem.

Authors:  Caroline Siegenthaler; Rudiyanto Gunawan
Journal:  PLoS One       Date:  2014-03-06       Impact factor: 3.240

7.  Mapping functional transcription factor networks from gene expression data.

Authors:  Brian C Haynes; Ezekiel J Maier; Michael H Kramer; Patricia I Wang; Holly Brown; Michael R Brent
Journal:  Genome Res       Date:  2013-05-01       Impact factor: 9.043

8.  A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

Authors:  Xiaomeng Zhang; Bin Shao; Yangle Wu; Ouyang Qi
Journal:  PLoS One       Date:  2013-09-19       Impact factor: 3.240

9.  Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

Authors:  Abhinandan Khan; Sudip Mandal; Rajat Kumar Pal; Goutam Saha
Journal:  Scientifica (Cairo)       Date:  2016-05-19

Review 10.  Data- and knowledge-based modeling of gene regulatory networks: an update.

Authors:  Jörg Linde; Sylvie Schulze; Sebastian G Henkel; Reinhard Guthke
Journal:  EXCLI J       Date:  2015-03-02       Impact factor: 4.068

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