Literature DB >> 19631244

Reverse engineering and verification of gene networks: principles, assumptions, and limitations of present methods and future perspectives.

Feng He1, Rudi Balling, An-Ping Zeng.   

Abstract

Reverse engineering of gene networks aims at revealing the structure of the gene regulation network in a biological system by reasoning backward directly from experimental data. Many methods have recently been proposed for reverse engineering of gene networks by using gene transcript expression data measured by microarray. Whereas the potentials of the methods have been well demonstrated, the assumptions and limitations behind them are often not clearly stated or not well understood. In this review, we first briefly explain the principles of the major methods, identify the assumptions behind them and pinpoint the limitations and possible pitfalls in applying them to real biological questions. With regard to applications, we then discuss challenges in the experimental verification of gene networks generated from reverse engineering methods. We further propose an optimal experimental design for allocating sampling schedule and possible strategies for reducing the limitations of some of the current reverse engineering methods. Finally, we examine the perspectives for the development of reverse engineering and urge the need to move from revealing network structure to the dynamics of biological systems.

Mesh:

Year:  2009        PMID: 19631244     DOI: 10.1016/j.jbiotec.2009.07.013

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  29 in total

1.  Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Authors:  Allon M Klein; Linas Mazutis; Ilke Akartuna; Naren Tallapragada; Adrian Veres; Victor Li; Leonid Peshkin; David A Weitz; Marc W Kirschner
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

2.  Fundamental limitations of network reconstruction from temporal data.

Authors:  Marco Tulio Angulo; Jaime A Moreno; Gabor Lippner; Albert-László Barabási; Yang-Yu Liu
Journal:  J R Soc Interface       Date:  2017-02       Impact factor: 4.118

3.  The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods.

Authors:  Johannes Meisig; Nils Blüthgen
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2018-07-05       Impact factor: 6.237

4.  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 5.  The recurrent architecture of tumour initiation, progression and drug sensitivity.

Authors:  Andrea Califano; Mariano J Alvarez
Journal:  Nat Rev Cancer       Date:  2016-12-15       Impact factor: 60.716

6.  SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.

Authors:  Tyler Grimes; Somnath Datta
Journal:  J Stat Softw       Date:  2021-07-10       Impact factor: 6.440

7.  A parallel algorithm for reverse engineering of biological networks.

Authors:  Jason N Bazil; Feng Qi; Daniel A Beard
Journal:  Integr Biol (Camb)       Date:  2011-11-14       Impact factor: 2.192

8.  DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.

Authors:  Alex Greenfield; Aviv Madar; Harry Ostrer; Richard Bonneau
Journal:  PLoS One       Date:  2010-10-25       Impact factor: 3.240

9.  Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis.

Authors:  Mu-Shui Cao; Bing-Ya Liu; Wen-Tao Dai; Wei-Xin Zhou; Yi-Xue Li; Yuan-Yuan Li
Journal:  Am J Cancer Res       Date:  2015-08-15       Impact factor: 6.166

10.  Dynamic transcription factor activity and networks during ErbB2 breast oncogenesis and targeted therapy.

Authors:  M S Weiss; B Peñalver Bernabé; S Shin; S Asztalos; S J Dubbury; M D Mui; A D Bellis; D Bluver; D A Tonetti; J Saez-Rodriguez; L J Broadbelt; J S Jeruss; L D Shea
Journal:  Integr Biol (Camb)       Date:  2014-12       Impact factor: 2.192

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