Time Series Prediction of Gene Expression in the SOS DNA Repair Network of Escherichia coli Bacterium Using Neuro-Fuzzy Networks

Manshaei, Roozbeh; Bidari, Pooya Sobhe; Alirezaie, Javad; Malboobi, Mohammad Ali

doi:10.1007/978-3-540-92841-6_458

Cited by 1 publication

(2 citation statements)

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“…The model of lexA from Fig. 7.a identifies the previously reported regulations of target gene lexA by uvrD, umuD, lexA and [31], [32], [33]. The model of recA from Fig.…”

Section: Experimental Results On the Escherichia Coli Sos Dna Rementioning

confidence: 84%

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Delayed and Hidden Variables Interactions in Gene Regulatory Networks

Kordmahalleh

Sefidmazgi

Homaifar

et al. 2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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Reverse Engineering of Gene Regulatory Networks (GRN), i.e. finding appropriate mathematical models to understand complex cellular systems, can be used in disease diagnosis, treatment, and drug design. There are fundamental gaps in the construction of GRN with regard to modeling of hidden/delayed interactions. Addressing these deficiencies is critical to understanding complex intracellular processes and enabling full use of the vast and ever-growing amount of available genomic data. Current modeling strategies either ignore or oversimplify time delays resulted from transcription and translation processes during gene expression. In addition, many research works do not account hidden variables such as transcription factors, repressors, small metabolites, DNA, microRNA species that regulate themselves and other genes but are not readily detectable on microarray experiments. To capture the effect of these parameters, in this paper, we utilize our developed Partially Connected Artificial Neural Networks with Evolvable Topology (PANNET) to find a more comprehensive model of GRN by considering the effects of unknown hidden variables and different time delays. This method is innovative, since the structure of the network has memory and internal states, which can model the unknown hidden variables and time delays. We furthermore use a new evolutionary optimization based on variable-length Genetic Algorithm (GA) to find a sparse structure of PANNET to predict the gene expression levels accurately. Finally we demonstrate the capability of PANNET in constructing GRN, including the effect of different delays and unknown hidden variables through modeling of E. coli SOS inducible DNA repair system.

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“…The model of lexA from Fig. 7.a identifies the previously reported regulations of target gene lexA by uvrD, umuD, lexA and [31], [32], [33]. The model of recA from Fig.…”

Section: Experimental Results On the Escherichia Coli Sos Dna Rementioning

confidence: 84%

“…In this way networks are tested by the data with different time point comparing to the ones used as training. This can lead us to more accurate and realistic evaluation from our networks [31].…”

Section: Experimental Results On the Escherichia Coli Sos Dna Rementioning

confidence: 94%