State-space Modelling of Replicated Dynamic Genetic Networks

Lotsi, Anani; Wit, Ernst

doi:10.9734/bjast/2016/28154

Cited by 2 publications

(5 citation statements)

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“…At the optimum turning parameter, we found Jun-B interacting directly with CASP4 through Jun-D; a result also supported by Beal et al (2005) . The unpenalized inference (Lotsi and Wit, 2012) A critical look at figure 3 reveals that AKT1 and MLC1 also occupy a crucial position.…”

Section: Resultsmentioning

confidence: 99%

“…Our method has resulted in relatively sparse networks as compared to (Lotsi and Wit, 2012). In all, the following genes were found to have the highest number of interactions in terms of inwards directed connections: TRAF5, C3X1, CASP4, CDK4 and IL3RA.…”

Section: Resultsmentioning

confidence: 99%

“…At the optimum turning parameter, we found Jun-B interacting directly with CASP4 through Jun-D; a result also supported by Beal et al (2005) . The unpenalized inference (Lotsi and Wit, 2012) approach has indicated that Jun B activates directly CDC2. This work also supports the same interaction.…”

Section: Resultsmentioning

confidence: 99%

“…In essence, we treated the data as a time series measurement data y tr , t = 1, 2, ..., 10 and r = 1, 2, ..., 44. Based on our previous work (Lotsi and Wit, 2012) and that of Andrea et al ( 2010), we assumed the dimension of the hidden state k to be 4. For each replicate, y t and θ t consist of 45 genes and 4 transcriptions factors respectively, each, measured at 10 different time points, i.e for each replicate r, y t and θ t are of dimension (45 by 10), (4 by 10) respectively.…”

Section: Applicationmentioning

confidence: 99%

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Network estimation in State Space Models with L1-regularization constraint

Lotsi¹,

Wit²

2017

JAS

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Abstract. Microarray technologies and related methods coupled with appropriate mathematical and statistical models have made it possible to identify dynamic regulatory networks by measuring time course expression levels of many genes simultaneously. However one of the challenges is the high-dimensional nature of such data coupled with the fact that these gene expression data are known not to include various biological process. As genomic interactions are highly structured, the aim was to derive a method for inferring a sparse dynamic network in a high dimensional data setting. The paper assumes that the observations are noisy measurements of gene expression in the form of mRNAs, whose dynamics can be described by some partially observed process.Résumé. .

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…At the optimum turning parameter, we found Jun-B interacting directly with CASP4 through Jun-D; a result also supported by Beal et al (2005) . The unpenalized inference (Lotsi and Wit, 2012) approach has indicated that Jun B activates directly CDC2. This work also supports the same interaction.…”

Section: Resultsmentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

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Network estimation in State Space Models with L1-regularization constraint

Lotsi¹,

Wit²

2017

JAS

Self Cite

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“…El Bakry et al [20] presented an approach, based on pairwise correlations, to infer GRN variable time delays without considering the effect of hidden variables. Estimation of known hidden variables (some identified proteins and transcription factors) has been investigated by state space modeling to infer hidden state variables from observations [7], [21]. The Unscented Kalman Filter is employed for estimation of both parameters and identified hidden variables when the nonlinear state space model is known [7].…”

Section: Introductionmentioning

confidence: 99%

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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State-space Modelling of Replicated Dynamic Genetic Networks

Cited by 2 publications

References 0 publications

Network estimation in State Space Models with L1-regularization constraint

Network estimation in State Space Models with L1-regularization constraint

Delayed and Hidden Variables Interactions in Gene Regulatory Networks

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