A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle

d’Alché‐Buc, Florence; Lahaye, Pierre-Jean; Perrin, Bruno-Edouard; Ralaivola, Liva; Vujasinovic, Todor; Mazurie, Aurélien; Bottani, Samuele

doi:10.1007/10950913_5

Cited by 3 publications

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“…We will give a simple derivation of how to add second order dynamics for the individual nodes of the genetic networks using the principle of continuous to discrete conversion. This is similar to d'Alché-Buc's method [ 28 ]. The third or higher order dynamics can be similarly added but we do not make use of it in this report.…”

Section: Methodsmentioning

confidence: 58%

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Dynamical pathway analysis

Xiong

Choe

2008

BMC Syst Biol

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Background: Although a great deal is known about one gene or protein and its functions under different environmental conditions, little information is available about the complex behaviour of biological networks subject to different environmental perturbations. Observing differential expressions of one or more genes between normal and abnormal cells has been a mainstream method of discovering pertinent genes in diseases and therefore valuable drug targets. However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.

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Section: Methodsmentioning

confidence: 58%

“…Although the number of parameters is small compared with the number of states, which agrees with the knowledge that genetic networks are sparse [ 28 ], it is still hard to see at a glance whether they differ in any fundamental way. For that, we must apply systematic analysis to the estimated systems.…”

Section: Resultsmentioning

confidence: 86%

Dynamical pathway analysis

Xiong

Choe

2008

BMC Syst Biol

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“…We have seen that conventional EM tends to over-fit and produce a model that has limited predictive power. One approach to alleviate overfitting is to enforce sparsity on the parameters (d' Alche´-Buc et al, 2005). Another approach is to separate parameter estimation and structural inference, to incorporate structural constraints into parameter estimation, as in Gennemark and Wedlin (2007).…”

Section: Discussionmentioning

confidence: 99%

Structural systems identification of genetic regulatory networks

Xiong

Choe

2008

Bioinformatics

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Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction

Jereesh

Govindan

2016

3 Biotech

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Bio-inspired algorithms are widely used to optimize the model parameters of GRN. In this paper, focus is given to develop improvised versions of bio-inspired algorithm for the specific problem of reconstruction of gene regulatory network. The approach is applied to the data set that was developed by the DNA microarray technology through biological experiments in the lab. This paper introduced a novel hybrid method, which combines the clonal selection algorithm and BFGS Quasi-Newton algorithm. The proposed approach implemented for real world E. coli data set and identified most of the relations. The results are also compared with the existing methods and proven to be efficient.

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A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle

Cited by 3 publications

References 0 publications

Dynamical pathway analysis

Dynamical pathway analysis

Structural systems identification of genetic regulatory networks

Immuno-hybrid algorithm: a novel hybrid approach for GRN reconstruction

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