Inference of gene regulatory networks and compound mode of action from time course gene expression profiles

By Mukesh Bansal, Giusy Della Gatta, D. di Bernardo. Bioinform., 2006

418 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0968c531d294c55ed46134eae6748948447460fb

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Citations in the corpus

Gene regulatory network inference: Data integration in dynamic models - A review (2009, background): “2002a), Gardner and Faith (2005), Filkov (2005), Van Riel (2006), ansal et al. (2007), Goutsias and Lee (2007), Cho et al. (2007) as ell as Markowetz and Spang (2007). Well-structured overviews f the general idea behind GRN inference and diverse common athematical modelling schemes can be found in D”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “In fact, other investigators (D’Haeseleer et al. 1999; Wessels, van Someren, and Reinders 2001; Bansal, Della Gatta, and di Bernardo 2006) have used this data augmentation strategy for ODE parameter estimation.”
Modelling and analysis of gene regulatory networks (2008, methodology): “One recent study adopted the linear framework to create a model of a regulatory network that is subjected to an arbitrary number of perturbations and studied multiple perturbation scenarios using simulated data and a single-perturbation scenario using experimental dat”
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation (2016, methodology): “There are some ODE-based algorithms that infer GRNs such that the observed expression dynamics can be reconstructed from the optimized ODE [22].”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, background): “By solving a large-scale optimization problem (in which several genes are assumed to be regulated by a small number of transcription factors) researchers aim to identify this (hidden) activity profile and to determine the set of interactions at the various time points 71,83,84 .”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, neutral): “Alternatively, the same system of equations can be solved using a single perturbation experiment, and measuring multiple time-points following the perturbation, as done in the Time-Series Network Identification algorithm (TSNI) [Bansal et al., 2006, Bansal and di Bernardo, 2007].”