Dynamic modeling of gene expression data.

By Neal S. Holter, Amos Maritan, Marek Cieplak, Nina V. Fedoroff, Jayanth R. Banavar. Proceedings of the National Academy of Sciences of the United States of America, 2001

339 citations recorded by the source corpus.

We describe the time evolution of gene expression levels by using a time translational matrix to predict future expression levels of genes based on their expression levels at some initial time. We deduce the time translational matrix for previously published DNA microarray gene expression data sets by modeling them within a linear framework by using the characteristic modes obtained by singular value decomposition. The resulting time translation matrix provides a measure of the relationships among the modes and governs their time evolution. We show that a truncated matrix linking just a few modes is a good approximation of the full time translation matrix. This finding suggests that the number of essential connections among the genes is small.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3d5c9fb1d5e89e64c768d57847ed23fe41e46a15

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Citations in the corpus

Dynamic modeling of genetic networks using genetic algorithm and S-system (2003, methodology): “Holter et al. (2001) used a time translational matrix for modeling these data within a linear framework.”
A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes (2006, methodology): “Essentially, this converts the clustering problem to one involving the clustering and analysis of curves rather than of random variables, establishing links with functional data analysis as described by, for example, Ramsay and Silverman (1997). This led us to develop a Bayesian model-based agglomer”
Universality in network dynamics (2013, neutral): “1 (equation (16)), leading to heterogeneous cascades.”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “gular value decomposition (SVD) (Holter et al., 2001; Yeung et al., 2002) and regularised least squares regression are the most promi-”
Analyzing time series gene expression data (2004, methodology): “Holter et al. (2001) used a time translational matrix to model the temporal relationships between different modes of the SVD.”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “…et al. 2001; Zou and Conzen 2005; Song, Kolar, and Xing 2009), vector autoregressive and state space models (SSMs; Hirose et al. 2008; Kojima et al. 2009; Shimamura et al. 2009); and differential equation models (Voit 2000; Holter et al. 2001; DeJong 2002; Yeung, Tegner, and Collins 2002).”
Modeling and Simulation of Genetic Regulatory Systems: A Literature Review (2002, positive): “…have been used to group together genes with similar temporal expression patterns (Alon et al ., 1999; Ben-Dor et al ., 1999; Brown et al ., 2000; Cho et al ., 1998; Eisen et al ., 1998; Holter et al ., 2001; Michaels et al ., 1998; Spellman et al ., 1998; Tamayo et al ., 1999; Wen et al ., 1998).”
Linear modes of gene expression determined by independent component analysis (2002, methodology): “Holter et al. (2001) fitted the time-behaviour of eigengenes by a linear dynamical model.”
Singular value decomposition and principal component analysis (2003, methodology): “have developed an SVD-based method for analysis of time series expression data (Holter et al., 2001).”