MODELING AND ANALYSIS OF GENE EXPRESSION TIME-SERIES BASED ON CO-EXPRESSION

In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologicaly relevant clusters.

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NETWORKS FROM GENE EXPRESSION TIME SERIES: CHARACTERIZATION OF CORRELATION PATTERNS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127407018543 ◽

2007 ◽

Vol 17 (07) ◽

pp. 2477-2483 ◽

Cited By ~ 4

Author(s):

D. REMONDINI ◽

N. NERETTI ◽

C. FRANCESCHI ◽

P. TIERI ◽

J. M. SEDIVY ◽

...

Keyword(s):

Gene Expression ◽

Time Series ◽

Large Scale ◽

Molecular Mechanisms ◽

Single Gene ◽

Biological Information ◽

Reconstruction Method ◽

Gene Expression Time Series ◽

Expression Time

We address the problem of finding large-scale functional and structural relationships between genes, given a time series of gene expression data, namely mRNA concentration values measured from genetically engineered rat fibroblasts cell lines responding to conditional cMyc proto-oncogene activation. We show how it is possible to retrieve suitable information about molecular mechanisms governing the cell response to conditional perturbations. This task is complex because typical high-throughput genomics experiments are performed with high number of probesets (103–104 genes) and a limited number of observations (< 102 time points). In this paper, we develop a deepest analysis of our previous work [Remondini et al., 2005] in which we characterized some of the main features of a gene-gene interaction network reconstructed from temporal correlation of gene expression time series. One first advancement is based on the comparison of the reconstructed network with networks obtained from randomly generated data, in order to characterize which features retrieve real biological information, and which are instead due to the characteristics of the network reconstruction method. The second and perhaps more relevant advancement is the characterization of the global change in co-expression pattern following cMyc activation as compared to a basal unperturbed state. We propose an analogy with a physical system in a critical state close to a phase transition (e.g. Potts ferromagnet), since the cell responds to the stimulus with high susceptibility, such that a single gene activation propagates to almost the entire genome. Our result is relative to temporal properties of gene network dynamics, and there are experimental evidence that this can be related to spatial properties regarding the global organization of chromatine structure [Knoepfler et al., 2006].

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The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data

Advances in Bioinformatics ◽

10.1155/2009/284251 ◽

2009 ◽

Vol 2009 ◽

pp. 1-10

Author(s):

Martina Bremer ◽

R. W. Doerge

Keyword(s):

Gene Expression ◽

Time Series ◽

Statistical Power ◽

Regulatory Networks ◽

Real Data ◽

Model Parameters ◽

Expression Data ◽

Data Set ◽

Gene Expression Time Series ◽

Expression Time

We present a statistical method to rank observed genes in gene expression time series experiments according to their degree of regulation in a biological process. The ranking may be used to focus on specific genes or to select meaningful subsets of genes from which gene regulatory networks can be built. Our approach is based on a state space model that incorporates hidden regulators of gene expression. Kalman (K) smoothing and maximum (M) likelihood estimation techniques are used to derive optimal estimates of the model parameters upon which a proposed regulation criterion is based. The statistical power of the proposed algorithm is investigated, and a real data set is analyzed for the purpose of identifying regulated genes in time dependent gene expression data. This statistical approach supports the concept that meaningful biological conclusions can be drawn from gene expression time series experiments by focusing on strong regulation rather than large expression values.

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