Networks From Gene Expression Time Series: Characterization of Correlation Patterns

Remondini, Daniel; Neretti, Nicola; Franceschi, Claudio; Tieri, Paolo; Sedivy, John M.; Milanesi, Luciano; Castellani, Gastone

doi:10.1142/s0218127407018543

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…for the c-Myc dataset: a 5 × 1191 array of values sampled from the Standardized Gaussian Distribution [29]). The resulting distribution strictly resambles that obtained with the no Tamoxifen dataset.…”

Section: Resultsmentioning

confidence: 99%

Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation

et al. 2007

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Time course gene expression experiments are a popular means to infer co-expression. Many methods have been proposed to cluster genes or to build networks based on similarity measures of their expression dynamics. In this paper we apply a correlation based approach to network reconstruction to three datasets of time series gene expression following system perturbation: 1) Conditional, Tamoxifen dependent, activation of the cMyc proto-oncogene in rat fibroblast; 2) Genomic response to nutrition changes in D. melanogaster; 3) Patterns of gene activity as a consequence of ageing occurring over a life-span time series (25y–90y) sampled from T-cells of human donors.We show that the three datasets undergo similar transitions from an "uncorrelated" regime to a positively or negatively correlated one that is symptomatic of a shift from a "ground" or "basal" state to a "polarized" state.In addition, we show that a similar transition is conserved at the pathway level, and that this information can be used for the construction of "meta-networks" where it is possible to assess new relations among functionally distant sets of molecular functions.

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Section: Resultsmentioning

confidence: 99%

Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation

et al. 2007

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“…One example of using a simple Pearson correlation measure is the work of Remondini et al ., where they used two sets of gene expression data from rat fibroblast cell lines to construct correlation-based networks [ 30 , 31 ]. Even though such a correlation measure can be useful in many cases, it cannot provide causal information.…”

Section: Inferring Structure Of the Networkmentioning

confidence: 99%

Inference of Gene Regulatory Networks Using Time-Series Data: A Survey

Sima¹,

Jiang²,

Jung³

2009

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The advent of high-throughput technology like microarrays has provided the platform for studying how different cellular components work together, thus created an enormous interest in mathematically modeling biological network, particularly gene regulatory network (GRN). Of particular interest is the modeling and inference on time-series data, which capture a more thorough picture of the system than non-temporal data do. We have given an extensive review of methodologies that have been used on time-series data. In realizing that validation is an impartible part of the inference paradigm, we have also presented a discussion on the principles and challenges in performance evaluation of different methods. This survey gives a panoramic view on these topics, with anticipation that the readers will be inspired to improve and/or expand GRN inference and validation tool repository.

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“…The scale-free property of connectivity degree distributions, as observed in many biological networks 33,42 is only a first step: many other structures can be embedded in a scale-free network (and in any other network topology as well) such as for example assortativity/dissortativity 110,111 or non trivial relationships between network observables (e.g. centrality measures 112 ) or the presence of feedback loops. 31,44 The role of these topological features is still to be investigated.…”

Section: Which Topology For a Plausible Model?mentioning

confidence: 99%

Functional models for large-scale gene regulation networks: realism and fiction

et al. 2009

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High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

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Networks From Gene Expression Time Series: Characterization of Correlation Patterns

Cited by 7 publications

References 15 publications

Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation

Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation

Inference of Gene Regulatory Networks Using Time-Series Data: A Survey

Functional models for large-scale gene regulation networks: realism and fiction

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