Integrating heterogeneous gene expression data for gene regulatory network modelling

Theory in Biosciences = Theorie in Den Biowissenschaften
Alina SîrbuMartin Crane

Abstract

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.

References

Jan 1, 2015·Microarrays·Alina SîrbuHeather J Ruskin

Citations

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Jan 29, 2010·BMC Bioinformatics·Alina SîrbuMartin Crane

Related Concepts

Stochastic Processes
Gene Expression
Gene Regulatory Networks
Systems Biology
Gene Modules

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