Abstract
BackgroundThe growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot.ResultsIn order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented.ConclusionsWhen tested on three real gene expression datasets from E.coli and Human Cell, the experiment results suggest that CVODE model could improve 20–50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.
Highlights
The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics
In this paper, we have presented complex-valued ordinary differential equation (ODE) model to identify the regulations among genes for gene regulatory network inference
Three real gene expression datasets are utilized and the results reveal that complex-valued version of ordinary differential equation (CVODE) model could improve 20%-50% prediction accuracy of gene expression data, and identify more true-positive regulatory relationships than ODE
Summary
The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. Some computational approaches have been developed to describe biology networks [11,12,13,14,15,16,17], especially the regulatory relationships between genes with gene expression time series, such as directed graph [18], Boolean network [3, 19], Bayesian network [20, 21], differential equation [22], neural network [23,24,25], stochastic equation [26, 27], etc. Differential equation model is more conducive to describing the concentration evolution of biological macromolecules such as RNA and protein over time. This model has been widely utilized in pharmacokinetics, enzymology and gene regulatory network construction [28,29,30,31,32,33]. Some special nonlinear ODE models have been proposed to infer GRN, such as S-system model [40,41,42]
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