Abstract
BackgroundBiochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.ResultsAn S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.ConclusionsGASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.
Highlights
Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways
We study how GASA with two model selection criteria and two search spaces performs compared to time series network inference algorithm (TSNI), network component analysis (NCA), genetic algorithm (GA) with regular simulated annealing (SA) (GA-regular SA) and GA with regular GA (GAGA)
When implemented with a factor analysis algorithm, AIC and BIC model selection criteria (MSC) outperform the other four MSC on inferring genetic networks using data simulated from a linear dynamic model [3]
Summary
Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. To approximate the nonlinear relationship of a target (T) and its activator (A) and repressor (R), [5] proposed ordinary differential equations including perturbations from genes of interest; the perturbations provided information of the underlying network topology. Vu and Vohradsky presented a sigmoid model to depict the interaction between a target and its transcriptional factors (TFs) in [11], where order-n polynomials were used to approximate the model. To model a network of k factors regulating a given gene, the proposed sigmoid model requires n (n + k + 2) parameters while the S-system requires 2n (n + 1). Despite the many merits of the S-system, the large number of parameters required restricts its applications in the area
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