Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks

Abstract

BackgroundA gene-regulatory network (GRN) refers to DNA segments that interact through their RNA and protein products and thereby govern the rates at which genes are transcribed. Creating accurate dynamic models of GRNs is gaining importance in biomedical research and development. To improve our understanding of continuous deterministic modeling methods employed to construct dynamic GRN models, we have carried out a comprehensive comparative study of three commonly used systems of ordinary differential equations: The S-system (SS), artificial neural networks (ANNs), and the general rate law of transcription (GRLOT) method. These were thoroughly evaluated in terms of their ability to replicate the reference models' regulatory structure and dynamic gene expression behavior under varying conditions.ResultsWhile the ANN and GRLOT methods appeared to produce robust models even when the model parameters deviated considerably from those of the reference models, SS-based models exhibited a notable loss of performance even when the parameters of the reverse-engineered models corresponded closely to those of the reference models: this is due to the high number of power terms in the SS-method, and the manner in which they are combined. In cross-method reverse-engineering experiments the different characteristics, biases and idiosynchracies of the methods were revealed. Based on limited training data, with only one experimental condition, all methods produced dynamic models that were able to reproduce the training data accurately. However, an accurate reproduction of regulatory network features was only possible with training data originating from multiple experiments under varying conditions.ConclusionsThe studied GRN modeling methods produced dynamic GRN models exhibiting marked differences in their ability to replicate the reference models' structure and behavior. Our results suggest that care should be taking when a method is chosen for a particular application. In particular, reliance on only a single method might unduly bias the results.

Analysis of Microarray Data using Artificial Intelligence Based Techniques

“…For review of the modeling techniques and the subject, refer to (De Jong, 2002;Wei et al, 2004;Schlitt and Brazma, 2007;Cho et al, 2007;Karlebach and Shamir, 2008;Swain et al, 2010;Sîrbu et al, 2010;Mitra et al, 2011;Raza and Parveen, 2013).…”

Biochemical Systems Theory: A Review

“…Nonetheless, the slope estimation strategy is statistically valid [204] and can, at the very least, be used to develop coarse solutions from which to start regular parameter optimization approaches. Reviews of this sub�eld include [68,69,[205][206][207][208][209][210][211][212][213][214].…”

Reverse engineering of gene regulation models from multi-condition experiments

“…Furthermore, different approaches have been adopted in an attempt to improve the performance of these methods and create more robust models, improve predictive performance, and identify regulatory interactions from gene expression measurements. Swain et al [5] evaluated three commonly used approaches which formulate dynamic GRN models as ordinary differential equations (ODEs). The authors assessed the ability of the different ODE structures to replicate the GRN systems' regulatory structure and dynamic gene expression behavior under varying conditions.…”

“…Reverse-engineer the final model M Λ from the combined learning data set Λ. 5. Validate (determine average accuracy or error) the models generated in Steps (3) and (4) against the validation data sets V. Fig.…”

“…A range of mathematical methods facilitating the reverse-engineering of quantitative, dynamic gene-regulatory network (GRN) models from time-series gene expression data have been reported in the literature [4]. Typical methods based on differential equations include the S-system (SS), artificial neural networks (ANN), and general rate law of transcription (GRLOT) method [5] [6].…”