The reconstruction of genome-wide gene regulatory networks is known as a challenge task in Systems Biology consisted of integration of biological domain knowledge, mathematical models for gene regulatory networks, and optimization of the proposed model with large parameters. We propose two methods, GeNOSA and IRNet, can reconstruct quantitative and robust gene regulatory networks (GRNs). By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated.
To GRNs remains an indomitable challenge to reveal the quantitative relationships of transcription factors and genes under different conditions. From the quantitative GRNs established from microarray data by using the proposed framework, GeNOSA, ones can interpret the hidden information, such as the transcription factor activities and their effects to expression of down-stream genes. GeNOSA successfully established highly agreed GRNs compared to prior biological domain knowledge and experimentally validated by electrophoretic mobility shift assays and real-time PCR in E. coli.
Using nonlinear model can capture true biological response, they difficultly inferring robust networks and with high false positive rate owing to the dataset has noise and a large number of genes but a small number of experimental data. In this study, we propose an incremental reconstruction network algorithm, IRNet, can determine regulations in each iteration, and the inheritable mechanism can inherit determined regulations to next iteration that gradually reduce the solution space. Finally, IRNet infers a robust network from a small number of replicated time-series expressions data.