Reverse engineering genetic networks with time-delayed S-system model and pearson correlation coefficient

In almost all biological systems including genetic networks, the complex simultaneous interactions occurring amongst different organelles within a cell are both - instantaneous and time-delayed. Among the various modeling approaches, applied for inferring Gene Regulatory Network (GRN), recently proposed Time-delayed S-System Model (TDSS) is capable of simultaneously represent both the instantaneous and time-delayed interactions. While the delay parameters are incorporated in the S-System model to propose TDSS, this open a new challenge in GRN reconstruction. This paper proposes a systematic approach to fit in various level of knowledge in the delay parameters during the reverse engineering process. Further, we have approximated the delay parameters with well-known statistical measure Pearson correlation coefficient. Experimental studies have been carried out considering two widely used synthetic networks with various delays and real-life network of Saccharomyces cerevisiae called IRMA. The results clearly exhibit the influence of incorporating knowledge in the parameter learning process. © Springer-Verlag 2013