Adaptive regulatory genes cardinality for reconstructing genetic networks
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Vinh, Nguyen
- Date: 2012
- Type: Text , Conference paper
- Relation: WCCI 2012 IEEE World Congress on Computational Intelligence
- Full Text: false
- Reviewed:
- Description: With the advent of microarray technology, researchers are able to determine cellular dynamics for thousands of genes simultaneously, thereby enabling reverse engineering of the gene regulatory network (GRN) from high-throughput time-series gene expression data. Amongst the various currently available models for inferring GRN, the S-System formalism is often considered as an excellent compromise between accuracy and mathematical tractability. In this paper, a novel approach for inferring GRN based on the decoupled S-System model, incorporating the new concept of adaptive regulatory genes cardinality, is proposed. Parameter learning for the S-System is carried out in an evolving manner using a versatile and robust Trigonometric Evolutionary Algorithm. The applicability and efficiency of the proposed method is studied using a well-known and widely studied synthetic network with various levels of noise, and excellent performance observed. Further, investigations of a 5 gene in-vivo synthetic biological network of Saccharomyces cerevisiae called IRMA, has succeeded in detecting higher number of correct regulations compared to other approaches reported earlier.
Reverse engineering genetic networks with time-delayed S-system model and pearson correlation coefficient
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2013
- Type: Text , Conference paper
- Relation: 20th International Conference on Neural Information Processing, ICONIP 2013; Daegu; South Korea; 3rd-7th November 2013; published in Neural Information Processing, Lecture Notes in Computer Science, vol 8227. p. 624-631
- Full Text: false
- Reviewed:
- Description: 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.