On the analysis of time-delayed interactions in genetic network using S-system model
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2013
- Type: Text , Conference paper
- Relation: 20th International Conference, ICONIP 2013 Daegu, Korea, November 3rd-7th; published in Neural Information Processing, Lecture Notes in Computer Science, vol 8227. pg 616-623
- Full Text: false
- Reviewed:
- Description: The Gene Regulatory Network (GRN) is the collection of genes and interactions among them, which captures the mutual interactions among genes. 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, although limited to represent the instantaneous interactions only. Recently proposed Time-delayed S-System Model (TDSS), an improved version of the traditional S-System model, is capable of representing the delayed interactions present in the genetic network. In this paper, we have shown the results of extensive analysis performed on TDSS over a widely used synthetic network. The two well-known performance measures applied to the synthetic network with various time-delayed regulations clearly demonstrate that the TDSS can capture both the instantaneous and delayed interactions correctly with high precision. Further, we have shown the effect of various samples sizes during the optimization where average error for the inferred parameters are reported and compared with an existing state-or-the-art algorithm. © Springer-Verlag 2013.
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.