Gene Regulatory Network (GRN) plays an important role in the understanding of complex biological systems. High throughput microarray gene expression data (or next gene sequencing data if available) is used for finding these regulatory relationships between genes. In this research we investigate novel modeling approaches for large scale reverse engineering GRNs. Comparative studies and robustness analysis using standard benchmark data will be used to show the superiority of the proposed methods. Real life data sets related to cardio vascular diseases (CVDs) or cancer data will be used to understand the probable underlying causes of diseases. Impact for drugs will also be investigated.

Supervision: Assoc Prof Madhu Chetty (Principal) and Assoc Prof Stuart Berzins (Associate)

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