This review discusses the methods for inferring genetic networks from gene expression data. It covers clustering of co-expression profiles to infer shared regulatory inputs and functional pathways, and various approaches to reverse engineering genetic networks, including discrete Boolean networks, continuous linear and non-linear models. The review emphasizes the importance of combining predictive modeling with systematic experimental verification to gain deeper insights into biological systems. It also discusses the challenges of analyzing complex dynamic systems, the role of attractors in gene expression, and the use of Boolean networks as a conceptual framework for understanding regulatory dynamics. The review highlights the importance of clustering methods in identifying gene expression patterns and regulatory relationships, and discusses different clustering algorithms, distance measures, and preprocessing techniques. It also addresses the challenges of modeling gene regulatory networks, including the choice between Boolean and continuous models, deterministic and stochastic approaches, and spatial versus non-spatial models. The review concludes with a discussion of data requirements for reverse engineering genetic networks and the need for robust modeling methods that can handle large-scale data and complex biological systems.This review discusses the methods for inferring genetic networks from gene expression data. It covers clustering of co-expression profiles to infer shared regulatory inputs and functional pathways, and various approaches to reverse engineering genetic networks, including discrete Boolean networks, continuous linear and non-linear models. The review emphasizes the importance of combining predictive modeling with systematic experimental verification to gain deeper insights into biological systems. It also discusses the challenges of analyzing complex dynamic systems, the role of attractors in gene expression, and the use of Boolean networks as a conceptual framework for understanding regulatory dynamics. The review highlights the importance of clustering methods in identifying gene expression patterns and regulatory relationships, and discusses different clustering algorithms, distance measures, and preprocessing techniques. It also addresses the challenges of modeling gene regulatory networks, including the choice between Boolean and continuous models, deterministic and stochastic approaches, and spatial versus non-spatial models. The review concludes with a discussion of data requirements for reverse engineering genetic networks and the need for robust modeling methods that can handle large-scale data and complex biological systems.