Using network theory on an integrated time-resolved genome-wide gene expression data, we investigated the intricate dynamic regulatory relationships of transcription factors and target genes to unravel signatures that contribute to extreme phenotypic differences in yeast, Saccharomyces cerevisiae. We performed a comparative analysis of the gene expression profiles of two yeast strains SK1 and S288c which are known for high and low sporulation efficiency, respectively. The results based on various structural attributes of the networks, such as clustering coefficient, degree-degree correlations, and betweenness centrality suggested that a delay in crosstalk between functional modules can be construed as one of the prime reasons behind low sporulation efficiency of S288c strain. A more hierarchical structure in the late phase of sporulation in S288c indicated an attempt of this low sporulating strain to obtain modularity, which is a feature of early sporulation phase. Further, the weak ties analysis revealed that mostly meiosis-associated genes were the end nodes of the weak ties for the high sporulating SK1 strain, while for the low sporulating S288c strain these nodes were mitotic genes. This again was a clear indication of the delay in regulatory activities in the S288c strain, which are essential to initiate sporulation. Our results demonstrate the potential of this framework in identifying candidate nodes contributing to phenotypic diversity in natural populations with application prospects in drug target discovery and personalized health.