New high-throughput technologies, such as microarrays and deep sequencing technologies, have provided unprecedented opportunities for mapping mutations, transcripts, transcription factor binding and histone modifications at high resolution and at genome-wide level. This has revolutionized the way regulations of diseases and other biological processes are studies and generated a large amount of heterogeneous data, which is begging to be unbiasedly and efficiently integrated. How to integrate these data still remains a big challenge. We have explored to ab initio predict or reconstruct regulatory networks based on heterogeneous data on gene expression, histone modification and genomic changes. We find that innovative integrations of these data can lead to not only global pictures of the complex biological processes, such as aging and early development, but also key regulatory events of these processes. We have also developed new computational algorithms to facilitate mapping of epigenetic features from the deep sequencing data. I will highlight our new methods and results for the integrative analyses of multi-dimensional heterogeneous large datasets to infer regulatory events, in particular for lineage determination during early development, and transcriptome changes during aging.