Growing enzyme gene networks by integration of gene expression, motif sequence, and metabolic information

In computational biology, gene networks are typically inferred from gene expression data alone. Incorporating multiple types of biological evidences makes it possible to improve gene network estimation. In this paper, we describe an approach for building enzyme gene networks by the integration of gene expression data, motif sequence, and metabolic information. To evaluate the approach, we apply it to a pool of E. coli genes related to aspartate pathway. The results show that integrative approach has potentials of obtaining more accurate gene networks.