This was progress for last year's report:
We have analyzed the relationships between different biological networks and between networks within different organisms. Looking at the expression and regulatory networks in yeast, we found that genes targeted by the same TF tend to be co-expressed (Yu et al., 2003; Qian et al., 2003). Those that share more than one TF are more likely to be co-expressed. Moreover, shared targets of a TF tend to have similar cellular functions. In contrast, the expression relationships between the TFs and their targets are much more complicated, often exhibiting time-shifted or inverted behaviors. In order to analyze the regulatory networks between organisms, we introduce the concept of a ?regulog? -- a conserved regulatory relationship between proteins across different species (Yu et al. 2004). Using the yeast regulatory network and our regulog method, 33 TFs, 621 targets, and 2,936 regulatory connections have been determined in D. melanogaster. Because of the lack of large-scale regulatory networks in other eukaryotes, a specific case study was used to prove the transferability of regulogs across species.
This was progress for this year's report:
So far, only analyses of static network structures have been performed, even though the dynamic nature of biological networks has been suggested by previous work. Therefore, we developed an approach for the Statistical Analysis of Network Dynamics (SANDY), combining well-known global topological measures, local motifs and newly derived statistics (Luscombe et al., 2004; Babu et al., 2004). Using SANDY, we analyzed the dynamics of the regulatory network on a genomic scale, by integrating transcriptional regulatory information and gene expression data for multiple conditions We found that underlying network architectures are rather different under different cellular conditions, i.e. TFs change their interactions in response to diverse stimuli. This observation is unexpected given current viewpoints and random simulations on large-scale network analysis. We showed that most TFs only act as transient hubs during certain conditions. In terms of sub-network structures, environmental responses facilitate fast signal propagation (e.g. with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (e.g. with highly inter-connected TFs).
Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data.
J Qian, J Lin, NM Luscombe, H Yu, M Gerstein (2003) Bioinformatics 19: 1917-26.
Genomic analysis of gene expression relationships in transcriptional regulatory networks.
H Yu, NM Luscombe, J Qian, M Gerstein (2003) Trends Genet 19: 422-7.
Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs.
H Yu, NM Luscombe, HX Lu, X Zhu, Y Xia, JD Han, N Bertin, S Chung, M Vidal, M Gerstein (2004) Genome Res 14: 1107-18.
Genomic analysis of regulatory network dynamics reveals large topological changes.
NM Luscombe, MM Babu, H Yu, M Snyder, SA Teichmann, M Gerstein (2004) Nature 431: 308-12.
Structure and evolution of transcriptional regulatory networks.
MM Babu, NM Luscombe, L Aravind, M Gerstein, SA Teichmann (2004) Curr Opin Struct Biol 14: 283-91.