Systems biology efforts are increasingly adopting quantitative, mechanistic modeling to study cellular signal transduction pathways and other networks. However, it is uncertain whether the particular set of kinetic parameter values of the model closely approximates the corresponding biological system. We propose that the parameters be assigned statistical distributions that reflect the degree of uncertainty for a comprehensive simulation analysis. From this analysis, we globally identify the key components and steps in signal transduction networks at a systems level. We investigated a recent mathematical model of interferon gamma induced Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway by applying multi-parametric sensitivity analysis that is based on simultaneous variation of the parameter values. We find that suppressor of cytokine signaling-1, nuclear phosphatases, cytoplasmic STAT1, and the corresponding reaction steps are sensitive perturbation points of this pathway.

BackgroundSubcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.DescriptionWe have developed a database of proteins with multiple subcellular localizations, designated DBMLoc. The initial release contains 10470 multiple subcellular localization-annotated entries. Annotations are collected from primary protein databases, specific subcellular localization databases and literature texts. All the protein entries are cross-referenced to GO annotations and SwissProt. Protein-protein interactions are also annotated. They are classified into 12 large subcellular localization categories based on GO hierarchical architecture and original annotations. Download, search and sequence BLAST tools are also available on the website.ConclusionDBMLoc is a protein database which collects proteins with more than one subcellular localization annotation. It is freely accessed at .

We uncovered the underlying energy landscape for a cellular network. We discovered that the energy landscape of the yeast cell-cycle network is funneled towards the global minimum (G0/G1 phase) from the experimentally measured or inferred inherent chemical reaction rates. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. The funneled landscape can be seen as a possible realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

We uncover the underlying potential energy landscape for a cellular network. We find that the potential energy landscape of the mitogen-activated protein-kinase signal transduction network is funneled toward the global minimum. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. Funneled landscape is a realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

Gene duplication is an important mechanism driving the evolution of biomolecular network. Thus, it is expected that there should be a strong relationship between a gene's duplicability and the interactions of its protein product with other proteins in the network. We studied this question in the context of the protein interaction network (PIN) of Saccharomyces cerevisiae. We found that duplicates have, on average, significantly lower clustering coefficient (CC) than singletons, and the proportion of duplicates (PD) decreases steadily with CC. Furthermore, using functional annotation data, we observed a strong negative correlation between PD and the mean CC for functional categories. By partitioning the network into modules and assigning each protein a modularity measure Q(n), we found that CC of a protein is a reflection of its modularity. Moreover, the core components of complexes identified in a recent high-throughput experiment, characterized by high CC, have lower PD than that of the attachments. Subsequently, 2 types of hub were identified by their degree, CC and Q(n). Although PD of intramodular hubs is much less than the network average, PD of intermodular hubs is comparable to, or even higher than, the network average. Our results suggest that high CC, and thus high modularity, pose strong evolutionary constraints on gene duplicability, and gene duplication prefers to happen in the sparse part of PINs.