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Background: A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions. Results: We present a framework to unify different notions of sign consistency and propose a refined method for data discretization that considers uncertainties in experimental profiles. We furthermore introduce a new constraint to filter undesired model behaviors induced by positive feedback loops. Finally, we generalize the way predictions can be made by the sign consistency approach. In particular, we distinguish strong predictions (e.g. increase of a node level) and weak predictions (e.g., node level increases or remains unchanged) enlarging the overall predictive power of the approach. We then demonstrate the applicability of our framework by confronting a large-scale gene regulatory network model of Escherichia coli with high-throughput transcriptomic measurements. Conclusion: Overall, our work enhances the flexibility and power of the sign consistency approach for the prediction of the behavior of signaling and gene regulatory networks and, more generally, for the validation and inference of these networks. [ABSTRACT FROM AUTHOR]

Regulation of polarised cell growth is essential for many cellular processes including spatial coordination of cell morphology changes during the division cycle. We present a mathematical model of the core mechanism responsible for the regulation of polarised growth dynamics during the fission yeast cell cycle. The model is based on the competition of growth zones localised at the cell tips for a common substrate distributed uniformly in the cytosol. We analyse the bifurcations in this model as the cell length increases, and show that the growth activation dynamics provides an explanation for the new-end take-off (NETO) as a saddle-node bifurcation at which the cell sharply switches from monopolar to bipolar growth. We study the parameter sensitivity of the bifurcation diagram and relate qualitative changes of the growth pattern, e.g. delayed or absent NETO, to previously observed mutant phenotypes. We investigate the effects of imperfect asymmetric cell division, and show that this leads to distinct growth patterns that provide experimentally testable predictions for validating the presented competitive growth zone activation model. Finally we discuss extension of the model for describing mutant cells with more than two growth zones. [ABSTRACT FROM AUTHOR]

Cells respond to changes in the internal and external environment by a complex regulatory system whose end-point is the activation of transcription factors controlling the expression of a pool of ad-hoc genes. Recent experiments have shown that certain stimuli may trigger oscillations in the concentration of transcription factors such as NF-kB and p53 influencing the final outcome of the genetic response. In this study we investigate the role of oscillations in the case of three different well known gene regulatory mechanisms using mathematical models based on ordinary differential equations and numerical simulations. We considered the cases of direct regulation, two-step regulation and feed-forward loops, and characterized their response to oscillatory input signals both analytically and numerically. We show that in the case of indirect two-step regulation the expression of genes can be turned on or off in a frequency dependent manner, and that feed-forward loops are also able to selectively respond to the temporal profile of oscillating transcription factors. [ABSTRACT FROM AUTHOR]

The hypoxia-inducible factor (HIF) is a key regulator of the transcriptional response to hypoxia. While the mechanism underpinning HIF activation is well understood, little is known about its resolution. Both the protein and the mRNA levels of HIF-1α (but not HIF-2α) were decreased in intestinal epithelial cells exposed to prolonged hypoxia. Coincident with this, microRNA (miRNA) array analysis revealed multiple hypoxia-inducible miRNAs. Among these was miRNA-155 (miR-155), which is predicted to target HIF-1α mRNA. We confirmed the hypoxic upregulation of miR-155 in cultured cells and intestinal tissue from mice exposed to hypoxia. Furthermore, a role for HIF-1α in the induction of miR-155 in hypoxia was suggested by the identification of hypoxia response elements in the miR-155 promoter and confirmed experimentally. Application of miR-155 decreased the HIF-1α mRNA, protein, and transcriptional activity in hypoxia, and neutralization of endogenous miR-155 reversed the resolution of HIF-1α stabilization and activity. Based on these data and a mathematical model of HIF-1α suppression by miR-155, we propose that miR-155 induction contributes to an isoform-specific negative-feedback loop for the resolution of HIF-1α activity in cells exposed to prolonged hypoxia, leading to oscillatory behavior of HIF-1α-dependent transcription. [ABSTRACT FROM AUTHOR]