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The circadian clock is the molecular mechanism responsible for the adaptation to daily rhythms in living organisms. Oscillations and fluctuations in environmental conditions regulate the circadian clock through signaling pathways. We study the response to continuous photic perturbations in a minimal molecular network model of the circadian clock, composed of 5 nonlinear delay differential equations with multiple feedbacks. We model the perturbation as a stationary stochastic process, and we consider the resulting irreversibility of trajectories as a key effect of the interaction. In particular we adopt a measure of mutual mapping irreversibility in the time series thermodynamics framework, and we find 12 hours harmonics., Comment: 6 pages, 4 figures

Non-coding RNAs are crucial regulators of gene expression in prokaryotes and eukaryotes, but it remains poorly understood how they affect the dynamics of transcriptional networks. We analyzed the temporal characteristics of the cyanobacterial iron stress response by mathematical modeling and quantitative experimental analyses, and focused on the role of a recently discovered small non-coding RNA, IsrR. We found that IsrR is responsible for a pronounced delay in the accumulation of isiA mRNA encoding the late-phase stress protein, IsiA, and that it ensures a rapid decline in isiA levels once external stress triggers are removed. These kinetic properties allow the system to selectively respond to sustained (as opposed to transient) stimuli, and thus establish a temporal threshold, which prevents energetically costly IsiA accumulation under short-term stress conditions. Biological information is frequently encoded in the quantitative aspects of intracellular signals (e.g., amplitude and duration). Our simulations reveal that competitive inhibition and regulated degradation allow intracellular regulatory networks to efficiently discriminate between transient and sustained inputs.

Increasingly used high throughput experimental techniques, like DNA or protein microarrays give as a result groups of interesting, e.g. differentially regulated genes which require further biological interpretation. With the systematic functional annotation provided by the Gene Ontology the information required to automate the interpretation task is now accessible. However, the determination of statistical significant e.g. molecular functions within these groups is still an open question. In answering this question, multiple testing issues must be taken into account to avoid misleading results. Here we present a statistical framework that tests whether functions, processes or locations described in the Gene Ontology are significantly enriched within a group of interesting genes when compared to a reference group. First we define an exact analytical expression for the expected number of false positives that allows us to calculate adjusted p-values to control the false discovery rate. Next, we demonstrate and discuss the capabilities of our approach using publicly available microarray data on cell-cycle regulated genes. Further, we analyze the robustness of our framework with respect to the exact gene group composition and compare the performance with earlier approaches. The software package GOSSIP implements our method and is made freely available at http://gossip.gene-groups.net/, Comment: supplement http://gossip.gene-groups.net/