Integrative analysis of large scale expression profiles reveals core transcriptional response and coordination between multiple cellular processes in a cyanobacterium

<p>Abstract</p> <p>Background</p> <p>Cyanobacteria are the only known prokaryotes capable of oxygenic photosynthesis. They play significant roles in global biogeochemical cycles and carbon sequestration, and have recently been recognized as potential vehicles for production of renewable biofuels. <it>Synechocystis </it>sp. PCC 6803 has been extensively used as a model organism for cyanobacterial studies. DNA microarray studies in <it>Synechocystis </it>have shown varying degrees of transcriptome reprogramming under altered environmental conditions. However, it is not clear from published work how transcriptome reprogramming affects pre-existing networks of fine-tuned cellular processes.</p> <p>Results</p> <p>We have integrated 163 transcriptome data sets generated in response to numerous environmental and genetic perturbations in <it>Synechocystis</it>. Our analyses show that a large number of genes, defined as the core transcriptional response (CTR), are commonly regulated under most perturbations. The CTR contains nearly 12% of <it>Synechocystis </it>genes found on its chromosome. The majority of genes in the CTR are involved in photosynthesis, translation, energy metabolism and stress protection. Our results indicate that a large number of differentially regulated genes identified in most reported studies in <it>Synechocystis </it>under different perturbations are associated with the general stress response. We also find that a majority of genes in the CTR are coregulated with 25 regulatory genes. Some of these regulatory genes have been implicated in cellular responses to oxidative stress, suggesting that reactive oxygen species are involved in the regulation of the CTR. A Bayesian network, based on the regulation of various KEGG pathways determined from the expression patterns of their associated genes, has revealed new insights into the coordination between different cellular processes.</p> <p>Conclusion</p> <p>We provide here the first integrative analysis of transcriptome data sets generated in a cyanobacterium. This compilation of data sets is a valuable resource to researchers for all cyanobacterial gene expression related queries. Importantly, our analysis provides a global description of transcriptional reprogramming under different perturbations and a basic framework to understand the strategies of cellular adaptations in <it>Synechocystis</it>.</p

Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers

Networks from Flows - From Dynamics to Topology

Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information