Transcriptomic response to prolonged ethanol production in the cyanobacterium Synechocystis sp. PCC6803

BACKGROUND: The production of biofuels in photosynthetic microalgae and cyanobacteria is a promising alternative to the generation of fuels from fossil resources. To be economically competitive, producer strains need to be established that synthesize the targeted product at high yield and over a long time. Engineering cyanobacteria into forced fuel producers should considerably interfere with overall cell homeostasis, which in turn might counteract productivity and sustainability of the process. Therefore, in-depth characterization of the cellular response upon long-term production is of high interest for the targeted improvement of a desired strain. RESULTS: The transcriptome-wide response to continuous ethanol production was examined in Synechocystis sp. PCC6803 using high resolution microarrays. In two independent experiments, ethanol production rates of 0.0338% (v/v) ethanol d(-1) and 0.0303% (v/v) ethanol d(-1) were obtained over 18 consecutive days, measuring two sets of biological triplicates in fully automated photobioreactors. Ethanol production caused a significant (~40%) delay in biomass accumulation, the development of a bleaching phenotype and a down-regulation of light harvesting capacity. However, microarray analyses performed at day 4, 7, 11 and 18 of the experiment revealed only three mRNAs with a strongly modified accumulation level throughout the course of the experiment. In addition to the overexpressed adhA (slr1192) gene, this was an approximately 4 fold reduction in cpcB (sll1577) and 3 to 6 fold increase in rps8 (sll1809) mRNA levels. Much weaker modifications of expression level or modifications restricted to day 18 of the experiment were observed for genes involved in carbon assimilation (Ribulose bisphosphate carboxylase and Glutamate decarboxylase). Molecular analysis of the reduced cpcB levels revealed a post-transcriptional processing of the cpcBA operon mRNA leaving a truncated mRNA cpcA* likely not competent for translation. Moreover, western blots and zinc-enhanced bilin fluorescence blots confirmed a severe reduction in the amounts of both phycocyanin subunits, explaining the cause of the bleaching phenotype. CONCLUSIONS: Changes in gene expression upon induction of long-term ethanol production in Synechocystis sp. PCC6803 are highly specific. In particular, we did not observe a comprehensive stress response as might have been expected

High Performance Networks: From Supercomputing to Cloud Computing

Datacenter networks provide the communication substrate for large parallel computer systems that form the ecosystem for high performance computing (HPC) systems and modern Internet applications. The design of new datacenter networks is motivated by an array of applications ranging from communication intensive climatology, complex material simulations and molecular dynamics to such Internet applications as Web search, language translation, collaborative Internet applications, streaming video and voice-over-IP. For both Supercomputing and Cloud Computing the network enables distributed applicat

Dennis Abts

A single node of a modern scalable multiprocessor consists of several ASICs comprising tens of millions of gates. This level of integration and complexity imposes an enormous onus on the verification process. A variety of tools, ranging from discrete-event logic simulation to formal model checking, can be used to attack this problem. Unfortunately, conventional simulation techniques, with their primitive interface to the hardware (i.e. test vectors), are inadequate tools for reasoning about the correctness of complex architectural features, such as cache coherence protocols and memory consistency models. Similarly, model checkers offer very limited utility on such large designs. We have previously proposed [1] a novel verification framework, called Raven, that addresses many of these challenges. In this paper, we examine the performance implications of verifying systems at higher levels of abstraction. A detailed performance analysis is conducted to compare this higherlevel approach against an equivalent Verilog test bench. We establish lower and upper bounds on the performance of the Raven environment executing on a single-processor, on a set of distributed processors, and on a shared-memory multiprocessor