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Background Poly(3-hydroxybutyrate) (PHB), a biodegradable bio-plastic, is one of the most common homopolymer of polyhydroxyalkanoates (PHAs). PHB is synthesized by a variety of microorganisms as intracellular carbon and energy storage compounds in response to environmental stresses. Bio-based production of PHB from renewable feedstock is a promising and sustainable alternative to the petroleum-based chemical synthesis of plastics. In this study, a novel strategy was applied to improve the PHB biosynthesis from different carbon sources. Results In this research, we have constructed E. coli strains to produce PHB by engineering the Serine-Deamination (SD) pathway, the Entner-Doudoroff (ED) pathway, and the pyruvate dehydrogenase (PDH) complex. Firstly, co-overexpression of sdaA (encodes L-serine deaminase), L-serine biosynthesis genes and pgk (encodes phosphoglycerate kinase) activated the SD Pathway, and the resulting strain SD02 (pBHR68), harboring the PHB biosynthesis genes from Ralstonia eutropha, produced 4.86 g/L PHB using glucose as the sole carbon source, representing a 2.34-fold increase compared to the reference strain. In addition, activating the ED pathway together with overexpressing the PDH complex further increased the PHB production to 5.54 g/L with content of 81.1% CDW. The intracellular acetyl-CoA concentration and the [NADPH]/[NADP+] ratio were enhanced after the modification of SD pathway, ED pathway and the PDH complex. Meanwhile, these engineering strains also had a significant increase in PHB concentration and content when xylose or glycerol was used as carbon source. Conclusions Significant levels of PHB biosynthesis from different kinds of carbon sources can be achieved by engineering the Serine-Deamination pathway, Entner-Doudoroff pathway and pyruvate dehydrogenase complex in E. coli JM109 harboring the PHB biosynthesis genes from Ralstonia eutropha. This work demonstrates a novel strategy for improving PHB production in E. coli. The strategy reported here should be useful for the bio-based production of PHB from renewable resources. [ABSTRACT FROM AUTHOR]

Genome-scale metabolic networks of organisms are normally very large and complex. Previous studies have shown that they are organized in a hierarchical and modular manner. In particular, a core-periphery modular organization structure has been proposed for metabolic networks. However, no methods or parameters are available in the literature to quantitatively evaluate or find the hierarchical and modular structure of metabolic networks. In this paper, we propose a parameter ,called "core coefficient" to quantitatively evaluate the core-periphery structure of a metabolic network. This parameter is defined based on the concept of closeness centrality of metabolites and a newly defined parameter: network capacity. To find or define the core and the periphery modules of a metabolic network, we further developed a method to decompose metabolic networks based on a quantitative parameter of modularity and a procedure of core extraction. The method has been developed with genome-scale metabolic networks of five representative organisms, which include Aeropyrum pernix, Bacillus subtilis, Escherichia coil, Saccharomyces cerevisiae, and Homo sapiens. The results were compared with two artificially generated network models. [ABSTRACT FROM AUTHOR]

Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. Results: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. Conclusion: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data. [ABSTRACT FROM AUTHOR]

An abstract of the article "Arsenic biosensor: a step further," by Chris French, Judith Nicholson, Farid Bizzari, Jelena Aleksic, Yizhi Cai, Sreemati Lalgudi Seshasayee, Sergii Ivakhno, Bryony Davidson, Jen Wilson, Kim de Mora, Hongwu Ma, Laszlo Kozma-Bognar and Alistair Elfick is presented.