Enterobacter sp. FY-07 can produce bacterial cellulose (BC) under aerobic and anaerobic conditions. Three potential BC synthesis gene clusters (bcsI, bcsII and bcsIII) of Enterobacter sp. FY-07 have been predicted using genome sequencing and comparative genome analysis, in which bcsIII was confirmed as the main contributor to BC synthesis by gene knockout and functional reconstitution methods. Protein homology, gene arrangement and gene constitution analysis indicated that bcsIII had high identity to the bcsI operon of Enterobacter sp. 638; however, its arrangement and composition were same as those of BC synthesizing operon of G. xylinum ATCC53582 except for the flanking sequences. According to the BC biosynthesizing process, oxygen is not directly involved in the reactions of BC synthesis, however, energy is required to activate intermediate metabolites and synthesize the activator, c-di-GMP. Comparative transcriptome and metabolite quantitative analysis demonstrated that under anaerobic conditions genes involved in the TCA cycle were downregulated, however, genes in the nitrate reduction and gluconeogenesis pathways were upregulated, especially, genes in three pyruvate metabolism pathways. These results suggested that Enterobacter sp. FY-07 could produce energy efficiently under anaerobic conditions to meet the requirement of BC biosynthesis.

In this paper, we investigate the potential application of quantum computation for constructing pseudo-random number generators (PRNGs) and further construct a novel PRNG based on quantum random walks (QRWs), a famous quantum computation model. The PRNG merely relies on the equations used in the QRWs, and thus the generation algorithm is simple and the computation speed is fast. The proposed PRNG is subjected to statistical tests such as NIST and successfully passed the test. Compared with the representative PRNG based on quantum chaotic maps (QCM), the present QRWs-based PRNG has some advantages such as better statistical complexity and recurrence. For example, the normalized Shannon entropy and the statistical complexity of the QRWs-based PRNG are 0.999699456771172 and 1.799961178212329e-04 respectively given the number of 8 bits-words, say, 16Mbits. By contrast, the corresponding values of the QCM-based PRNG are 0.999448131481064 and 3.701210794388818e-04 respectively. Thus the statistical complexity and the normalized entropy of the QRWs-based PRNG are closer to 0 and 1 respectively than those of the QCM-based PRNG when the number of words of the analyzed sequence increases. It provides a new clue to construct PRNGs and also extends the applications of quantum computation.

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