The hydrogenase (Hyd) isolated from the cytoplasmic membrane of Wolinellu succinogenes consists of three polypeptides (HydA, HydB and HydC) and contains cytochrome h (6.4 pmol/g protein), which was reduced upon the addition of H2. The enzyme catalyzed the reduction of 2,3-dimethyl-1, 4-naphthoquinone with H2, in contrast to an earlier preparation which was made up of HydA and HydB only and did not contain cytochrome b (Unden, G., Bocher, R., Knecht, J. & Kroger, A. (1982) FEBS Lett. 145, 230-234). This suggests that HydC is a cytochrome b which serves as a mediator in the electron transfer from H2 to the quinone.The hydrogenase genes were cloned, sequenced and identified by sequence comparison with the N-termini of the three subunits. The three genes were arranged in the order hydA, hydB, hydC, with the transcription start site in front of hydA, and were present only once on the genome. Separated by an intergene region of 69 nucleotides, hydC was followed by at least two more open reading frames of unknown function. The amino acid sequences derived from hydA, hydB and hydC were similar to those of the membrane Ni-hydrogenases of seven other bacteria. HydA and HydB also showed similarity to the small and the large subunits of periplasmic Ni-hydrogenases. HydC was predicted to contain four hydrophobic segments which might span the bacterial membrane. Two histidine residues located in hydrophobic segments are conserved in the corresponding sequences of the other membrane hydrogenases and might ligate the haem B.

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The distribution process in business-to-business materials trading is among the most complex and in transparent ones within logistics. The highly volatile environment requires continuous adaptations by the responsible decision-makers, who face a substantial number of potential improvement actions with conflicting goals, such as simultaneously maintaining a high service level and low costs. Simulation-optimisation approaches have been proposed in this context, for example based on evolutionary algorithms. But, on real-world system dimensions, they face impractically long computation times. This paper addresses this challenge in two principal streams. On the one hand, reinforcement learning is investigated to reduce the response time of the system in a concrete decision situation. On the other hand, domain-specific information and defining equivalent solutions are exploited to support a metaheuristic algorithm. For these approaches, we have developed suitable implementations and evaluated them with subsets of real-world data. The results demonstrate that reinforcement learning exploits the idle time between decision situations to learn which decisions might be most promising, thus adding computation time but significantly reducing the response time. Using domain-specific information reduces the number of required simulation runs and guides the search for promising actions. In our experimentation, defining equivalent solutions decreased the number of required simulation runs up to 15%.