Production of biofuels has been one of the promising efforts in biotechnology in the past few decades. The perspective of these efforts can be reduction of increasing demands for fossil fuels and consequently reducing environmental pollution. Nonetheless, most previous approaches did not succeed in obviating many big challenges in this way. In recent years systems biology with the help of microorganisms has been trying to overcome these challenges. Unicellular cyanobacteria are widespread phototrophic microorganisms that have capabilities such as consuming solar energy and atmospheric carbon dioxide for growth and thus can be a suitable chassis for the production of valuable organic materials such as biofuels. For the ultimate use of metabolic potential of cyanobacteria, it is necessary to understand the reactions that are taking place inside the metabolic network of these microorganisms. In this study, we developed a Java tool to reconstruct an integrated metabolic network of a cyanobacterium (Synechocystis sp. PCC 6803). We merged three existing reconstructed metabolic networks of this microorganism. Then, after modeling for biofuel production, the results from flux balance analysis (FBA) disclosed an increased yield in biofuel production for ethanol, isobutanol, 3-methyl-1-butanol, 2-methyl-1-butanol, and propanol. The numbers of blocked reactions were also decreased for 2-methyl-1-butanol production. In addition, coverage of the metabolic network in terms of the number of metabolites and reactions was increased in the new obtained model.

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Nowadays, studying microorganisms has become faster and deeper than the last decades, thanks to the modeling of genome-scale metabolic networks. Completed genome sequencing projects of microorganisms and annotating these sequences have provided a worthwhile platform for reconstructing and modeling genome-scale metabolic networks. The genome-scale metabolic network reconstruction is a laborious and time-consuming task which needs an extensive study and search in different types of databases. Furthermore, it also requires an iterative process of creating and curating the obtained network, particularly with experimental methods. Hence, different types of reconstructions and models of a targeted microorganism can be found with different qualities, as the goal and need of researchers differ. Due to these circumstances, scientists have to continue with only one of the reconstructed metabolic networks of each microorganism and ignore the rest in their in silico works. It is clear that having a tool which merges different metabolic networks of a single organism can be a useful and effective way to study them with minimal cost and time. To meet this need, we have developed iMet , the standalone graphical user interface (GUI) software tool to merge multiple reconstructed metabolic networks of microorganisms. As a case study, we merged three reconstructed metabolic networks of a cyanobacterium using iMet, and then all of them (including the new merged one) became modeled. The results of our evaluations including Flux Balance Analysis (FBA), revealed enhancing metabolic network coverage as well as increasing yield of desired products in the new obtained model.

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Background: Small supernumerary marker chromosomes (sSMCs) are chromosomal fragments with abnormal structures found in patients with fertility problems and developmental delay. They may be detected in amniotic cell karyotypes. sSMCs are categorized as hereditary or de novo. Here, we describe a case of prenatal de novo 4q11q12 sSMC and its molecular cytogenetic features which had no apparent phenotypic abnormality. Case: The fetus of a 36-yr-old pregnant woman was detected positive for Down’s syndrome (trisomy 21) at the 16th wk of gestation. Quantitative fluorescent polymerase chain reaction technique was applied for the rapid detection of numerical aneuploidy of chromosomes X, Y, 13, 18, and 21 microsatellites. Array comparative genomic hybridization (array CGH) technique was also conducted following the karyotype analysis of amniotic cells. The karyotype analysis was also done for the parents. Quantitative fluorescent polymerase chain reaction result revealed a male fetus with a normal chromosomal pattern, while the amniocentesis karyotype analysis identified a male fetus with a marker chromosome (47, XY, +mar), and the sSMC were existing in 100% of amniocyte metaphase spreads. The parents’ normal karyotypes indicated that the sSMC was de novo. Array CGH analysis revealed a 6.48-Mb duplication at 4q11q12. Eventually, the parents decided to terminate the pregnancy by legal abortion. Conclusion: Our study highlights the importance of the application of array CGH in combination with karyotype analysis for rapid and precise prenatal diagnosis of partial aneuploidy region.  Key words: Prenatal diagnosis, Array CGH, Chromosome 4, Chromosome markers.