Journal of Bioengineering & Biomedical Science

This work reports the use of metabolic profiling and PCR techniques as a means of studying the growth of Streptomyces tenjimariensis . Metabolic profiles were created from gas chromatography–mass spectrometry analysis of extracts prepared through extraction of the intracellular contents followed by fractionation of the polar metabolites and trimethylsilyl derivatization. All chromatograms yielded profiles of between 100 and 200 metabolites.The major metabolite groups including organic acids, amino acids, sugars, sugar alcohols, and phosphatidyl sugar alcohols were identified and their profiles correlated to growth stage and antimicrobial activity. Statistical analysis including analysis of variance, hierarchical cluster analysis, and principal component analysis suggested that differences between the major growth phases could be distinguished, and may be used to establish patterns amongst key metabolites. The expression of four enzyme genes involving substrate utilization and istamycin biosynthesis was detected with reverse transcriptase-PCR across four time points during the growth of S. tenjimariensis .

The modern science mainly treats the biochemical basis of sequencing in bio-macromolecules and processes in medicine and biochemistry. One can ask weather the language of biochemistry is the adequate scientific language to explain the phenomenon in that science. Is there maybe some other language, out of biochemistry, that determines how the biochemical processes will function and what the structure and organization of life systems will be? The research results provide some answers to these questions. They reveal to us that the process of sequencing in bio-macromolecules is conditioned and determined not only through biochemical, but also through cybernetic and information principles. Many studies have indicated that analysis of protein sequence codes and various sequence- based prediction approaches, such as predicting drug-target interaction networks [ 14 ], predicting functions of proteins [ 15 , 18 ], analysis and prediction of the metabolic stability of proteins [ 16 ], predicting the network of substrate-enzyme- product triads [ 7 ], membrane protein type prediction [ 1 , 2 , 5 ]. protein structural class prediction [ 4 , 12 ], protein secondary structure prediction [ 6 , 11 ], enzyme family class prediction [ 3 , 11 ], identifying cyclin proteins [ 20 ], protein subcellular location prediction [ 9 , 10 , 17 , 19 ] , among many others as summarized in a recent review [ 15 ] , can timely provide very useful information and insights for both basic research and drug design and hence are widely welcome by science community. The present study is attempted to develop a novel sequence-based method for studying insulin in hopes that it may become a useful tool in the relevant areas.