• Genomics & Informatics
• /
• v.4 no.4
• /
• pp.182-187
• /
• 2006

6-Phosphogluconolactonase (6PGL) is one of the key enzymes in the ubiquitous pathways of central carbon metabolism, but bacterial 6PGL had been long known as a missing enzyme even after complete bacterial genome sequence information became available. Although recent experimental characterization suggests that there are two types of 6PGLs (DevB and YbhE), their phylogenetic distribution is severely biased. Here we present that proteins in COG group previously described as 3-oarboxymuconate cyclase (COG2706) are actually the YbhE-type 6PGLs, which are widely distributed in Proteobacteria and Fimicutes. This case exemplifies how erroneous functional description of a member in the reference database commonly used in transitive genome annotation cause systematic problem in the prediction of genes even with universal cellular functions.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.10 no.5
• /
• pp.418-424
• /
• 2005

The complexity of the biological system/biological systems has been fascinating and challenging for a long time. With the advent of mathematical tools with various omics technology, systems biology was born and is already ubiquitous in every area of biology and biotechnology. Microalgal biotechnology is no exception in this new trend. As tens of microalgal genomes become publicly available on the Internet, vast amounts of data from genomics, transcriptomics, and proteomics are reported everyday. Though there has not yet been enough data gathered on microalgal metabolomics, the in silica models for relatively simple cyanobacteria or for organelles, such as chloroplasts, will appear presently. With the help of systems biology, a more in-depth understanding of microalgae will be possible. Consequently, most industrially-interested microalgae can be metabolically redesigned/reconfigured as cell factories. Microalgae will be served as the hosts in white biotechnology.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.10 no.5
• /
• pp.462-469
• /
• 2005

A top-down approach is known to be a useful and effective technique for the design and analysis of metabolic systems. In this Study, we have constructed a grouped metabolic network for Lactococcus lactis under aerobic conditions using grouped enzyme kinetics. To test the usefulness of grouping work, a non-grouped system and grouped systems were compared quantitatively with each other. Here, grouped Systems were designed as two groups according to the extent of grouping. The overall simulated flux values in grouped and non-grouped models had pretty similar distribution trends, but the details on flux ratio at the pyruvate branch point showed a little difference. This result indicates that our grouping technique can be used as a good model for complicated metabolic networks, however, for detailed analysis of metabolic network, a more robust mechanism Should be considered. In addition to the data for the pyruvate branch point analysis, Some major flux control coefficients were obtained in this research.

• Journal of Microbiology and Biotechnology
• /
• v.30 no.6
• /
• pp.793-803
• /
• 2020

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

• Genomics & Informatics
• /
• v.1 no.1
• /
• pp.1-6
• /
• 2003

In conclusion, the seemingly fuzzy and disorganized data of biology with thousands of different layers ranging from molecule to the Internet have refused so far to be mapped precisely and predicted successfully by mathematicians, physicists or computer scientists. Genomics and bioinformatics are the fields that process such complex data. The insights on the nature of biological entities as complex interaction networks are opening a door toward a generalization of the representation of biological entities. The main challenge of genomics and bioinformatics now lies in 1) how to data mine the networks of the domains of bioinformatics, namely, the literature, metabolic pathways, and proteome and structures, in terms of interaction; and 2) how to generalize the networks in order to integrate the information into computable genomic data for computers regardless of the levels of layer. Once bioinformatists succeed to find a general principle on the way components interact each other to form any organic interaction network at genomic scale, true simulation and prediction of life in silico will be possible.

• Molecules and Cells
• /
• v.20 no.3
• /
• pp.348-353
• /
• 2005

Using in silico approaches and RACE we cloned a full length trinucleotide (CAG) repeat-containing cDNA (cag-3). The cDNA is 2478 bp long and the deduced polypeptide consists of 140 amino acids of which 73 are glutamines. The genomic sequence spans approximately 79 kb on mouse chromosome 7 and the gene is composed of four exons. Standard and real-time PCR analyses of several mouse tissues showed that the gene is exclusively expressed in the brain and is not detected in embryonic stages. Within the brain, it is expressed throughout the forebrain region with predominant expression in the hypothalamus and olfactory bulb and very low levels in the mid- and hindbrain.

• Advances in nano research
• /
• v.15 no.2
• /
• pp.165-174
• /
• 2023

The study investigated the effect of geometric structures of nano-patterned surfaces, such as peak sharpness, height, width, aspect ratio, and spacing, on mechano-bactericidal properties. Here, in silico models were developed to explain surface interactions with Escherichia coli. Numerical solutions were performed based on the finite element method and verified by the artificial neural network method. An E. coli cell adhered to the nano surface formed elastic and creep deformation models, and the cells' maximum deformation, maximum stress, and maximum strain were calculated. The results determined that the increase in peak sharpness, aspect ratio, and spacing values increased the maximum deformation, maximum stress, and maximum strain on E. coli cell. In addition, the results showed that FEM and ANN methods were in good agreement with each other. This study proved that the geometrical structures of nano-patterned surfaces have an important role in the mechano-bactericidal effect.

• Journal of Integrative Natural Science
• /
• v.5 no.4
• /
• pp.216-219
• /
• 2012

Structure-based drug design possibly benefit from in silico methods that precisely predict the binding affinity of small molecules to target macromolecules. There are many limitations arise from the difficulty of predicting the binding affinity of a small molecule to a biological target with the current scoring functions. There is thus a strong interest in novel methodologies based on MD simulations that claim predictions of greater accuracy than current scoring functions, helpful for a regular use designed for drug discovery in the pharmaceutical industry. Herein, we report a short review on free energy calculations using MMPBSA method a useful method in structure based drug discovery.

• International Journal of Industrial Entomology and Biomaterials
• /
• v.15 no.2
• /
• pp.115-122
• /
• 2007

As the genome of B.mori is available in GenBank and the EST database of B.mori is expanding, identification of novel genes of B.mori is conceivable by data-mining techniques. We used the in silico cloning method to get the vacuolar-type $H^+-ATP$ synthetase (V-ATPase) c subunit (16 kDa proteolipid subunit) gene of B.mori and analysed with bioinformatics tools. The result was confirmed by RT-PCR and sequencing. The V-ATPase c subunit cDNA contains a 468 bp ORF. The ORF encoded a 155-residue protein that showed extensive homology with V-ATPase c subunits from other 15 species and contained four membrane-spanning helices. Tissue expression pattern analysis revealed that V-ATPase c expressed strongly in Malpighian tubules, not in fat body. This gene has been registered in GenBank under the accession number EU082222.

• Journal of Integrative Natural Science
• /
• v.6 no.2
• /
• pp.100-103
• /
• 2013

Using computational approaches we can dock small molecules into the structures of Macromolecular targets and then score their potential complementarity to binding sites is widely used in hit identification and lead optimization techniques. This review seeks to provide the application of docking in structure-based drug design (binding mode prediction, Lead Identification and Lead optimization), and also discussed how to manage errors in docking methodology in order to overcome certain limitations of docking and scoring algorithm.