• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.823-829
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• 2005

We have newly constructed an in silico model of fermentative metabolism for Lactococcus lactis in order to analyze the characteristics of metabolite flux for dynamic network. A rigorous mathematical model for metabolic flux has been developed and simulation researches have been performed by using GEPASI program. In this simulation task, we were able to predict the whole flux distribution trend for lactate metabolism and analyze the flux ratio on the pyruvate branch point by using metabolic flux analysis(MFA). And we have studied flux control coefficients of key reaction steps in the model by using metabolic control analysis(MCA). The role of pyruvate branch seems to be essential for the secretion of lactate and other organic byproducts. Then we have made an effort to elucidate its metabolic regulation characteristics and key reaction steps, and find an optimal condition for the production of lactate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.273-276
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• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• Animal Bioscience
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• v.37 no.3
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• pp.522-535
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• 2024

Objective: Transition period is considered from 3 weeks prepartum to 3 weeks postpartum, characterized with dramatic events (endocrine, metabolic, and physiological) leading to occurrence of production diseases (negative energy balance/ketosis, milk fever etc). The objectives of our study were to analyze the periodic concentration of serum beta-hydroxy butyric acid (BHBA), glucose and oxidative markers along with identification, and validation of the putative markers of negative energy balance in buffaloes using in-silico and quantitative real time-polymerase chain reaction (qRT-PCR) assay. Methods: Out of 20 potential markers of ketosis identified by in-silico analysis, two were selected and analyzed by qRT-PCR technique (upregulated; acetyl serotonin o-methyl transferase like and down regulated; guanylate cyclase activator 1B). Additional two sets of genes (carnitine palmotyl transferase A; upregulated and Insulin growth factor; downregulated) that have a role of hepatic fatty acid oxidation to maintain energy demands via gluconeogenesis were also validated. Extracted cDNA (complementary deoxyribonucleic acid) from the blood of the buffaloes were used for validation of selected genes via qRTPCR. Concentrations of BHBA, glucose and oxidative stress markers were identified with their respective optimized protocols. Results: The analysis of qRT-PCR gave similar trends as shown by in-silico analysis throughout the transition period. Significant changes (p<0.05) in the levels of BHBA, glucose and oxidative stress markers throughout this period were observed. This study provides validation from in-silico and qRT-PCR assays for potential markers to be used for earliest diagnosis of negative energy balance in buffaloes. Conclusion: Apart from conventional diagnostic methods, this study improves the understanding of putative biomarkers at the molecular level which helps to unfold their role in normal immune function, fat synthesis/metabolism and oxidative stress pathways. Therefore, provides an opportunity to discover more accurate and sensitive diagnostic aids.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.181-182
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• 2002

Recentadvancesin '-omics ' technologies enable us to discover more diverse disease- relevant target proteins, which encourages us to find out more target-specific novel lead compounds as new drug candidates. Therefore, high-throughput screening (HTS) becomes an essential tool in this area. Among many HTS tools, in silico HTS is a very fast and cost-effective tool to try to derive a new lead compound for any new targets, especially when the target protein structures are known or readily modeled. (omitted)

• Korean Journal of Microbiology
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• v.55 no.1
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• pp.1-8
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• 2019

Due to the major role played by several species of Streptococcus in the etiology of periodontitis, it is important to assess the pattern of Streptococcus pathogenic pathways within the infected subgingival pockets using a bacterial specific 16S rRNA fragment. From the total of 50 patients with periodontitis included in the study, only 23 Streptococcal isolates were considered for further analyses, in which their 16S rRNA fragments were amplified and sequenced. Then, a comprehensive phylogenetic tree was constructed and in silico prediction was performed for the observed Streptococcal species. The phylogenetic analysis of the subgingival Streptococcal species revealed a high discrimination power of the 16S rRNA fragment to accurately identify three groups of Streptococcus on the species level, including S. salivarius (14 isolates), S. anginosus (5 isolates), and S. gordonii (4 isolates). The employment of state-of-art in silico tools indicated that each Streptococcal species group was characterized with particular transcription factors that bound exclusively with a different 16S rRNA-based secondary structure. In conclusion, the observed data of the present study provided in-depth insights into the mechanism of each Streptococcal species in its pathogenesis, which differ in each observed group, according to the differences in the 16S rRNA secondary structure it takes, and the consequent binding with its corresponding transcription factors. This study paves the way for further interventions of the in silico prediction, with the main conventional in vitro microbiota identification to present an interesting insight in terms of the gene expression pattern and the signaling pathway that each pathogenic species follows in the infected subgingival site.

• Development and Reproduction
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• v.18 no.1
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• pp.1-11
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• 2014

Early growth response 1 (Egr1) is a zinc-finger transcription factor to direct second-wave gene expression leading to cell growth, differentiation and/or apoptosis. While it is well-known that Egr1 controls transcription of an array of targets in various cell types, downstream target gene(s) whose transcription is regulated by Egr1 in the uterus has not been identified yet. Thus, we have tried to identify a list of potential target genes of Egr1 in the uterus by performing multi-step in silico promoter analyses. Analyses of mRNA microarray data provided a cohort of genes (102 genes) which were differentially expressed (DEGs) in the uterus between Egr1(+/+) and Egr1(-/-) mice. In mice, the frequency of putative EGR1 binding sites (EBS) in the promoter of DEGs is significantly higher than that of randomly selected non-DEGs, although it is not correlated with expression levels of DEGs. Furthermore, EBS are considerably enriched within -500 bp of DEG's promoters. Comparative analyses for EBS of DEGs with the promoters of other species provided power to distinguish DEGs with higher probability as EGR1 direct target genes. Eleven EBS in the promoters of 9 genes among analyzed DEGs are conserved between various species including human. In conclusion, this study provides evidence that analyses of mRNA expression profiles followed by two-step in silico analyses could provide a list of putative Egr1 direct target genes in the uterus where any known direct target genes are yet reported for further functional studies.

• Natural Product Sciences
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• v.29 no.2
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• pp.104-112
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• 2023

COVID-19 caused a catastrophe in human health. People infected with COVID-19 also suffer from various clinical illnesses during and after the infection. The Boerhavia diffusa plant is well known for its antihypertensive activity. ACE-II inhibitors and calcium channel blockers are reported as mechanisms for the antihypertensive activity of B. diffusa phytoconstituents. Various studies have said ACE-II is the virus's binding site to attack host cells. COVID-19 treatment commonly employs a variety of synthetic antiviral and steroidal drugs. As a result, other clinical illnesses, such as hypertension and hyperglycemia, emerge as serious complications. Safe and effective drug delivery is a prime objective of the drug development process. COVID-19 is treated with various herbal treatments; however, they are not widely used due to their low potency. Many herbal plants and formulations are used to treat COVID-19 infection, in which B. diffusa is the most widely used plant. The current study relies on discovering active phytoconstituents with ACE-II inhibitory activity in the B. diffusa plant. As a result, it can be used as a treatment option for patients with COVID-19 and related diseases. Different phytoconstituents of the B. diffusa plant were selected from the reported literature. The activity of phytoconstituents against ACE-II proteins has been studied. Molecular docking and ligand-protein interaction computation tools are used in the in-silico experiment. Physicochemical, drug-likeness, water solubility, lipophilicity, and pharmacokinetic parameters are used to evaluate phytoconstituents. Liriodenine has the best drug-likeness, bioactivity, and binding score characteristics among the selected ligands. The in-silico study aims to find the therapeutic potential of B. diffusa phytoconstituents against ACE-II. Targeting ACE-II also shows an effect against SARS-CoV-2. It can serve as a rationale for designing a drug for patient infected with COVID-19 and associated diseases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Journal of Microbiology and Biotechnology
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• v.18 no.11
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• pp.1773-1784
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• 2008

Effects of various industrially important carbon sources (glucose, sucrose, xylose, gluconate, and glycerol) on shikimic acid (SA) biosynthesis in Escherichia coli were investigated to gain new insight into the metabolic capability for overproducing SA. At the outset, constraints-based flux analysis using the genome-scale in silico model of E. coli was conducted to quantify the theoretical maximum SA yield. The corresponding flux distributions fueled by different carbon sources under investigation were compared with respect to theoretical yield and energy utilization, thereby identifying the indispensable pathways for achieving optimal SA production on each carbon source. Subsequently, a shikimate-kinase-deficient E. coli mutant was developed by blocking the aromatic amino acid pathway, and the production of SA on various carbon sources was experimentally examined during 51 batch culture. As a result, the highest production rate, 1.92 mmol SA/h, was obtained when glucose was utilized as a carbon source, whereas the efficient SA production from glycerol was obtained with the highest yield, 0.21 mol SA formed per mol carbon atom of carbon source consumed. The current strain can be further improved to satisfy the theoretically achievable SA production that was predicted by in silico analysis.

• Natural Product Sciences
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• v.27 no.2
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• pp.99-114
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• 2021

Type 2 diabetes mellitus (T2DM) and its complications are important noncommunicable diseases with high mortality rates. Protein tyrosine phosphatase 1B (PTP1B) and aldose reductase inhibitors are recently approached and advanced for T2DM and its complications therapy. Matricaria chamomilla L. is acknowledged as a worldwide medicinal herb that has many beneficial health effects as well as antidiabetic effects. Our research was designed to determine the most potential antidiabetic phytochemicals from M. chamomilla employing in silico study. 142 phytochemicals were obtained from the databases. The first screening employed iGEMdock and Swiss ADME, involving 93 phytochemicals. Finally, 30 best phytochemicals were docked. Molecular docking and visualization analysis were performed using Avogadro, AutoDock 4.2., and Biovia Discovery Studio 2016. Molecular docking results demonstrate that ligand-protein interaction's binding affinities were -5.16 to -7.54 kcal/mol and -5.30 to -12.10 kcal/mol for PTP1B and aldose reductase protein targets respectively. In silico results demonstrate that M. chamomilla has potential antidiabetic phytochemical compounds for T2DM and its complications. We recommended anthecotulide, quercetin, chlorogenic acid, luteolin, and catechin as antidiabetic agents due to their binding affinities against both PTP1B and aldose reductase protein. Those phytochemicals' significant efficacy and potential as antidiabetic must be investigated in further advanced research.