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

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of Microbiology and Biotechnology
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• v.17 no.3
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• pp.496-510
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• 2007

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-a-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an integrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 $(2^3)$ fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

• IMMUNE NETWORK
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• v.9 no.5
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• pp.147-152
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• 2009

Ascorbate (vitamin C) is a cofactor for a number of metabolic enzymes and is an indisputable essential vitamin C for humans. However, the potential of ascorbate as an anticancer agent has been a topic of controversy. A number of previous reports have addressed both positive aspects and limitations of ascorbate in cancer therapy. In this review, we briefly summarize the potential antitumor effects of ascorbate and its prospects for clinical use.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.1003-1010
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• 2008

The objective of the present study was to identify differences in the expression levels of liver proteins between healthy and ketotic cows, establish a liver metabolic interrelationship of ketosis and elucidate the metabolic characteristics of the liver during ketosis. Liver samples from 8 healthy multiparous Hostein cows and 8 ketotic cows were pooled by health status and the proteins were separated by two-dimensional-electrophoresis (2D-E). Statistical analysis of gels was performed using PDQuest software 8.0. The differences in the expression levels of liver proteins (p<0.05) between ketotic and healthy cows were identified by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry. Five enzymes/proteins were identified as being differentially expressed in the livers of ketotic cows: expression of 3-hydroxyacyl-CoA dehydrogenase type-2 (HCDH), acetyl-coenzyme A acetyltransferase 2 (ACAT) and elongation factor Tu (EF-Tu) were down-regulated, whereas that of alpha-enolase and creatine kinase were up-regulated. On the basis of this evidence, it could be presumed that the decreased expression of HCDH, which is caused by high concentrations of acetyl-CoA in hepatic cells, in the livers of ketotic cows, implies reduced fatty acid ??oxidation. The resultant high concentrations of acetyl-CoA and acetoacetyl CoA would depress the level of ACAT and generate more ??hydroxybutyric acid; high concentrations of acetyl-CoA would also accelerate the Krebs Cycle and produce more ATP, which is stored as phosphocreatine, as a consequence of increased expression of creatine kinase. The low expression level of elongation factor Tu in the livers of ketotic cows indicates decreased levels of protein synthesis due to the limited availability of amino acids, because the most glucogenic amino acids sustain the glyconeogenesis pathway; thus increasing the level of alpha-enolase. Decreased protein synthesis also promotes the conversion of amino acids to oxaloacetate, which drives the Krebs Cycle under conditions of high levels of acetyl-CoA. It is concluded that the livers of ketotic cows possess high concentrations of acetyl-CoA, which through negative feedback inhibited fatty acid oxidation; show decreased fatty acid oxidation, ketogenesis and protein synthesis; and increased gluconeogenesis and energy production.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.290-296
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• 2019

Objective: Pigs share many physiological, anatomical and genomic similarities with humans, which make them suitable models for biomedical researches. Understanding the genetic status of Yucatan miniature pigs (YMPs) and their association with human diseases will help to assess their potential as biomedical model animals. This study was performed to identify non-synonymous single nucleotide polymorphisms (nsSNPs) in selective sweep regions of the genome of YMPs and present the genetic nsSNP distributions that are potentially associated with disease occurrence in humans. Methods: nsSNPs in whole genome resequencing data from 12 YMPs were identified and annotated to predict their possible effects on protein function. Sorting intolerant from tolerant (SIFT) and polymorphism phenotyping v2 analyses were used, and gene ontology (GO) network and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Results: The results showed that 8,462 genes, encompassing 72,067 nsSNPs were identified, and 118 nsSNPs in 46 genes were predicted as deleterious. GO network analysis classified 13 genes into 5 GO terms (p<0.05) that were associated with kidney development and metabolic processes. Seven genes encompassing nsSNPs were classified into the term associated with Alzheimer's disease by referencing the genetic association database. The KEGG pathway analysis identified only one significantly enriched pathway (p<0.05), hsa04080: Neuroactive ligand-receptor interaction, among the transcripts. Conclusion: The number of deleterious nsSNPs in YMPs was identified and then these variants-containing genes in YMPs data were adopted as the putative human diseases-related genes. The results revealed that many genes encompassing nsSNPs in YMPs were related to the various human genes which are potentially associated with kidney development and metabolic processes as well as human disease occurrence.

• Molecules and Cells
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• v.26 no.2
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• pp.206-211
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• 2008

HI0381 of Haemophilus influenzae was investigated by circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. HI0381 is a 153-residue peptidoglycan-associated outer membrane lipoprotein, and a part of the larger Tol/Pal network. Here, we report its backbone $^1H$, $^{15}N$, and $^{13}C$ resonance assignments, and secondary structure predictions. About 97% of all of the $^1HN$, $^{15}N$, $^{13}CO$, $^{13}C{\alpha}$, and $^{13}C{\beta}$ resonances covering 131 non-proline residues of the 134 residue, mature protein, were clarified by sequential and specific assignments. CSI and TALOS analyses revealed that HI0381 contains five ${\alpha}$-helices and five ${\beta}$-strands. To characterize the structure of HI0381, the effects of pH and salt concentration were investigated by CD. In addition, the structural changes occurring when HI0381 was in a membranous environment were investigated by comparing its HSQC spectra and CD data in buffer and in DPC micelles; the results showed that helix ${\alpha}4$ and strand ${\beta}4$ became aligned with the membrane. We conclude that the conformation of HI0381 is affected by the membrane environment, implying that its folded state is directly related to its function.

• IMMUNE NETWORK
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• v.6 no.4
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• pp.169-178
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• 2006

Background: Adipose tissues were initially introduced as energy storages, but recently they have become famous as an endocrine organ which produces and secretes various kinds of molecules to make physiologic and metabolic changes in human body. It has been studied that these molecules are secreted in abundance as the adipose tissue becomes bigger along with obesity. Furthermore, it has been found that they are mediating systemic inflammation and generation of metabolic diseases such as type 2 diabetes and atherosclerosis. On the basis of these, we studied previous papers which have been researched about the interaction between preadipocytes and macrophages, adipose tissues and lymph nodes, and adipose tissue secreting molecules. Results: Firstly, preadipocytes and macrophages are expressing similar transcriptomes and proteins, and preadipocytes can be converted to mature macrophages which have phagocytic activity. Moreover, the monocytes, which initially located in the bone marrow, are filtrated to the adipose tissue by monocyte chemotatic protein-1 and are matured to macrophages by colony stimulating factor-1. Secondly, adipose tissues and their associated lymph nodes are interacting each other in terms of energy efficiency. Lymph nodes promote lipolysis in adipose tissues, and polyunsaturated fatty acids in adipocytes become energy sources for dendritic cells. Lastly, adipose tissues produce and secrete proinflammatory molecules such as leptin, adiponectin, TNF-${\alpha}$, IL-6, and acute phase proteins, which induce the inflammation and potentially generate metabolic diseases. Conclusion: According to these, we can link adipose tissues to inflammation, but we need to affirm the actual levels and roles of adipose tissue-derived proinflammatory molecules in human body.

• KSBB Journal
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• v.31 no.4
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• pp.193-199
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• 2016

Global warming caused from the heavy consumption of fossil fuel is one of the biggest problems to be solved. Biofuel has been gained more attention as an alternative to reduce the consumption of fossil fuel. Recently, butanol produced from the genus Clostridium has been considered as one of the promising alternatives for gasoline, fossil based fuel. Nevertheless, the lack of the genome-engineering tools for the genus Clostridium is the major hurdle for the economic production of butanol. More recently, genome engineering tools have been developed for metabolic engineering of butanol-producing Clostridium species, which includes genome scale network model and genome editing tools on the basis of mobile group II introns and CRISPR/Cas system. In this study, the genome engineering tools for butanol-producing Clostridium species have been reviewed with a brief future perspective.

• Journal of Microbiology and Biotechnology
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• v.27 no.7
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• pp.1345-1358
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• 2017

The impact of overproduction of a heterologous protein on the metabolic system of host Lactococcus lactis was investigated. The protein expression profiles of L. lactis IL1403 containing two near-identical plasmids that expressed high- and low-level of the green fluorescent protein (GFP) were examined via shotgun proteomics. Analysis of the two strains via high-throughput LC-MS/MS proteomics identified the expression of 294 proteins. The relative amount of each protein in the proteome of both strains was determined by label-free quantification using the spectral counting method. Although expression level of most proteins were similar, several significant alterations in metabolic network were identified in the high GFP-producing strain. These changes include alterations in the pyruvate fermentation pathway, oxidative pentose phosphate pathway, and de novo synthesis pathway for pyrimidine RNA. Expression of enzymes for the synthesis of dTDP-rhamnose and N-acetylglucosamine from glucose was suppressed in the high GFP strain. In addition, enzymes involved in the amino acid synthesis or interconversion pathway were downregulated. The most noticeable changes in the high GFP-producing strain were a 3.4-fold increase in the expression of stress response and chaperone proteins and increase of caseinolytic peptidase family proteins. Characterization of these host expression changes witnessed during overexpression of GFP was might suggested the metabolic requirements and networks that may limit protein expression, and will aid in the future development of lactococcal hosts to produce more heterologous protein.