• Genomics & Informatics
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• v.5 no.3
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• pp.124-128
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• 2007

Microarray technology enables us to measure the expression of tens of thousands of genes simultaneously under various experimental conditions. Clustering analysis is one of the most successful methods for analyzing microarray data using the assumption that co-expressed genes may be co-regulated. It is important to extract meaningful clusters from a long unordered list of clusters and to evaluate the functional homogeneity and heterogeneity of clusters. Many quality measures for clustering results have been suggested in different conditions. In the present study, we consider biological pathways as a collection of biological knowledge and used them as a reference for measuring the quality of clustering results and functional homogeneities. PathTalk visualizes and evaluates functional relationships between gene clusters and biological pathways.

• Tuberculosis and Respiratory Diseases
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• v.87 no.1
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• pp.12-21
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• 2024

The management of severe asthma presents a significant challenge in asthma treatment. Over the past few decades, remarkable progress has been made in developing new treatments for severe asthma, primarily in the form of biological agents. These advances have been made possible through a deeper understanding of the underlying pathogenesis of asthma. Most biological agents focus on targeting specific inflammatory pathways known as type 2 inflammation. However, recent developments have introduced a new agent targeting upstream alarmin signaling pathways. This opens up new possibilities, and it is anticipated that additional therapeutic agents targeting various pathways will be developed in the future. Despite this recent progress, the mainstay of asthma treatment has long been inhalers. As a result, the guidelines for the appropriate use of biological agents are not yet firmly established. In this review, we aim to emphasize the current state of biological therapy for severe asthma and provide insights into its future prospects.

• Genomics & Informatics
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• v.6 no.4
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• pp.210-222
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• 2008

Due to the polygenic nature of cancer, it is believed that breast cancer is caused by the perturbation of multiple genes and their complex interactions, which contribute to the wide aspects of disease phenotypes. A systems biology approach for the identification of subnetworks of interconnected genes as functional modules is required to understand the complex nature of diseases such as breast cancer. In this study, we apply a 3-step strategy for the interpretation of microarray data, focusing on identifying significantly perturbed metabolic pathways rather than analyzing a large amount of overexpressed and underexpressed individual genes. The selected pathways are considered to be dysregulated functional modules that putatively contribute to the progression of disease. The subnetwork of protein-protein interactions for these dysregulated pathways are constructed for further detailed analysis. We evaluated the method by analyzing microarray datasets of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Using the strategy of microarray analysis, we selected several significantly perturbed pathways that are implicated in the regulation of progression of breast cancers, including the extracellular matrix-receptor interaction pathway and the focal adhesion pathway. Moreover, these selected pathways include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting interesting perturbed pathways that putatively play a role in the progression of breast cancer and provides an improved interpretability of networks of protein-protein interactions.

• Genomics & Informatics
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• v.3 no.2
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• pp.47-51
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• 2005

Molecular mechanisms of biological processes are typically represented as 'pathways' that have a graph­analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and the semantics is embedded implicitly. The kinds of interactions that connect biological entities are likewise diverse. Consequently, how to model or process pathway data is not a trivial issue. In this review article, we give an overview of the challenges in pathway database development by taking the INOH project as an example.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.373-378
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• 2005

The diagrammatic language for pathways is widely used for representing systems knowledge as a network of causal relations. Biologists infer and hypothesize with pathways to design experiments and verify models, and to identify potential drug targets. Although there have been many approaches to formalize pathways to simulate a system, reasoning with incomplete and high level knowledge has not been possible. We present a qualitative formalization of a pathway language with incomplete causal descriptions and its translation into propositional temporal logic to automate the reasoning process. Such automation accelerates the identification of drug targets in pathways.

• Genomics & Informatics
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• v.7 no.3
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• pp.168-170
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• 2009

The explosion in biological data resulting from high-throughput experiments requires new software tools to manipulate and display pathways in a way that can integrate disparate sources of information. A visual Java-based CAD tool for drawing and annotating biological pathways with semiautomatic image-processing features is described in this paper. The result of the image-editing process is an XML file for the appropriate links. This tool integrates the pathway images and XML file sources. The system has facilities for linking graphical objects to external databases and is capable of reproducing existing visual representations of pathway maps.

• Molecules and Cells
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• v.44 no.7
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• pp.425-432
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• 2021

Aging is associated with functional and structural declines in organisms over time. Organisms as diverse as the nematode Caenorhabditis elegans and mammals share signaling pathways that regulate aging and lifespan. In this review, we discuss recent combinatorial approach to aging research employing C. elegans and mammalian systems that have contributed to our understanding of evolutionarily conserved aging-regulating pathways. The topics covered here include insulin/IGF-1, mechanistic target of rapamycin (mTOR), and sirtuin signaling pathways; dietary restriction; autophagy; mitochondria; and the nervous system. A combinatorial approach employing high-throughput, rapid C. elegans systems, and human model mammalian systems is likely to continue providing mechanistic insights into aging biology and will help develop therapeutics against age-associated disorders.

• Journal of Microbiology and Biotechnology
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• v.28 no.2
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• pp.267-274
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• 2018

Lipids in microalgae are energy-rich compounds and considered as an attractive feedstock for biodiesel production. To redirect carbon flux from competing pathways to the fatty acid synthesis pathway of Tetraselmis sp., we used three types of chemical inhibitors that can block the starch synthesis pathway or photorespiration, under nitrogen-sufficient and nitrogen-deficient conditions. The starch synthesis pathway in chloroplasts and the cytosol can be inhibited by 3-(3,4-dichlorophenyl)-1,1-dimethylurea and 1,2-cyclohexane diamine tetraacetic acid (CDTA), respectively. Degradation of glycine into ammonia during photorespiration was blocked by aminooxyacetate (AOA) to maintain biomass concentration. Inhibition of starch synthesis pathways in the cytosol by CDTA increased fatty acid productivity by 27% under nitrogen deficiency, whereas the blocking of photorespiration in mitochondria by AOA was increased by 35% under nitrogen-sufficient conditions. The results of this study indicate that blocking starch or photorespiration pathways may redirect the carbon flux to fatty acid synthesis.

• Animal Bioscience
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• v.37 no.3
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• pp.428-436
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• 2024

Objective: This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population. Methods: The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows. Results: Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population. Conclusion: This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.

• Proceedings of the Zoological Society Korea Conference
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• 2000.10a
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• pp.201.2-201
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• 2000

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