• ALGAE
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• v.38 no.1
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• pp.1-22
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• 2023

An enormous body of research is focused on finding ways to commercialize carotenoids produced by the unicellular green alga, Haematococcus, often without the benefit of a sound phylogenetic assessment. Evidence of cryptic diversity in the genus means that comparing results of pigment studies may be confounded by the absence of a phylogenetic framework. Moreover, previous work has identified unnamed strains that are likely candidates for species status. We reconstructed the phylogeny of an expanded sampling of Haematococcus isolates utilizing data from nuclear ribosomal markers (18S rRNA gene, 26S rRNA gene, internal transcribed spacer [ITS]-1, 5.8S rRNA gene, and ITS-2) and the rbcL gene. In addition, we gathered morphological, ultrastructural and pigment data from key isolates of Haematococcus. Our expanded data and taxon sampling support the concept of a new species, H. privus, found exclusively in North America. Despite overlap in numerous morphological traits, results indicate that ratios of protoplast length to width and akinete diameter may be useful for discriminating Haematococcus lineages. High growth rate and robust astaxanthin yield indicate that H. rubicundus (SAG 34-1c) is worthy of additional scrutiny as a pigment source. With the description of H. privus, the evidence supports the existence of at least five, species-level lineages in the genus. Our phylogenetic assessment provides the tools to frame future pigment investigations of Haematococcus in an updated evolutionary context. In addition, our investigation highlighted open questions regarding polyploidy and sexuality in Haematococcus which demonstrate that much remains to be discovered about this green flagellate.

• Journal of Pharmaceutical Investigation
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• v.21 no.3
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• pp.179-188
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• 1991

An HPLC assay method of a drug to be applied to the pharmacokinetic studies of the drug should be completely validated. The validation process for an HPLC assay method in a biological sample was discussed using the data obtained from the development of HPLC method for the simultaneous quantitation of verapamil and norverapamil in human serum. The validation criteria included were specificity, linearity, accuracy, precision, sensitivity, recovery, drug stability, and ruggedness of an assay method.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.261-261
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• 2006

Membranes are a central feature of all biological systems and their ability to control many cellular processes is critically important. As a result, a better understanding of how molecules bind to biological membranes is an active area of research. In this report, the interaction between our biomimetic structures and different biological membranes is reported using both model vesicle and in vitro bacterial cell experiments. These results show that lipid composition is more important for selectivity than overall net charge. An effort is made to connect model vesicle studies with in vitro data and naturally occurring lipid compositions.

• 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.379-382
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• 2005

We demonstrate workflows in biological data retrieval and analysis using the DDBJ Web Service; specifically introduce a workflow for the analysis of proteins or proteomics data sets. The workflow mechanically extracts the gene whose protein structure and function are known from all the genes of a human genome in Ensembl (http://www.ensembl.org/) based on cross-references among Ensembl, Swiss-Prot (http://www.ebi.ac.uk/swissprot) and PDB (Protein Data Bank; http://www.wwpdb.org/). The workflow discovered ‘hidden’ linkages among databases. We will be able to integrate distributed and heterogeneous data systems into workflows, if they are provided based on standards for Web services.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.142-146
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• 1994

This paper proposes an fill-factor algorithm that determines embedding parameters which are needed in optimal attractor reconstruction. For reliability test, using this algorithm, we reconstructs the attractor of numerical chaotic data such as Duffing equation, Lorenz equation and Rossler equation whose embedding parameters are known. Also we reconstructs the attractor of experimental data and evaluates correlation dimension. Experimental data used in this paper are 38 ECG data of AHA(American Heart Association) ECG database. For numerical chaotic data, correlation dimension and Lyapunov exponent of reconstructed attractor are very close to those of attractor using original coordinate system.

• Molecules and Cells
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• v.25 no.2
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• KOGO NEWS
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• v.6 no.1
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• pp.29-33
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• 2006

Gene set analysis is a new concept and method. to analyze and interpret microarray gene expression data and tries to extract biological meaning from gene expression data at gene set level rather than at gene level. Compared with methods which select a few tens or hundreds of genes before gene ontology and pathway analysis, gene set analysis identifies important gene ontology terms and pathways more consistently and performs well even in gene expression data sets with minimal or moderate gene expression changes. Moreover, gene set analysis is useful for comparing multiple gene expression data sets dealing with similar biological questions. This review briefly summarizes the rationale behind the gene set analysis and introduces several algorithms and tools now available for gene set analysis.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.27-30
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• 1996

PR interval variability has been proposed as a noninvasive tool for in-vestigating the autonomic nervous system as welt as heart rate variability. The goal of this paper is to determine whether PR interval variability is generated from deterministic nonlinear dynamics. The data used in this study is a 24-hour bolter ECGs of 20 healthy adults. We developed an automatic PR interval measurement algorithm, and tested it using MIT ECG Databases. The general discriminants of nonlinear dynamics, such as, correlation dimension and phase space reconstruction are used. Surrogate data is generated from simpler linear models to have similar statistical characteristics with the original data. Nonlinear discriminants are applied to both data, and compared for any significant results. It was concluded that PR interval variability shows non-linear characteristics.

• Molecules and Cells
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• v.24 no.3
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• pp.307-315
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• 2007

Gene regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Inferring transcriptional interactions between TFs and their target genes has utmost importance for understanding the complex regulatory mechanisms in cellular systems. On one hand, with the rapid progress of various high-throughput experiment techniques, more and more biological data become available, which makes it possible to quantitatively study gene regulation in a systematic manner. On the other hand, transcription regulation is a complex biological process mediated by many events such as post-translational modifications, degradation, and competitive binding of multiple TFs. In this review, with a particular emphasis on computational methods, we report the recent advances of the research topics related to transcriptional regulatory networks, including how to infer transcriptional interactions, reveal combinatorial regulation mechanisms, and reconstruct TF activity profiles.