Helical magnetic micromotors decorated with nickel ferrocyanide for the active and rapid adsorption of radiocesium in water

Separating radioactive cesium from nuclear waste and contaminated environments is critical to mitigate radiological hazards. In response to this need, remote-controllable and Cs-selective micromotor adsorbents have been considered as a promising technology for rapid in-situ cleanup while minimizing secondary waste and radiation exposure to workers. In this study, we demonstrate the active and rapid removal of a radioactive contaminant from water by leveraging the magnetic manipulation capabilities of a helical and magnetic Ni micromotor coated with Cs-selective nickel ferrocyanide (NiFC). The use of polyvinyl alcohol fibers as a template enables the straightforward preparation of the helical wire structure, allowing for precise control over the diameter and pitch of the helix through simple twisting with Ni wires. By harnessing Ni2+ ions eluted from the Ni micromotor in an acid solution, we successfully fabricate NiFC-coated Ni (NiFC/Ni) micromotors that exhibit a selective removal efficiency greater than 98% for 137Cs, even in the presence of high concentrations of competing Na+ ions. Under the influence of an external magnetic field, the NiFC/Ni micromotor demonstrates rapid motion, achieving a pulling motion (100 body lengths per second) through a magnetic gradient and a tumbling motion (46 body lengths per second) induced by a rotating magnetic field. The tumbling motion of the NiFC/Ni micromotor substantially improves the Cs adsorption rate, resulting in a rate that surpasses that achieved under nonmoving conditions by a factor of 21. This improved adsorption rate highlights the considerable potential of magnetically manipulated micromotor self-propulsion for efficient water-pollution treatment.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Abstract 5035: Q-omics: smart software for assisting oncology and cancer research

Increased multi-level omics data has enabled data-driven studies on cancer drugs, targets and biomarkers. Thus, it is necessary to develop comprehensive tools for oncologists and cancer scientists to carry out extensive data mining without computational expertise. For this purpose, we have developed innovative software that enables user-driven analyses on cancer omics data, assisted by knowledge-based smart systems.

Publicly available multi-level omics data of mutations, gene/protein expression, patient survival, immune score (tumor infiltrating cells), drug screening and RNAi (shRNA and CRISPR) screenings on patient samples and cell lines, were integrated from the TCGA, GDSC, CCLE, NCI and DepMap databases. Q-omics provides user-friendly interface for calculation and visualization of cross-associated data pairs retrieved from integrated datasets. The optimal selection of samples, datasets and/or other filtering options is guided by knowledge-bases of the software for the quick and easy finding of significant associations between data pairs. Furthermore, implemented smart algorithms prioritize significant hits based on consensus scoring methods. Consensus scoring using multiple statistical tests with varied sample (or lineage) selection, enriches noise-free, robust cross-associated pairs in the hit list.

We believe that Q-omics provide simple but powerful tools for all areas of oncology and cancer research. The latest version of Q-omics software is available at http://qomics.sookmyung.ac.kr.

Citation Format: Sukjoon Yoon, Euna Jeong, Sumin Jeong, Jieun Lee, Youngju Kim. Q-omics: smart software for assisting oncology and cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5035.

Q-omics: Smart Software for Assisting Oncology and Cancer Research

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

A later menopausal age is associated with a lower prevalence of physical frailty in community-dwelling older adults: The Korean Frailty and Aging Cohort Study (KFACS)

OBJECTIVES

The aim of this study was to determine whether age at menopause is associated with physical frailty.

METHODS

This was a cross-sectional study that included 1264 women (70-84 years) from the Korean and Aging Cohort Study (KFACS) who had records of their ages at menarche and their ages at menopause and had experienced a natural menopause. We used Fried criteria to assess physical frailty status. The ages at menopause and menarche were collected using self-reported questionnaires.

RESULTS

The prevalence of physical frailty decreased by 5.3 % with each year of increase in age at menopause after adjusting for age, marital status, years of education, diabetes mellitus, hypertension, polypharmacy, hospitalizations, falls, and hormone replacement therapy (p = 0.005). The prevalence of frailty significantly decreased by 4.1 % when the reproductive span increased by a year (p = 0.019).

CONCLUSIONS

This study found that a later menopausal age was associated with a lower risk of frailty using Fried criteria. In addition, it showed that a longer reproductive span was associated with a lower prevalence of frailty.

Abstract 4855: Development of highly selective CDK7 inhibitor Q901 for solid tumors

Cyclin-dependent kinase 7 (CDK7) a member of the cyclin-dependent kinase family, forms a functional CDK-activating kinase (CAK) with cyclin H and MAT1, which regulates the cell cycle by phosphorylating other CDKs. CDK7 is also a component of transcription factor II H (TFIIH) that regulates transcriptional initiation and elongation through the phosphorylation of the RNA polymerase II C-terminal domain (CTD). Inhibition of CDK7 has been reported as a potential cancer therapeutic by addressing aberrant cell cycle regulation and transcription, as these abnormalities are considered main factors for tumor progression in specific types of cancer. Q901 is a novel highly selective CDK7 inhibitor in IND enabling development stage. Q901 shows extreme selectivity by singular inhibition of CDK7 among tested kinome panel. Q901 triggers G1 cell cycle arrest in specific cancer models in vitro and in vivo. Good correlation has been demonstrated between Q901 tumor growth inhibition and target engagement of a pharmacodynamics marker in treated models, suggesting CDK7 is a main driver of tumor progression in the selected models. The Q901 data supports the continued preclinical evaluation and potential of the compound as investigational treatment for patients with multiple solid tumors.

Citation Format: Donghoon Yu, Yeejin Jeon, Dongsik Park, Mooyoung Seo, Wongyun Ahn, Jaeseung Kim, Choa Park, Euna Jeong, Sukjoon Yoon, Kiyean Nam. Development of highly selective CDK7 inhibitor Q901 for solid tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4855.

Overexpression and Selective Anticancer Efficacy of ENO3 in STK11 Mutant Lung Cancers

Oncogenic gain-of-function mutations are clinical biomarkers for most targeted therapies, as well as represent direct targets for drug treatment. Although loss-of-function mutations involving the tumor suppressor gene, STK11 (LKB1) are important in lung cancer progression, STK11 is not the direct target for anticancer agents. We attempted to identify cancer transcriptome signatures associated with STK11 loss-offunction mutations. Several new sensitive and specific gene expression markers (ENO3, TTC39C, LGALS3, and MAML2) were identified using two orthogonal measures, i.e., fold change and odds ratio analyses of transcriptome data from cell lines and tissue samples. Among the markers identified, the ENO3 gene over-expression was found to be the direct consequence of STK11 loss-of-function. Furthermore, the knockdown of ENO3 expression exhibited selective anticancer effect in STK11 mutant cells compared with STK11 wild type (or recovered) cells. These findings suggest that ENO3 -based targeted therapy might be promising for patients with lung cancer harboring STK11 mutations.

β-catenin/TCF activity regulates IGF-1R tyrosine kinase inhibitor sensitivity in colon cancer

The availability of large-scale drug screening data on cell line panels provides a unique opportunity to identify predictive biomarkers for targeted drug efficacy. Analysis of diverse drug data on ~990 cancer cell lines revealed enhanced sensitivity of insulin-like growth factor 1 receptor/ Insulin Receptor (IGF-1R/IR) tyrosine kinase inhibitors (TKIs) in colon cancer cells. Interestingly, β-catenin/TCF(T cell factor)-responsive promoter activity exhibited a significant positive association with IGF-1R/IR TKI response, while the mutational status of direct upstream genes, such as CTNNB1 and APC, was not significantly associated with the response. The β-catenin/TCF activity high cell lines express components of IGF-1R/IR signaling more than the low cell lines explaining their enhanced sensitivity against IGF-1R/IR TKI. Reinforcing β-catenin/TCF responsive promoter activity by introducing CTNNB1 gain-of-function mutations into IGF-1R/IR TKI-resistant cells increased the expression and activity of IGF-1R/IR signaling components and also sensitized the cells to IGF-1R/IR TKIs in vitro and in vivo. Analysis of TCGA data revealed that the stronger β-catenin/TCF responsive promoter activity was associated with higher IGF-1R and IGF2 transcription in human colon cancer specimens as well. Collectively, compared to the mutational status of upstream genes, β-catenin/TCF responsive promoter activity has potential to be a stronger predictive positive biomarker for IGF-1R/IR TKI responses in colon cancer cells. The present study highlights the potential of transcriptional activity as therapeutic biomarkers for targeted therapies, overcoming the limited ability of upstream genetic mutations to predict responses.

QSurface: fast identification of surface expression markers in cancers

Background

Cell surface proteins have provided useful targets and biomarkers for advanced cancer therapies. The recent clinical success of antibody-drug conjugates (ADCs) highlights the importance of finding selective surface antigens for given cancer subtypes. We thus attempted to develop stand-alone software for the analysis of the cell surface transcriptome of patient cancer samples and to prioritize lineage- and/or mutation-specific over-expression markers in cancer cells.

Results

A total of 519 genes were selected as surface proteins, and their expression was profiled in 14 cancer subtypes using patient sample transcriptome data. Lineage/mutation-oriented analysis was used to identify subtype-specific surface markers with statistical confidence. Experimental validation confirmed the unique over-expression of predicted surface markers (MUC4, MSLN, and SLC7A11) in lung cancer cells at the protein level. The differential cell surface gene expression of cell lines may differ from that of tissue samples due to the absence of the tumor microenvironment.

Conclusions

In the present study, advanced 3D models of lung cell lines successfully reproduced the predicted patterns, demonstrating the physiological relevance of cell line-based 3D models in validating surface markers from patient tumor data. Also QSurface software is freely available at http://compbio.sookmyung.ac.kr/~qsurface.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0541-6) contains supplementary material, which is available to authorized users.

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium. 15-18 March 2016

[This corrects the article DOI: 10.1186/s13054-016-1208-6.].

Loss-of-function screens of druggable targetome against cancer stem-like cells

Cancer stem–like cells (CSLCs) contribute to the initiation and recurrence of tumors and to their resistance to conventional therapies. In this study, small interfering RNA (siRNA)-based screening of ∼4800 druggable genes in 3-dimensional CSLC cultures in comparison to 2-dimensional bulk cultures of U87 glioma cells revealed 3 groups of genes essential for the following: survival of the CSLC population only, bulk-cultured population only, or both populations. While diverse biologic processes were associated with siRNAs reducing the bulk-cultured population, CSLC-eliminating siRNAs were enriched in a few functional categories, such as lipid metabolism, protein metabolism, and gene expression. Interestingly, siRNAs that selectively reduced CSLC only were found to target genes for cholesterol and unsaturated fatty acid synthesis. The lipidomic profile of CSLCs revealed increased levels of monounsaturated lipids. Pharmacologic blockage of these target pathways reduced CSLCs, and this effect was eliminated by addition of downstream metabolite products. The present CSLC-sensitive target categories provide a useful resource that can be exploited for the selective elimination of CSLCs.—Song, M., Lee, H., Nam, M.-H., Jeong, E., Kim, S., Hong, Y., Kim, N., Yim, H. Y., Yoo, Y.-J., Kim, J. S., Kim, J.-S., Cho, Y.-Y., Mills, G. B., Kim, W.-Y., Yoon, S. Loss-of-function screens of druggable targetome against cancer stem–like cells.

Glucose starvation induces mutation and lineage-dependent adaptive responses in a large collection of cancer cell lines

Tolerance of glucose deprivation is an important factor for cancer proliferation, survival, migration and progression. To systematically understand adaptive responses under glucose starvation in cancers, we analyzed reverse phase protein array (RPPA) data of 115 protein antibodies across a panel of approximately 170 heterogeneous cancer cell lines, cultured under normal and low glucose conditions. In general, glucose starvation broadly altered levels of many of the proteins and phosphoproteins assessed across the cell lines. Many mTOR pathway components were selectively sensitive to glucose stress, although the change in their levels still varied greatly across the cell line set. Furthermore, lineage- and genotype-based classification of cancer cell lines revealed mutation-specific variation of protein expression and phosphorylation in response to glucose starvation. Decreased AKT phosphorylation (S473) was significantly associated with PTEN mutation under glucose starvation conditions in lung cancer cell lines. The present study (see TCPAportal.org for data resource) provides insight into adaptive responses to glucose deprivation under diverse cellular contexts.

Analysis of image-based phenotypic parameters for high throughput gene perturbation assays

Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays.

MACE: mutation-oriented profiling of chemical response and gene expression in cancers

SUMMARY

The mutational status of specific cancer lineages can affect the sensitivity to or resistance against cancer drugs. The MACE database provides web-based interactive tools for interpreting large chemical screening and gene expression datasets of cancer cell lines in terms of mutation and lineage categories. GI50 data of chemicals against individual NCI60 cell lines were normalized and organized to statistically identify mutation- or lineage-specific chemical responses. Similarly, DNA microarray data on NCI60 cell lines were processed to analyze mutation- or lineage-specific gene expression signatures. A combined analysis of GI50 and gene expression data to find potential associations between chemicals and genes is also a capability of this system. This database will provide extensive, systematic information to identify lineage- or mutation-specific anticancer agents and related gene targets.

AVAILABILITY AND IMPLEMENTATION

The MACE web database is available at http://mace.sookmyung.ac.kr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

yoonsj@sookmyung.ac.kr.

Dissecting the global variation of gene expression for the functional interpretation of transcriptome data

To perform their biological functions, individual genes exhibit varying ranges of expression levels. Thus, considering the intrinsic variability of gene expression can improve geneset-based functional analyses which are typically used to interpret transcriptome data. Through the extensive quantitative analysis of the expressional variability of individual genes using large collections of transcriptome and proteome data, we found the existence of the intrinsic variability of gene expression at the transcriptional level. Interestingly, genes under post-translational regulation were not sensitively regulated at the transcriptional level. Because genes have intrinsically different levels of regulation at the transcription and translation stages, the functional geneset-based interpretation of transcriptome data should only include genes that are significantly varied at the transcriptional level. Thus, by removing genes with low transcriptional variation from the DNA microarray data, we showed that geneset enrichment analysis could provide improved resolution in prioritizing target functional pathways in several different experimental datasets.

Effects of silver nanoparticles on biological nitrogen removal processes

The effects of silver (Ag) nanoparticles (NPs) on activated sludge in a biological nitrogen removal (BNR) process were investigated under aerobic and anoxic conditions. We show that nitrification was more vulnerable to Ag NPs exposure than denitrification at the same Ag NPs concentration. In continuous operation of the BNR process, a higher inhibitory effect on nitrification was attributed to a smaller size of Ag NPs. About 70-90% of the Ag NPs supplied were embedded in the sludge matrix but 10-30% of the Ag NPs remained in the supernatant. This indicates that significant amounts of Ag NPs could be discharged from wastewater treatment plants and potentially impact on aquatic ecosystems.

CSO validator: improving manual curation workflow for biological pathways

SUMMARY

Manual curation and validation of large-scale biological pathways are required to obtain high-quality pathway databases. In a typical curation process, model validation and model update based on appropriate feedback are repeated and requires considerable cooperation of scientists. We have developed a CSO (Cell System Ontology) validator to reduce the repetition and time during the curation process. This tool assists in quickly obtaining agreement among curators and domain experts and in providing a consistent and accurate pathway database.

AVAILABILITY

The tool is available on http://csovalidator.csml.org.

CONTACT

masao@hgc.jp.

Systems biology model repository for macrophage pathway simulation

SUMMARY

The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis.

AVAILABILITY

MACPAK can be accessed via an interactive web site at http://macpak.csml.org. The CSML pathway models are available under the Creative Commons license.

Ontology-based instance data validation for high-quality curated biological pathways

BACKGROUND

Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.

RESULTS

We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models.

CONCLUSIONS

A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

Cell illustrator 4.0: a computational platform for systems biology

Cell Illustrator is a software platform for Systems Biology that uses the concept of Petri net for modeling and simulating biopathways. It is intended for biological scientists working at bench. The latest version of Cell Illustrator 4.0 uses Java Web Start technology and is enhanced with new capabilities, including: automatic graph grid layout algorithms using ontology information; tools using Cell System Markup Language (CSML) 3.0 and Cell System Ontology 3.0; parameter search module; high-performance simulation module; CSML database management system; conversion from CSML model to programming languages (FORTRAN, C, C++, Java, Python and Perl); import from SBML, CellML, and BioPAX; and, export to SVG and HTML. Cell Illustrator employs an extension of hybrid Petri net in an object-oriented style so that biopathway models can include objects such as DNA sequence, molecular density, 3D localization information, transcription with frame-shift, translation with codon table, as well as biochemical reactions.

Cell Illustrator 4.0: a computational platform for systems biology

Cell Illustrator is a software platform for Systems Biology that uses the concept of Petri net for modeling and simulating biopathways. It is intended for biological scientists working at bench. The latest version of Cell Illustrator 4.0 uses Java Web Start technology and is enhanced with new capabilities, including: automatic graph grid layout algorithms using ontology information; tools using Cell System Markup Language (CSML) 3.0 and Cell System Ontology 3.0; parameter search module; high-performance simulation module; CSML database management system; conversion from CSML model to programming languages (FORTRAN, C, C++, Java, Python and Perl); import from SBML, CellML, and BioPAX; and, export to SVG and HTML. Cell Illustrator employs an extension of hybrid Petri net in an object-oriented style so that biopathway models can include objects such as DNA sequence, molecular density, 3D localization information, transcription with frame-shift, translation with codon table, as well as biochemical reactions.

Pulmonary tuberculosis with acute respiratory failure

The aim of the present study was to evaluate the clinical characteristics, prognoses and predictors of mortality of patients with pulmonary tuberculosis (TB) with acute respiratory failure (ARF), and to investigate the adjunctive use of corticosteroids in such cases. TB patients with ARF requiring mechanical ventilation (n = 90) were enrolled retrospectively during 1989-2006. The patients were divided into two groups: tuberculous pneumonia (TBP; n = 66), and miliary TB (MTB; n = 24). The TBP patients were older than the MTB patients (mean age 68.0 versus 54.5 yrs), and the mean+/-SD interval from hospital admission to start of anti-TB treatment was longer for the TBP than for the MTB group (5.0+/-7.0 versus 2.8+/-2.5 days). However, there was no difference in in-hospital mortality rate between the two groups (68.2 versus 58.3%). In the TBP patients, multivariate analysis showed that advanced age and shock unrelated to sepsis were associated with poor outcomes. Even though corticosteroid use was a predictor of survival in TBP patients, it was difficult to conclusively determine the efficacy of corticosteroids in TBP with ARF because of the retrospective study design. The present study reveals the need for randomised controlled trials to clarify the role of corticosteroids as adjunctive therapy in the management of tuberculous pneumonia with acute respiratory failure.

Systematic reconstruction of TRANSPATH data into cell system markup language

BACKGROUND

Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level.

RESULTS

We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models.

CONCLUSION

By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.

Rule-based reasoning for system dynamics in cell systems

A system-dynamics-centered ontology, called the Cell System Ontology (CSO), has been developed for representation of diverse biological pathways. Many of the pathway data based on the ontology have been created from databases via data conversion or curated by expert biologists. It is essential to validate the pathway data which may cause unexpected issues such as semantic inconsistency and incompleteness. This paper discusses three criteria for validating the pathway data based on CSO as follows: (1) structurally correct models in terms of Petri nets, (2) biologically correct models to capture biological meaning, and (3) systematically correct models to reflect biological behaviors. Simultaneously, we have investigated how logic-based rules can be used for the ontology to extend its expressiveness and to complement the ontology by reasoning, which aims at qualifying pathway knowledge. Finally, we show how the proposed approach helps exploring dynamic modeling and simulation tasks without prior knowledge.

Cell system ontology: representation for modeling, visualizing, and simulating biological pathways

With the rapidly accumulating knowledge of biological entities and networks, there is a growing need for a general framework to understand this information at a system level. In order to understand life as system, a formal description of system dynamics with semantic validation will be necessary. Within the context of biological pathways, several formats have been proposed, e.g., SBML, CellML, and BioPAX. Unfortunately, these formats lack the formal definitions of each term or fail to capture the system dynamics behavior. Thus, we have developed a new system dynamics centered ontology called Cell System Ontology (CSO). As an exchange format, the ontology is implemented in the Web Ontology Language (OWL), which enables semantic validation and automatic reasoning to check the consistency of biological pathway models. The features of CSO are as follows: (1) manipulation of different levels of granularity and abstraction of pathways, e.g., metabolic pathways, regulatory pathways, signal transduction pathways, and cell-cell interactions; (2) capture of both quantitative and qualitative aspects of biological function by using hybrid functional Petri net with extension (HFPNe); and (3) encoding of biological pathway data related to visualization and simulation, as well as modeling. The new ontology also predefines mature core vocabulary, which will be necessary for creating models with system dynamics. In addition, each of the core terms has at least one standard icon for easy modeling and accelerating the exchangeability among applications. In order to demonstrate the potential of CSO-based pathway modeling, visualization, and simulation, we present an HFPNe model for the ASEL and ASER regulatory networks in Caenorhabditis elegans.

Conversion from BioPAX to CSO for system dynamics and visualization of biological pathway

The vast accumulation of biological pathway data scattered in various sources presents challenges in the exchange and integration of these data. Major new standards for representation of pathway data and the ability to check inconsistency in pathways are inevitable for the development of a reliable pathway data repository. Within the context of biological pathways, the cell system ontology (CSO) had been developed as a general framework to model system dynamics and visualization of diverse biological pathways. CSO provides an excellent environment for modeling, visualizing, and simulating complex molecular mechanisms at different levels of details. This paper examines whether CSO addresses the integration capability of pathway data with system dynamics. We present a conversion tool for converting BioPAX to CSO. Transforming the data from BioPAX to CSO not only allows an analysis of the dynamic behaviors in molecular interactions but also allows the results to be stored for further biological investigations, which is not possible in BioPAX. The conversion is done using simple inference algorithms with the addition of view- and simulation-related properties. We demonstrate how CSO can be used to build a complete and consistent pathway repository and enhance the interoperability among applications.

A neural network method for identification of RNA-interacting residues in protein

Identification of the most putative RNA-interacting residues in protein is an important and challenging problem in a field of molecular recognition. Structural analysis of protein-RNA complexes reveals a strong correlation between interaction residues and their structure. Building on this viewpoint, we have developed a neural network predictor to correctly identify residues involved in protein-RNA interactions from protein sequence and its structural information. The system has been exhaustedly cross-validated with various strategies differing in input encoding, amount of input information, and network architectures. In addition, we have evaluated performance among functional subsets of complexes. Finally, to reflect the properties of protein-RNA complexes in our dataset, two kinds of post-processing method are adopted. The experimental result shows that our system yields not-trivial performance although the residues in interaction sites are too scarce.

Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns

Structural analysis of protein-RNA complexes is labor-intensive, yet provides insight into the interaction patterns between a protein and RNA. As the number of protein-RNA complex structures reported has increased substantially in the last few years, a systematic method is required for automatically identifying interaction patterns. This paper presents a computational analysis of the hydrogen bonds in the most representative set of protein-RNA complexes. The analysis revealed several interesting interaction patterns. (1) While residues in the beta-sheets favored unpaired nucleotides, residues in the helices showed no preference and residues in turns favored paired nucleotides. (2) The backbone hydrogen bonds were more dominant than the base hydrogen bonds in the paired nucleotides, but the reverse was observed in the unpaired nucleotides. (3) The protein-RNA complexes contained more paired nucleotides than unpaired nucleotides, but the unpaired nucleotides were observed more frequently interacting with the proteins. And (4) Arg-U, Thr-A, Lys-A, and Asn-U were the most frequently observed pairs. The interaction patterns discovered from the analysis will provide us with useful information in predicting the structure of the RNA binding protein and the structure of the protein binding RNA.

Discovering the interaction propensities of amino acids and nucleotides from protein-RNA complexes

With the availability of many genome sequences, the mining of biological data is attracting much attention, most of it limited to the sequences of macromolecules. Sequence data are easy to analyze as they can be treated as strings of characters, whereas the structure of a macromolecule is much more complex. We developed a set of algorithms to analyze the structures of protein-RNA complexes at the atomic level and used them to analyze protein-RNA interactions using structural data on 51 protein-RNA complexes. The analysis revealed, among other things, that: (1) polar and charged amino acids have a strong tendency to interact with nucleotides, (2) arginine and asparagine tend to hydrogen bond with uracil, and (3) histidine favors uracil in water-mediated bonding with RNA. We analyzed a large set of structural data of protein-RNA complexes involving water-mediated hydrogen bonds as well as direct hydrogen bonds. The interaction patterns discovered from the analysis provide useful information for predicting the structure of RNA that binds proteins, and of proteins that bind RNA.

A Web-based interactive education programme on vaccination for public health officials

Health and Welfare Training Center of Korean National Institute of Health (KNIH) has developed various education curricula for officials who are involved in the public health or welfare sectors. Still almost all education programmes are off-line based. In off-line settings, both the lecturers and the students should come to NIH from their counties to join the education programmes, and it is impossible for the students to review or to re-practice the education contents. We developed a prototype on-line setting education programmes to provide a more convenient and more effective service to the officials. This web-based interactive education programme consisted of the specific objectives on vaccination for the public health officials who were working for the national immunization programmes. It became a prototype of web-based education and was evaluated by the participants in April, 2001. From the evaluation, we improved the contents and tried to develop more subjects for the officials' training.

Phase 1 drug interaction study of suramin and warfarin in patients with prostate cancer

Potential interaction between suramin and warfarin was evaluated when coadministered to patients with cancer. Thirteen men with advanced hormone-refractory prostate cancer were initially stabilized with warfarin to a prothrombin time (PT) of 2 +/- 0.2 International Normalized Ratio (INR) during a lead-in period of 4 weeks. A baseline daily warfarin dose was established, and treatment with suramin plus hydrocortisone was then started. The effect of suramin on the anticoagulant activity of warfarin was assessed in each patient by comparing his baseline warfarin dose with average daily doses required to maintain the same INR level over each of the initial 6 weeks of a 12-week course of suramin treatment. The average daily dose of warfarin required to maintain PT at 2 +/- 0.2 INR decreased from a baseline value of 4.2 to between 3.4 and 4.0 during the 6 weeks of suramin plus warfarin treatment. Despite failing to demonstrate equivalence applying a 90% confidence interval approach, required reductions in warfarin dose were clinically minor and the combination was well tolerated. Based on these results, the eligibility criteria for a large ongoing randomized study were amended to allow entry of men receiving warfarin therapy. This interaction study, together with experience gained in a larger trial setting, has confirmed that warfarin and suramin can be safely coadministered, provided that coagulation status is appropriately monitored.

A solenoid-like coil producing transverse RF fields for MR imaging

A solenoidal RF coil is presented that generates an RF field only perpendicular to the long axis, so that it can be used longitudinally in the usual superconducting solenoid magnet for NMR. This new design is superior to the traditional saddle coil or Helmholtz pair. Applications for the coil will include imaging of legs, arms, etc., as well as analytical application in high-resolution NMR. A tilted single-turn-solenoid, which has the plane of each loop tilted with respect to the cylinder axis, is implemented to generate an RF field with a component perpendicular to the coil-cylinder axis. The novel addition of an eddy-current coil eliminates the undesirable longitudinal component of the RF field, leaving an RF field entirely perpendicular to the coil-cylinder axis. By inserting the eddy coil, the spatial homogeneity of the transverse RF field is greatly improved, with significant increases in the H1 field strength and receiving sensitivity. Images with the eddy-coil-modified tilted solenoid are remarkably uniform in intensity over a large useful volume.

Stability of paclitaxel with ondansetron hydrochloride or ranitidine hydrochloride during simulated Y-site administration

The stability of paclitaxel with either ondansetron hydrochloride or ranitidine hydrochloride during simulated Y-site injection at room temperature was studied. Triplicate test solutions of paclitaxel 0.3 and 1.2 mg/mL were admixed 1:1 with ondansetron 0.03 and 0.3 mg/mL (as the hydrochloride salt) or ranitidine 0.5 and 2.0 mg/mL (as the hydrochloride salt). Also, paclitaxel 1.2 mg/mL was admixed 1:1:1 with ondansetron 0.3 mg/mL and ranitidine 2.0 mg/mL. The solutions were stored in glass containers at room temperature, and samples were removed at zero, one, two, and four hours for immediate assay. At the time of the assay and before any dilution, each sample was visually inspected for clarity, color, and precipitation, and the pH was determined. Drug concentrations were measured by stability-indicating high-performance liquid chromatographic procedures. Throughout the study, more than 90% of the initial concentrations of paclitaxel, ondansetron, and ranitidine remained in the solutions. No precipitates, color changes, or haziness was seen. The changes in pH were minor. Paclitaxel in concentrations of 0.3 and 1.2 mg/mL was stable when mixed with either ondansetron (0.03 or 0.3 mg/mL, as the hydrochloride salt) or ranitidine (0.5 or 2.0 mg/mL, as the hydrochloride salt) and stored in glass containers for four hours. Paclitaxel 1.2 mg/mL was also stable when mixed with both ondansetron 0.3 mg/mL and ranitidine 2.0 mg/mL and stored in glass containers for four hours.