Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033129
Canjun Wang, M. Yi, Keli Yang
The roles of time delay on gene switch and stochastic resonance are systematically explored based on a famous gene transcriptional regulatory model with noises. Our theoretical results show that the time delay can induce the switch, i.e., the TF-A monomer concentration shifts from the high concentration state to the low concentration state (“on”→“off”), and can further accelerate the transition from “on” to “off”. Moreover, it is found that the stochastic resonance can be enhanced by the time delay and the correlated noise intensity. However, the additive noise original from the synthesis rate restrains the stochastic resonance. It is very interesting that the resonance bi-peaks structure appears for the large value of the additive noise intensity. The theoretical results by using small-delay time-approximation approach are consistent well with our numerical simulation.
{"title":"Time delay-accelerated transition of gene switch and -enhanced stochastic resonance in a bistable gene regulatory model","authors":"Canjun Wang, M. Yi, Keli Yang","doi":"10.1109/ISB.2011.6033129","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033129","url":null,"abstract":"The roles of time delay on gene switch and stochastic resonance are systematically explored based on a famous gene transcriptional regulatory model with noises. Our theoretical results show that the time delay can induce the switch, i.e., the TF-A monomer concentration shifts from the high concentration state to the low concentration state (“on”→“off”), and can further accelerate the transition from “on” to “off”. Moreover, it is found that the stochastic resonance can be enhanced by the time delay and the correlated noise intensity. However, the additive noise original from the synthesis rate restrains the stochastic resonance. It is very interesting that the resonance bi-peaks structure appears for the large value of the additive noise intensity. The theoretical results by using small-delay time-approximation approach are consistent well with our numerical simulation.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"87 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127996009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033149
Tatsuya Sekiguchi, M. Okamoto
Previously, we developed a biochemical reaction simulator called WinBEST-KIT (Biochemical Engineering System analyzing Tool-KIT, which runs under Microsoft Windows) for analyzing complicated metabolic pathways. WinBEST-KIT provides an integrated simulation environment for experimental researchers in metabolic engineering. A particularly notable feature of WinBEST-KIT is that users can easily define and customize reaction symbols in the graphical user interface. Users can use their original kinetic equations, in addition to the pre-installed standard kinetic equations, to represent unknown kinetic mechanisms as reaction steps. However, owing to the increasing size of reaction systems to be analyzed in metabolic pathways, large-scale reaction systems must be divided into several arbitrary compartmental reaction systems and procedures are needed, such as multilayered hierarchical representation, to describe the interactions between the compartmental reaction systems. Accordingly, in this study, we developed a new version of WinBEST-KIT that enables users to construct several arbitrary reaction schemes as layers, to connect the layers, and to analyze the interactions between them. This hierarchical representation is effective for constructing multilayered mathematical models of biochemical systems, such as genome-enzyme-metabolite systems, reaction cascade systems, and multicellular systems.
{"title":"WinBEST-KIT for analyzing multilayer and multicellular systems","authors":"Tatsuya Sekiguchi, M. Okamoto","doi":"10.1109/ISB.2011.6033149","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033149","url":null,"abstract":"Previously, we developed a biochemical reaction simulator called WinBEST-KIT (Biochemical Engineering System analyzing Tool-KIT, which runs under Microsoft Windows) for analyzing complicated metabolic pathways. WinBEST-KIT provides an integrated simulation environment for experimental researchers in metabolic engineering. A particularly notable feature of WinBEST-KIT is that users can easily define and customize reaction symbols in the graphical user interface. Users can use their original kinetic equations, in addition to the pre-installed standard kinetic equations, to represent unknown kinetic mechanisms as reaction steps. However, owing to the increasing size of reaction systems to be analyzed in metabolic pathways, large-scale reaction systems must be divided into several arbitrary compartmental reaction systems and procedures are needed, such as multilayered hierarchical representation, to describe the interactions between the compartmental reaction systems. Accordingly, in this study, we developed a new version of WinBEST-KIT that enables users to construct several arbitrary reaction schemes as layers, to connect the layers, and to analyze the interactions between them. This hierarchical representation is effective for constructing multilayered mathematical models of biochemical systems, such as genome-enzyme-metabolite systems, reaction cascade systems, and multicellular systems.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128069893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033168
Lida Zhu, Fengji Liang, Juan Liu, S. Rayner, Yinghui Li, Shanguang Chen, J. Xiong
Background: Much effort has been expended in exploring the connections between transcriptome, disease and drug, based on the premise that drug induced perturbations in the transcriptome will affect the phenotype and finally help to cure a disease. MicroRNAs (miRNAs) play a key role in the regulation of the transcriptome and have been identified as a key mediator in human disease and drug response. However, even if miRNA expression can be precisely detected, the information regarding miRNAs action on a particular part of the transcriptome is still lacking. Here, we introduced a novel concept, the Context-specific MiRNA activity (CoMi activity), to reflect a miRNA's regulation effect on a context specific gene set, by calculating the statistical difference between the distributions of its target gene expression and non-target gene expression. In this study we investigate whether CoMi activity could provide a novel perspective on miRNA mechanisms of action in disease and drug response, and facilitate in silico drug screening.
{"title":"Dynamic remodeling of context-specific miRNAs regulation networks facilitate in silico cancer drug screening","authors":"Lida Zhu, Fengji Liang, Juan Liu, S. Rayner, Yinghui Li, Shanguang Chen, J. Xiong","doi":"10.1109/ISB.2011.6033168","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033168","url":null,"abstract":"Background: Much effort has been expended in exploring the connections between transcriptome, disease and drug, based on the premise that drug induced perturbations in the transcriptome will affect the phenotype and finally help to cure a disease. MicroRNAs (miRNAs) play a key role in the regulation of the transcriptome and have been identified as a key mediator in human disease and drug response. However, even if miRNA expression can be precisely detected, the information regarding miRNAs action on a particular part of the transcriptome is still lacking. Here, we introduced a novel concept, the Context-specific MiRNA activity (CoMi activity), to reflect a miRNA's regulation effect on a context specific gene set, by calculating the statistical difference between the distributions of its target gene expression and non-target gene expression. In this study we investigate whether CoMi activity could provide a novel perspective on miRNA mechanisms of action in disease and drug response, and facilitate in silico drug screening.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033160
Ben-gong Zhang, Luonan Chen, K. Aihara
This paper studies the dynamics of the Hepatitis B virus (HBV) model with intermittent antiviral therapy. We first propose a mathematical model of HBV and then analyze its qualitative and dynamical properties with a new treatment therapy. Combining with the clinical data and theoretical analysis, we show that the intermittent antiviral therapy regimen is one of optimal strategies to treat this kind of complex disease. There are two mainly advantages on this therapy. Firstly, it can delay the drug resistance. Secondly, it can reduce the duration of treatment time comparing with the long term continuous therapy, thereby reducing the adverse side effect. Our results clear provides a new way to treat the HBV disease.
{"title":"Dynamics of HBV model with intermittent antiviral therapy","authors":"Ben-gong Zhang, Luonan Chen, K. Aihara","doi":"10.1109/ISB.2011.6033160","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033160","url":null,"abstract":"This paper studies the dynamics of the Hepatitis B virus (HBV) model with intermittent antiviral therapy. We first propose a mathematical model of HBV and then analyze its qualitative and dynamical properties with a new treatment therapy. Combining with the clinical data and theoretical analysis, we show that the intermittent antiviral therapy regimen is one of optimal strategies to treat this kind of complex disease. There are two mainly advantages on this therapy. Firstly, it can delay the drug resistance. Secondly, it can reduce the duration of treatment time comparing with the long term continuous therapy, thereby reducing the adverse side effect. Our results clear provides a new way to treat the HBV disease.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123017424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033112
Hong Ling, S. Samarasinghe, D. Kulasiri
Recent experiments have shown that cellular senescence, a mechanism employed by cells for thwarting cell proliferation, plays an important role in protecting cells against cancer; therefore, a deeper understanding of cellular senescence can lead to effective cancer treatment. Inhibition of CDK2 is thought to be the critical trigger for cellular senescence. In this study, we first implement a mathematical model of G1/S transition involving the DNA-damage pathway and show that cellular senescence can be achieved by lowering CDK2. The robustness of CDK2 in triggering cellular senescence is determined from the probability (β) of DNA-damaged cells passing G1/S checkpoint for normal CDK2 and CDK2-deficient situations based on different thresholds of the peak time of two important biomarkers, CycE and E2F. The comparison of the values of β under the normal CDK2 and lower CDK2 levels reveals that reducing CDK2 levels can decrease the percentage of damaged cells passing G1/S checkpoint; more importantly, 50% reduction of CDK2 achieves 65% reduction in the percentage of damaged cells passing the G1/S checkpoint. These results point out that the developed model can highlight the possibility of lowering the bar for cellular senescence by reducing CDK2 levels. The results of investigation of β for the different thresholds of the peak times of other biomarkers show that β is insensitive to these perturbations of the peak time indicating that CDK2 activity is robust in lowering the senescence bar for low and high levels of DNA-damage. Furthermore, a mathematical formulation of robustness indicates that the robustness of CDK2 -triggered senescence increases with decreasing levels of CDK2, and is slightly greater for low-level DNA damage condition.
{"title":"Robustness of CDK2 in triggering cellular senescence based on probability of DNA-damaged cells passing G1/S checkpoint","authors":"Hong Ling, S. Samarasinghe, D. Kulasiri","doi":"10.1109/ISB.2011.6033112","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033112","url":null,"abstract":"Recent experiments have shown that cellular senescence, a mechanism employed by cells for thwarting cell proliferation, plays an important role in protecting cells against cancer; therefore, a deeper understanding of cellular senescence can lead to effective cancer treatment. Inhibition of CDK2 is thought to be the critical trigger for cellular senescence. In this study, we first implement a mathematical model of G1/S transition involving the DNA-damage pathway and show that cellular senescence can be achieved by lowering CDK2. The robustness of CDK2 in triggering cellular senescence is determined from the probability (β) of DNA-damaged cells passing G1/S checkpoint for normal CDK2 and CDK2-deficient situations based on different thresholds of the peak time of two important biomarkers, CycE and E2F. The comparison of the values of β under the normal CDK2 and lower CDK2 levels reveals that reducing CDK2 levels can decrease the percentage of damaged cells passing G1/S checkpoint; more importantly, 50% reduction of CDK2 achieves 65% reduction in the percentage of damaged cells passing the G1/S checkpoint. These results point out that the developed model can highlight the possibility of lowering the bar for cellular senescence by reducing CDK2 levels. The results of investigation of β for the different thresholds of the peak times of other biomarkers show that β is insensitive to these perturbations of the peak time indicating that CDK2 activity is robust in lowering the senescence bar for low and high levels of DNA-damage. Furthermore, a mathematical formulation of robustness indicates that the robustness of CDK2 -triggered senescence increases with decreasing levels of CDK2, and is slightly greater for low-level DNA damage condition.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127743634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033147
Fabio Gori, Dimitrios Mavroedis, M. Jetten, E. Marchiori
Metagenomics studies microbial communities by analyzing their genomic content directly sequenced from the environment. To this aim metagenomic datasets, consisting of many short DNA or RNA fragments, are computationally analyzed using statistical and machine learning methods with the general purpose of binning or taxonomic annotation. Many of these methods act on features derived from the data through a genomic signature, where a typical genomic signature of a fragment is a vector whose entries specify the frequency with which oligonucleotides appear in that fragment. In this article we analyze experimentally the ability of existing genomic signatures to facilitate the discrimination between fragments belonging to different genomes. We also propose new genomic signatures that take into account that fragments can have been sequenced from both strands of a genome; this is achieved by exploiting the reverse complementarity of oligonucleotides. We conduct extensive experiments on in silico sampled genomic fragments in order to assess comparatively the effectiveness of existing genomic signatures and those proposed in this article. Results of the experiments indicate that the direct use of the reverse complementarity of tetranucleotides in the definition of a genome signatures allows to have performances comparable to the best existing signatures using less features. Therefore the proposed genomic signatures provide an alternative set of features for analyzing metagenomic data. Online Supplementary material is available at http://www.cs.ru.nl/∼gori/signature metagenomics/.
{"title":"Genomic signatures for metagenomic data analysis: Exploiting the reverse complementarity of tetranucleotides","authors":"Fabio Gori, Dimitrios Mavroedis, M. Jetten, E. Marchiori","doi":"10.1109/ISB.2011.6033147","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033147","url":null,"abstract":"Metagenomics studies microbial communities by analyzing their genomic content directly sequenced from the environment. To this aim metagenomic datasets, consisting of many short DNA or RNA fragments, are computationally analyzed using statistical and machine learning methods with the general purpose of binning or taxonomic annotation. Many of these methods act on features derived from the data through a genomic signature, where a typical genomic signature of a fragment is a vector whose entries specify the frequency with which oligonucleotides appear in that fragment. In this article we analyze experimentally the ability of existing genomic signatures to facilitate the discrimination between fragments belonging to different genomes. We also propose new genomic signatures that take into account that fragments can have been sequenced from both strands of a genome; this is achieved by exploiting the reverse complementarity of oligonucleotides. We conduct extensive experiments on in silico sampled genomic fragments in order to assess comparatively the effectiveness of existing genomic signatures and those proposed in this article. Results of the experiments indicate that the direct use of the reverse complementarity of tetranucleotides in the definition of a genome signatures allows to have performances comparable to the best existing signatures using less features. Therefore the proposed genomic signatures provide an alternative set of features for analyzing metagenomic data. Online Supplementary material is available at http://www.cs.ru.nl/∼gori/signature metagenomics/.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114912592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033124
Yong-Cui Wang, X. Ren, Chunhua Zhang, N. Deng, Xiang-Sun Zhang
The past decades witnessed extensive efforts to study the relationships among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. The composition vectors are usually constructed to encode proteins as real-value vectors, which is feeding to a machine learning framework. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vector. Thus formulation to estimate the noise may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which are efficient in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E.coli) and Saccharomyces cerevisiae (S.cerevisiae) randomly and artificial negative datasets, respectively, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that, the denoising formulation efficient in phylogenetic trees construction can not improve the PPIs prediction, that is, what is noise is dependent on the applications.
{"title":"Evaluating the denoising techniques in protein-protein interaction prediction","authors":"Yong-Cui Wang, X. Ren, Chunhua Zhang, N. Deng, Xiang-Sun Zhang","doi":"10.1109/ISB.2011.6033124","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033124","url":null,"abstract":"The past decades witnessed extensive efforts to study the relationships among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. The composition vectors are usually constructed to encode proteins as real-value vectors, which is feeding to a machine learning framework. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vector. Thus formulation to estimate the noise may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which are efficient in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E.coli) and Saccharomyces cerevisiae (S.cerevisiae) randomly and artificial negative datasets, respectively, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that, the denoising formulation efficient in phylogenetic trees construction can not improve the PPIs prediction, that is, what is noise is dependent on the applications.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124546449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033156
Yifei Tang, Jiajia Chen, Cheng Luo, A. Kaipia, Bairong Shen
MicroRNAs (miRNAs) are reported to play essential roles in cancer initiation and progression and microarray technologies are intensively applied to study the miRNA expression profile in cancer. It is very common that the set of differentially expressed miRNAs related to the same cancer identified from different laboratories varies widely. Meanwhile, how the altered miRNAs coordinately contribute to the cause of prostate cancer is still not clear. In this study, we collected and processed four human prostate cancer associated miRNA microarray expression datasets with newly developed cancer outlier detection methods to identify differentially expressed miRNAs (DE-miRNAs). The targets of these DE-miRNAs were then extracted from database or predicted by bioinformatics prediction and then mapped to functional databases for enrichment analysis and overlapping comparison. Newly developed outlier detection methods were found to be more appropriate than t-test in cancer research, and the consistency of independent prostate cancer expression profiles at pathway or gene-set level was shown higher than that at gene (i.e. miRNA here) level. Furthermore, we identified 41 Gene Ontology terms, 4 KEGG pathways and 77 GeneGO pathways which are associated with prostate cancer. Among the top 15 GeneGO pathways, 5 were reported previously and the rest could be putative ones. Our analyses showed that more appropriate outlier detection methods should be used to detect oncogenes or oncomiRNAs that are altered only in a subset of samples. We proved that expression signatures of independent microarray experiments are more consistent rather at pathway level than at miRNA / gene level. We also found that the utilization of similar meta-analysis methods between miRNA and mRNA profiling datasets result in the detection of the same pathways.
{"title":"MicroRNA expression analysis reveals significant biological pathways in human prostate cancer","authors":"Yifei Tang, Jiajia Chen, Cheng Luo, A. Kaipia, Bairong Shen","doi":"10.1109/ISB.2011.6033156","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033156","url":null,"abstract":"MicroRNAs (miRNAs) are reported to play essential roles in cancer initiation and progression and microarray technologies are intensively applied to study the miRNA expression profile in cancer. It is very common that the set of differentially expressed miRNAs related to the same cancer identified from different laboratories varies widely. Meanwhile, how the altered miRNAs coordinately contribute to the cause of prostate cancer is still not clear. In this study, we collected and processed four human prostate cancer associated miRNA microarray expression datasets with newly developed cancer outlier detection methods to identify differentially expressed miRNAs (DE-miRNAs). The targets of these DE-miRNAs were then extracted from database or predicted by bioinformatics prediction and then mapped to functional databases for enrichment analysis and overlapping comparison. Newly developed outlier detection methods were found to be more appropriate than t-test in cancer research, and the consistency of independent prostate cancer expression profiles at pathway or gene-set level was shown higher than that at gene (i.e. miRNA here) level. Furthermore, we identified 41 Gene Ontology terms, 4 KEGG pathways and 77 GeneGO pathways which are associated with prostate cancer. Among the top 15 GeneGO pathways, 5 were reported previously and the rest could be putative ones. Our analyses showed that more appropriate outlier detection methods should be used to detect oncogenes or oncomiRNAs that are altered only in a subset of samples. We proved that expression signatures of independent microarray experiments are more consistent rather at pathway level than at miRNA / gene level. We also found that the utilization of similar meta-analysis methods between miRNA and mRNA profiling datasets result in the detection of the same pathways.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128026504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033176
Ying-Tsang Lo, Tsan-Huang Shih, Han-Jia Lin, Tun-Wen Pai, M. Chang
Ankyrin repeat domain (ARD) proteins contain various numbers of internal repeat units. They are considered as one important factor to influence hypoxia response through hydroxylation interaction with Factor Inhibiting HIF (FIH) enzymes which can repress HIF under normoxia environment. In this study, we adopted sequence based method and applied conserved hydroxylation motif patterns for identifying ASN/ASP/HIS hydroxylation sites on ARDs. First, a set of known ARD proteins was collected, and all corresponding repeat units were manually constructed and verified by removing redundant units. All extracted segments served as fundamental seed units to retrieve all ARDs proteins from 5 different species. Those ARD candidates were automatically segmented and a conserved hydroxylation motif pattern was applied for identifying all hydroxylation sites. As a result, the retrieval performance for ARDs achieved a sensitivity of 82% and a specificity of 98% for human species based on a testing dataset of 1,244 protein sequences. For hydroxylation site prediction, a sensitivity of 72.2% and a positive prediction value of 62% were achieved based on a set of 18 experimentally verified hydroxylation residues.
{"title":"Cross-species identification of hydroxylation sites for ARD and FIH interaction","authors":"Ying-Tsang Lo, Tsan-Huang Shih, Han-Jia Lin, Tun-Wen Pai, M. Chang","doi":"10.1109/ISB.2011.6033176","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033176","url":null,"abstract":"Ankyrin repeat domain (ARD) proteins contain various numbers of internal repeat units. They are considered as one important factor to influence hypoxia response through hydroxylation interaction with Factor Inhibiting HIF (FIH) enzymes which can repress HIF under normoxia environment. In this study, we adopted sequence based method and applied conserved hydroxylation motif patterns for identifying ASN/ASP/HIS hydroxylation sites on ARDs. First, a set of known ARD proteins was collected, and all corresponding repeat units were manually constructed and verified by removing redundant units. All extracted segments served as fundamental seed units to retrieve all ARDs proteins from 5 different species. Those ARD candidates were automatically segmented and a conserved hydroxylation motif pattern was applied for identifying all hydroxylation sites. As a result, the retrieval performance for ARDs achieved a sensitivity of 82% and a specificity of 98% for human species based on a testing dataset of 1,244 protein sequences. For hydroxylation site prediction, a sensitivity of 72.2% and a positive prediction value of 62% were achieved based on a set of 18 experimentally verified hydroxylation residues.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125242357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033162
Shu-Qiang Wang, Han-Xiong Li
A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. In this work, a regulatory model based binding energy is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity and the activity level of transcription factor (TF) are incorporated into a general learning model. The sequence features of the promoter and the possible occupancy of nucleosomes are exploited to estimate the binding probability of regulators. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than some previous models can do.
{"title":"A quantitative framework of transcriptional dynamics by integrating multiple sources of knowledge","authors":"Shu-Qiang Wang, Han-Xiong Li","doi":"10.1109/ISB.2011.6033162","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033162","url":null,"abstract":"A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. In this work, a regulatory model based binding energy is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity and the activity level of transcription factor (TF) are incorporated into a general learning model. The sequence features of the promoter and the possible occupancy of nucleosomes are exploited to estimate the binding probability of regulators. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than some previous models can do.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"743 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116121193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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