Pub Date : 2011-10-03DOI: 10.1109/ISB.2011.6033151
Xiaoquan Su, Jian Xu, K. Ning
Metagenomics method directly sequences and analyzes genome information from microbial communities. There are usually more than hundreds of genomes from different microbial species in the same community, and the main computational tasks for metagenomics data analysis include taxonomical and functional component of these genomes in the microbial community. Metagenomic data analysis is both data- and computation- intensive, which requires extensive computational power. Most of the current metagenomic data analysis softwares were designed to be used on a single computer, which could not match with the fast increasing number of large metagenomic projects' computational requirements. Therefore, advanced computational methods and pipelines have to be developed to cope with such need for efficient analyses. In this paper, we proposed Parallel-META, a GPU- and multi-core-CPU-based open-source pipeline for metagenomic data analysis, which enabled the efficient and parallel analysis of multiple metagenomic datasets. In Parallel-META, the similarity-based database search was parallelized based on GPU computing and multi-core CPU computing optimization. Experiments have shown that Parallel-META has at least 15 times speed-up compared to traditional metagenomic data analysis method, with the same accuracy of the results (http://www.bioenergychina.org:8800/).
{"title":"Parallel-META: A high-performance computational pipeline for metagenomic data analysis","authors":"Xiaoquan Su, Jian Xu, K. Ning","doi":"10.1109/ISB.2011.6033151","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033151","url":null,"abstract":"Metagenomics method directly sequences and analyzes genome information from microbial communities. There are usually more than hundreds of genomes from different microbial species in the same community, and the main computational tasks for metagenomics data analysis include taxonomical and functional component of these genomes in the microbial community. Metagenomic data analysis is both data- and computation- intensive, which requires extensive computational power. Most of the current metagenomic data analysis softwares were designed to be used on a single computer, which could not match with the fast increasing number of large metagenomic projects' computational requirements. Therefore, advanced computational methods and pipelines have to be developed to cope with such need for efficient analyses. In this paper, we proposed Parallel-META, a GPU- and multi-core-CPU-based open-source pipeline for metagenomic data analysis, which enabled the efficient and parallel analysis of multiple metagenomic datasets. In Parallel-META, the similarity-based database search was parallelized based on GPU computing and multi-core CPU computing optimization. Experiments have shown that Parallel-META has at least 15 times speed-up compared to traditional metagenomic data analysis method, with the same accuracy of the results (http://www.bioenergychina.org:8800/).","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"30 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":"114655777","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.2011.6033178
Pei Wang, Jinhu Lu, Yuhuan Zhang, M. Ogorzałek
It is well known that gene regulatory circuits can be modeled by the deterministic or stochastic approach. In this paper, a three-component coupled positive and negative feedback genetic circuit is firstly modeled deterministically by Hill kinetics. Then, a corresponding stochastic model is also investigated by using Gellispie's stochastic simulation. Some typical dynamical behaviors of the genetic circuit are further discussed based on the bifurcation analysis of deterministic system, including monostability, bistability, excitability, and oscillation. This paper aims to further investigate the effect of intrinsic noise inherently in stochastic models on steady states transition. It includes: i) For the parameters in deterministically bistable region, intrinsic noise may induce bistable switch for the not too large system volume, which can be observed by the generation of a new stable steady state; ii) For the parameters in deterministically excitable region, intrinsic noise may induce periodic switch for the very large system volume, which can be observed by the stabilization of another unstable steady state and the switching between two stable states; iii) When time delays are introduced in these two models, similar phenomena can be observed. The above results will certainly increase the understanding of the inner relationships between different modeling for the genetic circuit. It sheds some light on the real- world engineering applications, such as the engineering design of synthetic circuits.
{"title":"Intrinsic noise induced state transition in coupled positive and negative feedback genetic circuit","authors":"Pei Wang, Jinhu Lu, Yuhuan Zhang, M. Ogorzałek","doi":"10.1109/ISB.2011.6033178","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033178","url":null,"abstract":"It is well known that gene regulatory circuits can be modeled by the deterministic or stochastic approach. In this paper, a three-component coupled positive and negative feedback genetic circuit is firstly modeled deterministically by Hill kinetics. Then, a corresponding stochastic model is also investigated by using Gellispie's stochastic simulation. Some typical dynamical behaviors of the genetic circuit are further discussed based on the bifurcation analysis of deterministic system, including monostability, bistability, excitability, and oscillation. This paper aims to further investigate the effect of intrinsic noise inherently in stochastic models on steady states transition. It includes: i) For the parameters in deterministically bistable region, intrinsic noise may induce bistable switch for the not too large system volume, which can be observed by the generation of a new stable steady state; ii) For the parameters in deterministically excitable region, intrinsic noise may induce periodic switch for the very large system volume, which can be observed by the stabilization of another unstable steady state and the switching between two stable states; iii) When time delays are introduced in these two models, similar phenomena can be observed. The above results will certainly increase the understanding of the inner relationships between different modeling for the genetic circuit. It sheds some light on the real- world engineering applications, such as the engineering design of synthetic circuits.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"49 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":"127877357","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}
High-throughput technologies have produced a large number of protein-protein interactions (PPIs) for different species. As protein domains are functional and structural units of proteins, many computational efforts have been made to identify domain-domain interactions (DDIs) from PPIs. Parsimony assumption is widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they could detect specific DDIs, which is often hard for many probabilistic methods. In this paper, we revisit the parsimony model by presenting two important extensions. First, ‘complex networks’ as an emerging concept is incorporated as prior knowledge into the parsimony model. With this improvement, the prediction accuracy increases, which to some extent enhances the biological meaning of the common property of complex networks. Second, two randomization tests are designed to show the parsimony nature of the DDIs in mediating PPIs, which corroborates the model validation.
{"title":"Inferring domain-domain interactions using an extended parsimony model","authors":"Cheng Chen, Junfei Zhao, Qiang Huang, Rui-Sheng Wang, Xiang-Sun Zhang","doi":"10.1109/ISB.2011.6033181","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033181","url":null,"abstract":"High-throughput technologies have produced a large number of protein-protein interactions (PPIs) for different species. As protein domains are functional and structural units of proteins, many computational efforts have been made to identify domain-domain interactions (DDIs) from PPIs. Parsimony assumption is widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they could detect specific DDIs, which is often hard for many probabilistic methods. In this paper, we revisit the parsimony model by presenting two important extensions. First, ‘complex networks’ as an emerging concept is incorporated as prior knowledge into the parsimony model. With this improvement, the prediction accuracy increases, which to some extent enhances the biological meaning of the common property of complex networks. Second, two randomization tests are designed to show the parsimony nature of the DDIs in mediating PPIs, which corroborates the model validation.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"32 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":"134166753","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.2011.6033173
Xianfu Gao, Wanjia Chen, Rongxia Li, Minfeng Wang, Chunlei Chen, R. Zeng, Yueyi Deng
Background: Membranous nephropathy is an important glomerular disease characterized by podocyte injury and proteinuria, but no metabolomics research was reported as yet. Here, we performed a parallel metabolomics study, based on human urine and serum, to comprehensively profile systematic metabolic alterations, identify differential metabolites, and understand the pathogenic mechanism of membranous nephropathy. Results: There were obvious metabolic distinctions between the membranous nephropathy patients with urine protein lower than 3.5 g/24h (LUPM) and those higher than 3.5 g/24h (HUPM) by PLS-DA model analysis. In total, 26 urine metabolites and 9 serum metabolites were identified to account for such differences, and the majority of metabolites was significantly increased in HUPM patients whether for urines or for serums. Combining the results of urine with serum, all differential metabolites were classified to 5 classes. This classification helps globally insight the systematic metabolic alteration before and after blood flowing through kidney. Citric acid and 4 amino acids were markedly increased only in the serum samples of HUPM patients, implying more impaired filtration function of kidneys of HUPM patients than LUPM patients. The dicarboxylic acids, phenolic acids, and cholesterol were significantly elevated only in urines of HUPM patients, suggesting more severe oxidative attacks than LUPM patients. Conclusion: Parallel metabolomics of urine and serum revealed the systematic metabolic variations associated with LUPM and HUPM patients, where HUPM patients suffered more severe injury of kidney function and oxidative stresses than LUPM patients. This research exhibited a promising application of parallel metabolomics in renal diseases.
{"title":"Parallel metabolomics of urine and serum revealed systematic alteration associated with renal disease","authors":"Xianfu Gao, Wanjia Chen, Rongxia Li, Minfeng Wang, Chunlei Chen, R. Zeng, Yueyi Deng","doi":"10.1109/ISB.2011.6033173","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033173","url":null,"abstract":"Background: Membranous nephropathy is an important glomerular disease characterized by podocyte injury and proteinuria, but no metabolomics research was reported as yet. Here, we performed a parallel metabolomics study, based on human urine and serum, to comprehensively profile systematic metabolic alterations, identify differential metabolites, and understand the pathogenic mechanism of membranous nephropathy. Results: There were obvious metabolic distinctions between the membranous nephropathy patients with urine protein lower than 3.5 g/24h (LUPM) and those higher than 3.5 g/24h (HUPM) by PLS-DA model analysis. In total, 26 urine metabolites and 9 serum metabolites were identified to account for such differences, and the majority of metabolites was significantly increased in HUPM patients whether for urines or for serums. Combining the results of urine with serum, all differential metabolites were classified to 5 classes. This classification helps globally insight the systematic metabolic alteration before and after blood flowing through kidney. Citric acid and 4 amino acids were markedly increased only in the serum samples of HUPM patients, implying more impaired filtration function of kidneys of HUPM patients than LUPM patients. The dicarboxylic acids, phenolic acids, and cholesterol were significantly elevated only in urines of HUPM patients, suggesting more severe oxidative attacks than LUPM patients. Conclusion: Parallel metabolomics of urine and serum revealed the systematic metabolic variations associated with LUPM and HUPM patients, where HUPM patients suffered more severe injury of kidney function and oxidative stresses than LUPM patients. This research exhibited a promising application of parallel metabolomics in renal diseases.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"45 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":"127441957","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.2011.6033122
Wei Tian, Hongyuan Zhu, X. Lei, P. Ao
Noises in biological modeling may be classified into two kinds: intrinsic noise, which derives from the variability in dominant molecular interaction and is responsible for the given phenomenon, and extrinsic noise, which arises from other sources, like fluctuations in the environment and so on. Phage lambda is a simple model organism that exhibits important noisy characteristics. It lives in either lysogenic state or lytic state after infecting a bacterium, that is determined by a genetic switch. The mathematical modeling of this genetic switch typically only considers intrinsic noise, though a previous study by one of present authors suggested the critical role of extrinsic noise. In the present study by comparing theoretical results of phage lambda in lysogeny with experiment data, we first achieve good numerical agreements of five constrains of phage lambda for averaged variables. This success indicates that current dominant molecular agents are right. In addition, we confirm the existence of extrinsic noise in lambda genetic switch and find it surprisingly large. This finding calls for an extension of the current mathematical model to better describe the noises. We also point out some possible sources of extrinsic noise.
{"title":"Extrinsic vs. intrinsic noises in phage lambda genetic switch","authors":"Wei Tian, Hongyuan Zhu, X. Lei, P. Ao","doi":"10.1109/ISB.2011.6033122","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033122","url":null,"abstract":"Noises in biological modeling may be classified into two kinds: intrinsic noise, which derives from the variability in dominant molecular interaction and is responsible for the given phenomenon, and extrinsic noise, which arises from other sources, like fluctuations in the environment and so on. Phage lambda is a simple model organism that exhibits important noisy characteristics. It lives in either lysogenic state or lytic state after infecting a bacterium, that is determined by a genetic switch. The mathematical modeling of this genetic switch typically only considers intrinsic noise, though a previous study by one of present authors suggested the critical role of extrinsic noise. In the present study by comparing theoretical results of phage lambda in lysogeny with experiment data, we first achieve good numerical agreements of five constrains of phage lambda for averaged variables. This success indicates that current dominant molecular agents are right. In addition, we confirm the existence of extrinsic noise in lambda genetic switch and find it surprisingly large. This finding calls for an extension of the current mathematical model to better describe the noises. We also point out some possible sources of extrinsic noise.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"4 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":"123868694","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.2011.6033152
Ding-Kang Wang, Shu-Hua Zhai, Bin Wang, Guifen Sun
We studied the floral morphology and floral biology of Cynanchum otophyllum Schneid in experimental plots and field sites. Some observations were augmented by laboratory studies of floral traits, including scanning electron microscopy and light microscopy. The flower was characterized by a staminal corona. The pollinia were lodged in sacs on each side of the stigma and needed pollen vector for fruit production. C. otophyllum has characteristics similar to bee-pollinated plants. Honeybees (Apis cerana Fabricius) were the main pollinators. Pollinaria removal and pollinia insertion rates were low at 5.4% and 0.45%, respectively. The fruit set was only 2.2% in natural population. The flowering span of C. otophyllum was about 3 months, and the functional longevity of individual flowers was 6–8 days. The extended period may be related to the relatively low levels of effective pollinator activity. The flowers were self-incompatible. Umbels displayed open flowers for 9–10 days, and there was a large overlap in flowering time within and among inflorescences in a single plant. Therefore, a high level of self-pollination is possible. From the significant increases in fruit set in cross-pollinated flowers (12.6%) compared with self-pollinated flowers (1.52%), the low fruit set in C. otophyllum could be partially explained by pollen limitation.
{"title":"Floral structure and pollination in relation to fruit set in cynanchum otophyllum schneid","authors":"Ding-Kang Wang, Shu-Hua Zhai, Bin Wang, Guifen Sun","doi":"10.1109/ISB.2011.6033152","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033152","url":null,"abstract":"We studied the floral morphology and floral biology of Cynanchum otophyllum Schneid in experimental plots and field sites. Some observations were augmented by laboratory studies of floral traits, including scanning electron microscopy and light microscopy. The flower was characterized by a staminal corona. The pollinia were lodged in sacs on each side of the stigma and needed pollen vector for fruit production. C. otophyllum has characteristics similar to bee-pollinated plants. Honeybees (Apis cerana Fabricius) were the main pollinators. Pollinaria removal and pollinia insertion rates were low at 5.4% and 0.45%, respectively. The fruit set was only 2.2% in natural population. The flowering span of C. otophyllum was about 3 months, and the functional longevity of individual flowers was 6–8 days. The extended period may be related to the relatively low levels of effective pollinator activity. The flowers were self-incompatible. Umbels displayed open flowers for 9–10 days, and there was a large overlap in flowering time within and among inflorescences in a single plant. Therefore, a high level of self-pollination is possible. From the significant increases in fruit set in cross-pollinated flowers (12.6%) compared with self-pollinated flowers (1.52%), the low fruit set in C. otophyllum could be partially explained by pollen limitation.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"31 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":"122995831","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.2011.6033164
Chao Dai, Wenyuan Li, Juan Liu, X. Zhou
Alternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. In this paper we study splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors. We have designed a tensor-based approach to identify co-splicing clusters that appear frequently across multiple conditions, thus very likely to represent splicing modules - a unit in the splicing regulatory network. In particular, we model each RNA-seq dataset as a co-splicing network, where the nodes represent exons and the edges are weighted by the correlations between exon inclusion rate profiles. We apply our tensor-based method to the 19 co-splicing networks derived from RNA-seq datasets and identify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent splicing modules by validating against four biological knowledge databases. The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach. We also demonstrate that the co-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, and that the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced.
{"title":"Systematic reconstruction of splicing regulatory modules by integrating many RNA-seq datasets","authors":"Chao Dai, Wenyuan Li, Juan Liu, X. Zhou","doi":"10.1109/ISB.2011.6033164","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033164","url":null,"abstract":"Alternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. In this paper we study splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors. We have designed a tensor-based approach to identify co-splicing clusters that appear frequently across multiple conditions, thus very likely to represent splicing modules - a unit in the splicing regulatory network. In particular, we model each RNA-seq dataset as a co-splicing network, where the nodes represent exons and the edges are weighted by the correlations between exon inclusion rate profiles. We apply our tensor-based method to the 19 co-splicing networks derived from RNA-seq datasets and identify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent splicing modules by validating against four biological knowledge databases. The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach. We also demonstrate that the co-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, and that the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"1 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":"131120679","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.2011.6033125
Zunming Liu, Jingfa Xiao, Jiayan Wu, Jun Yu
RNA-Seq has become one of the most important new approaches for gene expression analysis as well as transcriptome analysis. The issue of how to analysis RNA-Seq data is one of the biggest challenges for current transcriptomics research. In this study, we develop an RNA-Seq data annotation pipeline named RNADAP, which is an efficient transcriptomes analysis tool to evaluate gene expression quantization in isoform level and compatible for reads data from different platforms. RNADAP is a typical Java application so the pipeline could be carried out on Windows as well as Linux. The installation process is convenient and user can grasp it very easily with a friendly user interface. RNADAP is a free, open-source software and written in Java. All source code, instructions, testing data and additional scripts are available at http://rnadap.sourceforge.net/.
{"title":"RNADAP—RNA-Seq data annotation pipeline","authors":"Zunming Liu, Jingfa Xiao, Jiayan Wu, Jun Yu","doi":"10.1109/ISB.2011.6033125","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033125","url":null,"abstract":"RNA-Seq has become one of the most important new approaches for gene expression analysis as well as transcriptome analysis. The issue of how to analysis RNA-Seq data is one of the biggest challenges for current transcriptomics research. In this study, we develop an RNA-Seq data annotation pipeline named RNADAP, which is an efficient transcriptomes analysis tool to evaluate gene expression quantization in isoform level and compatible for reads data from different platforms. RNADAP is a typical Java application so the pipeline could be carried out on Windows as well as Linux. The installation process is convenient and user can grasp it very easily with a friendly user interface. RNADAP is a free, open-source software and written in Java. All source code, instructions, testing data and additional scripts are available at http://rnadap.sourceforge.net/.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"39 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":"131752604","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.2011.6033154
Wei Zhang, Xiufen Zou
Based on the model of the Xenopus embryonic cell cycle proposed in literature [1], which can exhibit sustained limit cycle oscillations, we first build a multi-cell system of these oscillators that are coupled through a common complex protein that plays an important role in the core regulation of cell-cycle oscillators, and then show synchronization features in this coupled multi-cell system. Through bifurcation analysis and numerical simulations, we give synchronization intervals of the sensitive parameters in the individual oscillator and the coupling parameters in the coupled oscillators. Then, we analyze the effects of these parameters on synchronization time, period and amplitude, and find interesting phenomena, e.g., there are two synchronization intervals of activation coefficient in the Hill function of the activated CDK1 that activates the Plk1, and different synchronization intervals have distinct influences on synchronization time, period and amplitude. More interestingly, we find that the coupled system can switch between a stable state and a stable periodic orbit. These results suggest that the reaction process that the activated cyclin-CDK1 activates the Plk1 has very important influence on the synchronization ability of the coupled system. Our work not only can be viewed as an important step toward the comprehensive understanding for mechanisms of Xenopus embryonic cell cycle and but also can provide the guide for further biological experiments.
{"title":"Synchronization feature of coupled cell-cycle oscillators","authors":"Wei Zhang, Xiufen Zou","doi":"10.1109/ISB.2011.6033154","DOIUrl":"https://doi.org/10.1109/ISB.2011.6033154","url":null,"abstract":"Based on the model of the Xenopus embryonic cell cycle proposed in literature [1], which can exhibit sustained limit cycle oscillations, we first build a multi-cell system of these oscillators that are coupled through a common complex protein that plays an important role in the core regulation of cell-cycle oscillators, and then show synchronization features in this coupled multi-cell system. Through bifurcation analysis and numerical simulations, we give synchronization intervals of the sensitive parameters in the individual oscillator and the coupling parameters in the coupled oscillators. Then, we analyze the effects of these parameters on synchronization time, period and amplitude, and find interesting phenomena, e.g., there are two synchronization intervals of activation coefficient in the Hill function of the activated CDK1 that activates the Plk1, and different synchronization intervals have distinct influences on synchronization time, period and amplitude. More interestingly, we find that the coupled system can switch between a stable state and a stable periodic orbit. These results suggest that the reaction process that the activated cyclin-CDK1 activates the Plk1 has very important influence on the synchronization ability of the coupled system. Our work not only can be viewed as an important step toward the comprehensive understanding for mechanisms of Xenopus embryonic cell cycle and but also can provide the guide for further biological experiments.","PeriodicalId":355056,"journal":{"name":"2011 IEEE International Conference on Systems Biology (ISB)","volume":"3 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":"134554254","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.2011.6033117
H. Yalamanchili, Junwen Wang, Quan-Wu Xiao
A large amount of proteomic data is being generated due to the advancements in high-throughput genome sequencing. But the rate of functional annotation of these sequences falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOfigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. The lack of annotation coverage of the existing methods advocates novel methods to improve protein function prediction. Here we present a automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. The main idea of this algorithm is to define a distance metric that corresponds to the similarity of the subsequences and reflects how the human brain can distinguish different sequences. Given query protein, we predict the most similar target protein using a two layered neural response algorithm and thereby assigned the GO term of the target protein to the query. Our method predicted and ranked the actual leaf GO term among the top 5 probable GO terms with 87.66% accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The NRProF program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.
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