Pub Date : 2012-09-27DOI: 10.1109/ISB.2012.6314121
Wei Fang, Xingzhi Chang, Xiaoquan Su, Jian Xu, Deli Zhang, K. Ning
As more than 90% of microbial community could not be isolated and cultivated, the metagenomic methods have been commonly used to analyze the microbial community as a whole. With the fast acumination of metagenomic samples, it is now intriguing to find simple biomarkers, especially functional biomarkers, which could distinguish different metagenomic samples. Next-generation sequencing techniques have enabled the detection of very accurate gene-presence (abundance) values in metagenomic studies. And the presence/absence or different abundance values for a set of genes could be used as appropriate biomarker for identification of the corresponding microbial community's phenotype. However, it is not yet clear how to select such a set of genes (features), and how accurate would it be for such a set of selected genes on prediction of microbial community's phenotype. In this study, we have evaluated different machine learning methods, including feature selection methods and classification methods, for selection of biomarkers that could distinguish different samples. Then we proposed a machine learning framework, which could discover biomarkers for different microbial communities from the mining of metagenomic data. Given a set of features (genes) and their presence values in multiple samples, we first selected discriminative features as candidate by feature selection, and then selected the feature sets with low error rate and classification accuracies as biomarkers by classification method. We have selected whole genome sequencing data from simulation, public domain and in-house metagenomic data generation facilities. We tested the framework on prediction and evaluation of the biomarkers. Results have shown that the framework could select functional biomarkers with very high accuracy. Therefore, this framework would be a suitable tool to discover functional biomarkers to distinguish different microbial communities.
{"title":"A machine learning framework of functional biomarker discovery for different microbial communities based on metagenomic data","authors":"Wei Fang, Xingzhi Chang, Xiaoquan Su, Jian Xu, Deli Zhang, K. Ning","doi":"10.1109/ISB.2012.6314121","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314121","url":null,"abstract":"As more than 90% of microbial community could not be isolated and cultivated, the metagenomic methods have been commonly used to analyze the microbial community as a whole. With the fast acumination of metagenomic samples, it is now intriguing to find simple biomarkers, especially functional biomarkers, which could distinguish different metagenomic samples. Next-generation sequencing techniques have enabled the detection of very accurate gene-presence (abundance) values in metagenomic studies. And the presence/absence or different abundance values for a set of genes could be used as appropriate biomarker for identification of the corresponding microbial community's phenotype. However, it is not yet clear how to select such a set of genes (features), and how accurate would it be for such a set of selected genes on prediction of microbial community's phenotype. In this study, we have evaluated different machine learning methods, including feature selection methods and classification methods, for selection of biomarkers that could distinguish different samples. Then we proposed a machine learning framework, which could discover biomarkers for different microbial communities from the mining of metagenomic data. Given a set of features (genes) and their presence values in multiple samples, we first selected discriminative features as candidate by feature selection, and then selected the feature sets with low error rate and classification accuracies as biomarkers by classification method. We have selected whole genome sequencing data from simulation, public domain and in-house metagenomic data generation facilities. We tested the framework on prediction and evaluation of the biomarkers. Results have shown that the framework could select functional biomarkers with very high accuracy. Therefore, this framework would be a suitable tool to discover functional biomarkers to distinguish different microbial communities.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125049168","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314155
L. Nowak, M. Ogorzałek, M. P. Pawlowski
This paper demonstrates a method for detecting pigment based dermatoscopic structure called pigment network. This structure is used in dermatoscopy as one of the criteria in clinical evaluation of pigmented skin lesions and can indicate if a lesion is of malignant nature. For detection process we have developed an adaptive filter, inspired by Swarm Intelligence (SI) optimization algorithms. The introduced filtering method is applied in a non-linear manner, to processed dermatoscopic image of a skin lesion. The non-linear approach derives from SI algorithms, and allows selective image filtering. In the beginning of filtration process, the filters (agents) are randomly applied to sections of the image, where each of them adapts its output based on the neighborhood surrounding it. Agents share its information with other agents that are located in immediate vicinity. This is a new approach to the problem of dermatoscopic structure detection, and it is highly flexible, as it can be applied to images without the need of previous pre-processing stage. This feature is highly desirable, mainly due to the fact that in most cases of computer aided diagnostic, input images need to be pre-processed (e.g.: brightness normalization, histogram equation, contrast enhancement, color normalization) and results of this can introduce unwanted artifacts, so this step need to be verified by human. Results of applying the introduced method can be used as one of the differential structures criteria for calculating the Total Dermatoscopy Score (TDS) of the ABCD rule.
{"title":"Pigmented network structure detection using semi-smart adaptive filters","authors":"L. Nowak, M. Ogorzałek, M. P. Pawlowski","doi":"10.1109/ISB.2012.6314155","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314155","url":null,"abstract":"This paper demonstrates a method for detecting pigment based dermatoscopic structure called pigment network. This structure is used in dermatoscopy as one of the criteria in clinical evaluation of pigmented skin lesions and can indicate if a lesion is of malignant nature. For detection process we have developed an adaptive filter, inspired by Swarm Intelligence (SI) optimization algorithms. The introduced filtering method is applied in a non-linear manner, to processed dermatoscopic image of a skin lesion. The non-linear approach derives from SI algorithms, and allows selective image filtering. In the beginning of filtration process, the filters (agents) are randomly applied to sections of the image, where each of them adapts its output based on the neighborhood surrounding it. Agents share its information with other agents that are located in immediate vicinity. This is a new approach to the problem of dermatoscopic structure detection, and it is highly flexible, as it can be applied to images without the need of previous pre-processing stage. This feature is highly desirable, mainly due to the fact that in most cases of computer aided diagnostic, input images need to be pre-processed (e.g.: brightness normalization, histogram equation, contrast enhancement, color normalization) and results of this can introduce unwanted artifacts, so this step need to be verified by human. Results of applying the introduced method can be used as one of the differential structures criteria for calculating the Total Dermatoscopy Score (TDS) of the ABCD rule.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125348400","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314122
Huayong Xu, Hui Yu, K. Tu, Qianqian Shi, Chaochun Wei, Yuan-yuan Li, Yixue Li
While we witness rapid progresses in development of methodologies/algorithms for constructing and analyzing the combinatorial regulation network which includes both TF regulators and miRNA regulators, we find a lack of tools or servers available for facilitating related works. A web service is especially needed that allows user to upload their own expression datasets and mine the combinatorial gene reglatory networks regarding the particular experimental context. Herein we report cGRNexp, a web platform for building combinatorial gene regulation networks based on user-uploaded gene expression datasets. In cGRNexp, we deposit three types of sequence-matching-based regulatory relationships and implement two functional modules for processing microRNA-perturbed gene expression datasets and parallel miRNA/mRNA expression datasets. With the microarrays and next-generation sequencing platforms being increasingly accessible, a large amount of miRNA or mRNA expression datasets will be attainable in the near future, and thus, our web platform cGRNexp will be very useful for helping people mine the conditional combinatorial regulatory networks from their own expression datasets. cGRNexp is accessible at http://www.scbit.org/cgrnexp/.
{"title":"cGRNexp: a web platform for building combinatorial gene regulation networks based on user-uploaded gene expression datasets","authors":"Huayong Xu, Hui Yu, K. Tu, Qianqian Shi, Chaochun Wei, Yuan-yuan Li, Yixue Li","doi":"10.1109/ISB.2012.6314122","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314122","url":null,"abstract":"While we witness rapid progresses in development of methodologies/algorithms for constructing and analyzing the combinatorial regulation network which includes both TF regulators and miRNA regulators, we find a lack of tools or servers available for facilitating related works. A web service is especially needed that allows user to upload their own expression datasets and mine the combinatorial gene reglatory networks regarding the particular experimental context. Herein we report cGRNexp, a web platform for building combinatorial gene regulation networks based on user-uploaded gene expression datasets. In cGRNexp, we deposit three types of sequence-matching-based regulatory relationships and implement two functional modules for processing microRNA-perturbed gene expression datasets and parallel miRNA/mRNA expression datasets. With the microarrays and next-generation sequencing platforms being increasingly accessible, a large amount of miRNA or mRNA expression datasets will be attainable in the near future, and thus, our web platform cGRNexp will be very useful for helping people mine the conditional combinatorial regulatory networks from their own expression datasets. cGRNexp is accessible at http://www.scbit.org/cgrnexp/.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126643291","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}
Gene-gene association or protein-protein interaction databases have been important resource for the study of cellular functions and human diseases. A number of gene association databases have been available in the public domain. Each of these databases has its own unique virtues, but no single database could provide enough confidence and coverage. These years some meta-databases have been built by integrating various resources of gene functional associations and weighing the evidence of each association by some score systems. In this work, we compared three weighted genome-scale human gene association networks constructed from three such meta-databases, STRING, FunCoup and FLN, respectively. We found that the three networks share a large fraction of common genes but only quite limited overlapped interactions. However, most genes involved in important cellular processes and human diseases, as well as their pairwise interactions, is included in all of the three networks. This explains why all the three networks have been successfully applied in the study of cellular functions and diseases mechanisms. We believe that further integration of these meta-databases would provide higher confidence and coverage of gene associations in human proteome and facilitate the study of human gene association networks.
{"title":"A comparison of three weighted human gene functional association networks","authors":"Jing Zhao, Chun-Lin Wang, Tinghong Yang, Bo Li, Xing Chen, Xiaona Shen, Ling Fang","doi":"10.1109/ISB.2012.6314108","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314108","url":null,"abstract":"Gene-gene association or protein-protein interaction databases have been important resource for the study of cellular functions and human diseases. A number of gene association databases have been available in the public domain. Each of these databases has its own unique virtues, but no single database could provide enough confidence and coverage. These years some meta-databases have been built by integrating various resources of gene functional associations and weighing the evidence of each association by some score systems. In this work, we compared three weighted genome-scale human gene association networks constructed from three such meta-databases, STRING, FunCoup and FLN, respectively. We found that the three networks share a large fraction of common genes but only quite limited overlapped interactions. However, most genes involved in important cellular processes and human diseases, as well as their pairwise interactions, is included in all of the three networks. This explains why all the three networks have been successfully applied in the study of cellular functions and diseases mechanisms. We believe that further integration of these meta-databases would provide higher confidence and coverage of gene associations in human proteome and facilitate the study of human gene association networks.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126346809","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314129
Jing Yang, X. Ling, L. Yao, Hua-Liang Wei, V. Kadirkamanathan
Bioethanol production by means of anaerobic thermophilic microorganisms with pentose or hexose as the substrate are of paramount importance in sustainable fuel innovation. Manipulation of microorganisms and the associated experiment conditions by means of various ad-hoc technology is obviously the most straightforward way with the aim of maximizing bioethanol yield. However, methodology by means of mathematical modeling and analysis is often neglected among these routines. In this paper, typical input-output models are applied in the metabolic system analysis of Thermoanaerobacter sp. X514 under sole glucose substrate, sole xylose substrate and mixed glucose and xylose substrates conditions. Orthogonal Least Squares (OLS) approach is used for model parameter estimation. Model selection is proposed in order to testify the generality of the suggested model. System identification results illustrate that various forms of Nonlinear AutoRegressive with eXogenous input models (NARX) are applicable in delineating the system where different substrates (glucose or xylose) are utilized during the experiments. The proposed model structure infers that the yields of various products in X514 are mainly driven by the history information of the substrate consumption change. Moreover, the interaction between the main fermentation products of X514 is indirectly connected through the proposed models.
{"title":"System identification of the fermentation system of Thermoanaerobacter sp. X514","authors":"Jing Yang, X. Ling, L. Yao, Hua-Liang Wei, V. Kadirkamanathan","doi":"10.1109/ISB.2012.6314129","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314129","url":null,"abstract":"Bioethanol production by means of anaerobic thermophilic microorganisms with pentose or hexose as the substrate are of paramount importance in sustainable fuel innovation. Manipulation of microorganisms and the associated experiment conditions by means of various ad-hoc technology is obviously the most straightforward way with the aim of maximizing bioethanol yield. However, methodology by means of mathematical modeling and analysis is often neglected among these routines. In this paper, typical input-output models are applied in the metabolic system analysis of Thermoanaerobacter sp. X514 under sole glucose substrate, sole xylose substrate and mixed glucose and xylose substrates conditions. Orthogonal Least Squares (OLS) approach is used for model parameter estimation. Model selection is proposed in order to testify the generality of the suggested model. System identification results illustrate that various forms of Nonlinear AutoRegressive with eXogenous input models (NARX) are applicable in delineating the system where different substrates (glucose or xylose) are utilized during the experiments. The proposed model structure infers that the yields of various products in X514 are mainly driven by the history information of the substrate consumption change. Moreover, the interaction between the main fermentation products of X514 is indirectly connected through the proposed models.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131832656","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}
In recent years, the gene expression profiles are used for cancer recognition. But the researchers are disturbed by their large variables and small observes. In this paper, a novel feature selection method based on correlation-based feature selection(CFS) was proposed. Firstly, the measures of variable to variable and variable to observe were calculated respectively. Then we utilized heuristic search method to search the space of variable for selecting informative gene subset and the subset weight was computed using these measures. Through regression we obtained a subset of distinguished genes. Finally, the stratified sampling strategy was presented to obtain the most informative genes. And classification performance was tested to evaluate the proposed method. Ten-fold cross-validation experiment was performed in three datasets including leukemia, colon cancer and prostate tumor. The experimental results show that the proposed method can obtain the distinguished gene subset and different classifier can acquire better classification performance with this subset.
{"title":"A novel feature selection method based on CFS in cancer recognition","authors":"Xinguo Lu, Xianghua Peng, Ping Liu, Yong Deng, Bingtao Feng, Bo Liao","doi":"10.1109/ISB.2012.6314141","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314141","url":null,"abstract":"In recent years, the gene expression profiles are used for cancer recognition. But the researchers are disturbed by their large variables and small observes. In this paper, a novel feature selection method based on correlation-based feature selection(CFS) was proposed. Firstly, the measures of variable to variable and variable to observe were calculated respectively. Then we utilized heuristic search method to search the space of variable for selecting informative gene subset and the subset weight was computed using these measures. Through regression we obtained a subset of distinguished genes. Finally, the stratified sampling strategy was presented to obtain the most informative genes. And classification performance was tested to evaluate the proposed method. Ten-fold cross-validation experiment was performed in three datasets including leukemia, colon cancer and prostate tumor. The experimental results show that the proposed method can obtain the distinguished gene subset and different classifier can acquire better classification performance with this subset.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934675","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314116
Hao Guo, Yun-ping Zhu, Dong Li, F. He, Qi-jun Liu
Gene expression microarray enables us to measure the gene expression levels for thousands of genes at the same time. Here, we constructed the non-negative matrix factorization analysis strategy (NMFAS) to dig the underlying biological pathways related with various diseases by factorizing the pathway expression matrix, which was extracted from microarray matrix using pathway membership information, into the product of row and column vectors. We defined row vector as the pathway activity and column vector as the gene contribution weight. Via comparing the pathway activity of two different sample groups, we can identify significantly expressed pathways. We applied this strategy on two different cases: smoking and type 2 diabetes (DM2). We found 152 differentially expressed pathways by the comparison of pathway activity between smoker and never smoker, including pathways that have been validated in literature, such as “O-Glycans biosynthesis” and “Glutathione metabolism”. We also found important genes related to smoking phenotype, such as NQO, HSPA1A, ALDH3A1. As for DM2 analysis, our results suggested 9 pathways were significantly expressed, including typical pathways like “Oxidative phosphorylation” and “mTOR signaling pathway”, and found genes like CAPNS1, APP, COX7A1, COX7B, which might play important roles in the cellular regulations of DM2. In conclusion, Our strategy can be efficiently used to integrate gene expression profiles and biological pathway information to identify the key processes underlying human disease and can identify gene pathways missed by alternative approaches.
{"title":"Using NMFAS to identify key biological pathways associated with human diseases","authors":"Hao Guo, Yun-ping Zhu, Dong Li, F. He, Qi-jun Liu","doi":"10.1109/ISB.2012.6314116","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314116","url":null,"abstract":"Gene expression microarray enables us to measure the gene expression levels for thousands of genes at the same time. Here, we constructed the non-negative matrix factorization analysis strategy (NMFAS) to dig the underlying biological pathways related with various diseases by factorizing the pathway expression matrix, which was extracted from microarray matrix using pathway membership information, into the product of row and column vectors. We defined row vector as the pathway activity and column vector as the gene contribution weight. Via comparing the pathway activity of two different sample groups, we can identify significantly expressed pathways. We applied this strategy on two different cases: smoking and type 2 diabetes (DM2). We found 152 differentially expressed pathways by the comparison of pathway activity between smoker and never smoker, including pathways that have been validated in literature, such as “O-Glycans biosynthesis” and “Glutathione metabolism”. We also found important genes related to smoking phenotype, such as NQO, HSPA1A, ALDH3A1. As for DM2 analysis, our results suggested 9 pathways were significantly expressed, including typical pathways like “Oxidative phosphorylation” and “mTOR signaling pathway”, and found genes like CAPNS1, APP, COX7A1, COX7B, which might play important roles in the cellular regulations of DM2. In conclusion, Our strategy can be efficiently used to integrate gene expression profiles and biological pathway information to identify the key processes underlying human disease and can identify gene pathways missed by alternative approaches.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125855373","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}
Motivation:MiRNAs can downregulate gene expression by mRNA cleavage or translational repression. Discovering human encoded miRNAs that regulate the influenza virus genome is important for molecular targets for drug development, and it also plays positive role in influenza control and prevention. Methods: We propose a new method based on scoring to discover human encoded miRNAs that regulate the influenza virus genome. The scoring based on the same complementary sites, the secondary structure of the complementary sites and the binding sites of all sequences respectively. Among them, taking the secondary structure as a vital factor is a new attempt. Results: Has-miR-489, has-miR-325, has-miR-876-3p and has-miR-2117 are targeted HA, PB2, MP and NS of influenza A, respectively.
{"title":"Human encoded miRNAs that regulate the inflenenza virus genome","authors":"H. Zhang, Xin Li, Yuanning Liu, Zhi Li, Minggang Hu, Dong Xu","doi":"10.1109/ISB.2012.6314107","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314107","url":null,"abstract":"Motivation:MiRNAs can downregulate gene expression by mRNA cleavage or translational repression. Discovering human encoded miRNAs that regulate the influenza virus genome is important for molecular targets for drug development, and it also plays positive role in influenza control and prevention. Methods: We propose a new method based on scoring to discover human encoded miRNAs that regulate the influenza virus genome. The scoring based on the same complementary sites, the secondary structure of the complementary sites and the binding sites of all sequences respectively. Among them, taking the secondary structure as a vital factor is a new attempt. Results: Has-miR-489, has-miR-325, has-miR-876-3p and has-miR-2117 are targeted HA, PB2, MP and NS of influenza A, respectively.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128501214","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314124
Ya-Jing Huang, W. Yong
Apoptosis is important for maintaining normal embryonic development, tissue homeostasis and normal immune-system operation in multicellular organisms. Its malfunction may result in serious diseases such as cancer, autoimmunity, and neurodegeneration. In apoptosis, tens of species are present in many biochemical reactions with times scales of widely differing orders of magnitude. According to the law of mass action, apoptosis is usually described with a large and stiff system of ODEs (ordinary differential equations). The goal of this work is to derive a simple system of ODEs by using the classical PEA (partial equilibrium approximation) method. For this purpose, we firstly justify the mathematical correctness of the PEA in a quite general framework. On the basis of this result, we simplify the Fas-signaling pathway model proposed by Hua et al. (2005) by assuming the fastness of several reversible reactions. Numerical simulations and sensitivity analysis show that our simplification model is reliable.
{"title":"A stable simplification of a fas-signaling pathway model for apoptosis","authors":"Ya-Jing Huang, W. Yong","doi":"10.1109/ISB.2012.6314124","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314124","url":null,"abstract":"Apoptosis is important for maintaining normal embryonic development, tissue homeostasis and normal immune-system operation in multicellular organisms. Its malfunction may result in serious diseases such as cancer, autoimmunity, and neurodegeneration. In apoptosis, tens of species are present in many biochemical reactions with times scales of widely differing orders of magnitude. According to the law of mass action, apoptosis is usually described with a large and stiff system of ODEs (ordinary differential equations). The goal of this work is to derive a simple system of ODEs by using the classical PEA (partial equilibrium approximation) method. For this purpose, we firstly justify the mathematical correctness of the PEA in a quite general framework. On the basis of this result, we simplify the Fas-signaling pathway model proposed by Hua et al. (2005) by assuming the fastness of several reversible reactions. Numerical simulations and sensitivity analysis show that our simplification model is reliable.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123396677","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 : 2012-09-27DOI: 10.1109/ISB.2012.6314133
Yueying Yang, Di Liu, Jun Meng
The focus of the network of research is to determine their community or module, it helps the functional organization and evolution of the network. Modular can be seen as a function of a dynamic cell system executing complex functions in a living cell. How to identify the precious knowledge resources to build a more reliable module is still one of the most important and difficult problems in bioinformatics. We put forward a state space model combining the topological method to describe the time and space module in the cell cycle of the process. Not only our module function sets of genes related to identify a condition to activate or suppress in the cell cycle process in S.cerevisiae, but also have many different solutions, which have evolved into different molecular components will be the assembly at the right time in the cell cycle. The resulting module mapping analysis showed several assumptions connection biological process to a particular cell cycle conditions.
{"title":"Module of cellular networks in saccharomyces cerevisiae","authors":"Yueying Yang, Di Liu, Jun Meng","doi":"10.1109/ISB.2012.6314133","DOIUrl":"https://doi.org/10.1109/ISB.2012.6314133","url":null,"abstract":"The focus of the network of research is to determine their community or module, it helps the functional organization and evolution of the network. Modular can be seen as a function of a dynamic cell system executing complex functions in a living cell. How to identify the precious knowledge resources to build a more reliable module is still one of the most important and difficult problems in bioinformatics. We put forward a state space model combining the topological method to describe the time and space module in the cell cycle of the process. Not only our module function sets of genes related to identify a condition to activate or suppress in the cell cycle process in S.cerevisiae, but also have many different solutions, which have evolved into different molecular components will be the assembly at the right time in the cell cycle. The resulting module mapping analysis showed several assumptions connection biological process to a particular cell cycle conditions.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"72 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891747","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：