Pub Date : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735930
Xiangfang Li, Lijun Qian, M. Bittner, E. Dougherty
In this paper, a model that combining cell population and genetic regulation within a single cell by using stochastic hybrid systems is proposed. The objective is to study the response of a population of cancer cells to various drugs that targeting the proliferation and survival pathways. The proposed model captures both the dynamics of the cell population and the dynamics of gene regulations within each individual cell. We use drug Lapatinib applied to colon cancer cell line HCT-116 as an example to validate the proposed model. Simulation results demonstrate the phenomena that observed in TGen experiments.
{"title":"Drug effect study on proliferation and survival pathways on cell line-based platform: A stochastic hybrid systems approach","authors":"Xiangfang Li, Lijun Qian, M. Bittner, E. Dougherty","doi":"10.1109/GENSIPS.2013.6735930","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735930","url":null,"abstract":"In this paper, a model that combining cell population and genetic regulation within a single cell by using stochastic hybrid systems is proposed. The objective is to study the response of a population of cancer cells to various drugs that targeting the proliferation and survival pathways. The proposed model captures both the dynamics of the cell population and the dynamics of gene regulations within each individual cell. We use drug Lapatinib applied to colon cancer cell line HCT-116 as an example to validate the proposed model. Simulation results demonstrate the phenomena that observed in TGen experiments.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127104846","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735938
Xiaoning Qian, E. Dougherty
In this paper, we investigate a phenotypically constrained inference algorithm to reconstruct genetic regulatory networks modeled as Boolean networks (BNs). Based on a previous universal Minimum Description Length (uMDL) network inference algorithm, we study whether adding the prior information based on prescribed attractors or steady states can help better reconstruct the underlying gene regulatory relationships. Comparing the network inference performance with and without prescribed steady states, the experiments based on randomly generated networks as well as a metastatic melanoma network have shown that the phenotypically constrained inference obtains improved performance when we have small numbers of state transition observations.
{"title":"Phenotypically constrained Boolean network inference with prescribed steady states","authors":"Xiaoning Qian, E. Dougherty","doi":"10.1109/GENSIPS.2013.6735938","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735938","url":null,"abstract":"In this paper, we investigate a phenotypically constrained inference algorithm to reconstruct genetic regulatory networks modeled as Boolean networks (BNs). Based on a previous universal Minimum Description Length (uMDL) network inference algorithm, we study whether adding the prior information based on prescribed attractors or steady states can help better reconstruct the underlying gene regulatory relationships. Comparing the network inference performance with and without prescribed steady states, the experiments based on randomly generated networks as well as a metastatic melanoma network have shown that the phenotypically constrained inference obtains improved performance when we have small numbers of state transition observations.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129516967","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735937
Antti Larjo, H. Lähdesmäki
Active learning methods aim at identifying measurements that should be done in order to benefit a learning problem maximally. We use Bayesian networks as models of biological systems and show how active learning can be used to select new measurements to be incorporated via structure priors. Improved performance of the methods is demonstrated with both simulated and real datasets.
{"title":"Active learning for Bayesian network models of biological networks using structure priors","authors":"Antti Larjo, H. Lähdesmäki","doi":"10.1109/GENSIPS.2013.6735937","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735937","url":null,"abstract":"Active learning methods aim at identifying measurements that should be done in order to benefit a learning problem maximally. We use Bayesian networks as models of biological systems and show how active learning can be used to select new measurements to be incorporated via structure priors. Improved performance of the methods is demonstrated with both simulated and real datasets.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127878323","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735921
Mario Flores, Yufei Huang, Yidong Chen
By using the competing endogenous RNA (ceRNA) concept, we implemented a web-based application TraceRNA. TraceRNA allows us to interactively construct a regulation network for a specific phenotype by using a disease-specific transcriptome data. In this work, we further extend the TraceRNA with a novel algorithm implementation where we examined the microRNA expression derived from same disease type. The proposed algorithm, NetceRNA, finds an optimized network representation under a certain phenotype context by iteratively perturbing the network and measuring the network configuration change with respect to the original ceRNA network. The resulting algorithm outputs an improved network together with a ranked list of genes and miRNAs which are characteristic of the specific phenotype. To illustrate the utility of NetceRNA, gene expression and microRNA expression data of breast cancer study from The Cancer Genome Atlas (TCGA) were used.
{"title":"NetceRNA: An algorithm for construction of phenotype-specific regulation networks via competing endogenous RNAs","authors":"Mario Flores, Yufei Huang, Yidong Chen","doi":"10.1109/GENSIPS.2013.6735921","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735921","url":null,"abstract":"By using the competing endogenous RNA (ceRNA) concept, we implemented a web-based application TraceRNA. TraceRNA allows us to interactively construct a regulation network for a specific phenotype by using a disease-specific transcriptome data. In this work, we further extend the TraceRNA with a novel algorithm implementation where we examined the microRNA expression derived from same disease type. The proposed algorithm, NetceRNA, finds an optimized network representation under a certain phenotype context by iteratively perturbing the network and measuring the network configuration change with respect to the original ceRNA network. The resulting algorithm outputs an improved network together with a ranked list of genes and miRNAs which are characteristic of the specific phenotype. To illustrate the utility of NetceRNA, gene expression and microRNA expression data of breast cancer study from The Cancer Genome Atlas (TCGA) were used.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122647883","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735934
Lori A. Dalton
Although it is typically accepted that cluster analysis is a subjective activity, without an objective framework it is impossible to understand, let alone guarantee, the predictive capacity of clustering. To address this, recent work utilizes random point process theory to develop a probabilistic theory of clustering. The theory fully parallels Bayes decision theory for classification: given a known underlying processes and specified cost function there exist Bayes clustering operators with minimum expected error. Clustering is hence transformed from a subjective activity to an objective operation. In this work, we present conditions under which the optimization function utilized in classical k-means clustering is optimal in the new Bayes clustering theory, and thus begin to understand this algorithm objectively.
{"title":"On the optimality of k-means clustering","authors":"Lori A. Dalton","doi":"10.1109/GENSIPS.2013.6735934","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735934","url":null,"abstract":"Although it is typically accepted that cluster analysis is a subjective activity, without an objective framework it is impossible to understand, let alone guarantee, the predictive capacity of clustering. To address this, recent work utilizes random point process theory to develop a probabilistic theory of clustering. The theory fully parallels Bayes decision theory for classification: given a known underlying processes and specified cost function there exist Bayes clustering operators with minimum expected error. Clustering is hence transformed from a subjective activity to an objective operation. In this work, we present conditions under which the optimization function utilized in classical k-means clustering is optimal in the new Bayes clustering theory, and thus begin to understand this algorithm objectively.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861549","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735943
Lori A. Dalton
A Bayesian modeling framework over an uncertainty class of underlying distributions has been used to derive an optimal MMSE error estimator for arbitrary classifiers and an optimal Bayesian classification rule that minimizes expected error, both relative to the overall misclassification rate. In this work, we use the same Bayesian framework to formulate a Neyman-Pearson based approach that optimizes relative to true and false positive rates. True and false positive rates are often of more practical use than the misclassification rate in medical applications, meanwhile the Neyman-Pearson theory does not require modeling or knowledge of the prior class probabilities.
{"title":"Optimal neyman-pearson classification under Bayesian uncertainty models","authors":"Lori A. Dalton","doi":"10.1109/GENSIPS.2013.6735943","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735943","url":null,"abstract":"A Bayesian modeling framework over an uncertainty class of underlying distributions has been used to derive an optimal MMSE error estimator for arbitrary classifiers and an optimal Bayesian classification rule that minimizes expected error, both relative to the overall misclassification rate. In this work, we use the same Bayesian framework to formulate a Neyman-Pearson based approach that optimizes relative to true and false positive rates. True and false positive rates are often of more practical use than the misclassification rate in medical applications, meanwhile the Neyman-Pearson theory does not require modeling or knowledge of the prior class probabilities.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130267395","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735929
Qian Wan, R. Pal
Drug sensitivity prediction based on genomic characterization remains a significant challenge in the area of systems medicine. Multiple approaches have been proposed for mapping genomic characterization to drug sensitivity and among them ensemble based learning techniques like Random Forests (RF) have been a top performer [1, 2]. The majority of the current approaches infer a predictive model for each drug individually but correlation between different drug sensitivities suggests that multiple response prediction incorporating the co-variance of the different drug responses can possibly improve prediction accuracy. In this abstract, we report a prediction and analysis framework based on Multivariate Random Forests (MRF) that incorporates the correlation between different drug sensitivities.
{"title":"A multivariate random forest based framework for drug sensitivity prediction","authors":"Qian Wan, R. Pal","doi":"10.1109/GENSIPS.2013.6735929","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735929","url":null,"abstract":"Drug sensitivity prediction based on genomic characterization remains a significant challenge in the area of systems medicine. Multiple approaches have been proposed for mapping genomic characterization to drug sensitivity and among them ensemble based learning techniques like Random Forests (RF) have been a top performer [1, 2]. The majority of the current approaches infer a predictive model for each drug individually but correlation between different drug sensitivities suggests that multiple response prediction incorporating the co-variance of the different drug responses can possibly improve prediction accuracy. In this abstract, we report a prediction and analysis framework based on Multivariate Random Forests (MRF) that incorporates the correlation between different drug sensitivities.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127172587","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735917
N. Banerjee, S. Chothani, L. Harris, N. Dimitrova
Long non-coding RNAs (lncRNAs) are suspected to have a wide range of roles in cellular functions. The precise transcriptional mechanisms and the interactions with coding RNAs (genes) are yet to be elucidated. In this paper we present a novel methodology that explores interactions between coding genes and lncRNAs and constructs gene-lncRNA co-expression networks, taking into account their unique expression characteristics. We evaluated several similarity measures to associate a gene and a lncRNA from RNA sequencing data of breast cancer patients and determined correlation to be the metric appropriately suited to this kind of data. Based on an empirically determined threshold, we selected a number of pairs to construct co-expression networks and identified sub-networks that capture previously-unknown lncRNA partners of key players in breast cancer like estrogen receptor. In essence, we have developed a data-driven approach to identify important, functional, coding-lncRNA interactions that sets the stage for more in-depth analyses capturing how non-coding interactions influence expression of protein coding genes and modulate pathways contributing to cancer.
{"title":"Identifying RNAseq-based coding-noncoding co-expression interactions in breast cancer","authors":"N. Banerjee, S. Chothani, L. Harris, N. Dimitrova","doi":"10.1109/GENSIPS.2013.6735917","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735917","url":null,"abstract":"Long non-coding RNAs (lncRNAs) are suspected to have a wide range of roles in cellular functions. The precise transcriptional mechanisms and the interactions with coding RNAs (genes) are yet to be elucidated. In this paper we present a novel methodology that explores interactions between coding genes and lncRNAs and constructs gene-lncRNA co-expression networks, taking into account their unique expression characteristics. We evaluated several similarity measures to associate a gene and a lncRNA from RNA sequencing data of breast cancer patients and determined correlation to be the metric appropriately suited to this kind of data. Based on an empirically determined threshold, we selected a number of pairs to construct co-expression networks and identified sub-networks that capture previously-unknown lncRNA partners of key players in breast cancer like estrogen receptor. In essence, we have developed a data-driven approach to identify important, functional, coding-lncRNA interactions that sets the stage for more in-depth analyses capturing how non-coding interactions influence expression of protein coding genes and modulate pathways contributing to cancer.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133567894","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735947
A. Zollanvari, U. Braga-Neto, E. Dougherty
Cross-validation is commonly used to estimate the overall error rate of a designed classifier in a small-sample expression study. The true error of the classifier is a function of the prior probabilities of the classes. With random sampling these can be estimated consistently in terms of the class sample sizes, but when sampling is separate, meaning these sample sizes are determined prior to sampling, there are no reasonable estimates from the data and the prior probabilities must be “estimated” outside the experiment. We have conducted a set of simulations to study the bias of cross-validation as a function of these “estimates”. The results show that a poor choice for estimating these probabilities can significantly increase the bias of cross-validation as an estimator of the true error.
{"title":"Effect of mixing probabilities on the bias of cross-validation under separate sampling","authors":"A. Zollanvari, U. Braga-Neto, E. Dougherty","doi":"10.1109/GENSIPS.2013.6735947","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735947","url":null,"abstract":"Cross-validation is commonly used to estimate the overall error rate of a designed classifier in a small-sample expression study. The true error of the classifier is a function of the prior probabilities of the classes. With random sampling these can be estimated consistently in terms of the class sample sizes, but when sampling is separate, meaning these sample sizes are determined prior to sampling, there are no reasonable estimates from the data and the prior probabilities must be “estimated” outside the experiment. We have conducted a set of simulations to study the bias of cross-validation as a function of these “estimates”. The results show that a poor choice for estimating these probabilities can significantly increase the bias of cross-validation as an estimator of the true error.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128372571","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 : 2013-11-01DOI: 10.1109/GENSIPS.2013.6735939
Rajani Varghese, Sriram Sridharan, A. Datta, Jijayanagaram Venkatraj
Summary form only given. Hypoxic stress is a consequence of the decrease in oxygen reaching the tissues of the body. Oxygen is essential for energy production, since it is the terminal electron acceptor in the Electron Transport Chain (ETC) of the mitochondria. This makes the condition of low oxygen availability (hypoxia) deleterious to living cells and proper adaptation techniques must be employed by the cell for survival. The main sensors for cellular partial pressure alterations and hypoxia are three hydroxylases, known as prolyl hydroxylase domain containing proteins (PHDs) namely PHD1, PHD2 and PHD3. They initiate a cascade of cell signaling through a family of transcription factors appropriately named as Hypoxia Inducible factor (HIF). There are currently three members HIF-1, HIF-2 and HIF-3, each of them is an HIF heterodimer, possessing α and β factor subunits coded by 6 genes (HIF1α, ARNT (Aryl Hydrocarbon Nuclear Translocator), EPAS1 (Endothelial PAS domain containing protein 1), ARNT2, HIF3α and ARNT3 respectively). When enough oxygen is available, the proline residue in the Oxygen Dependent Degradation (ODD) domain of HIF-1α undergoes non-reversible hydroxylation in the presence of PHD2. During normoxia, HIF-1α is hydroxylated by PHD2, and the hydroxylated HIF-1α interacts with von Hippel-Lindua tumor suppressor protein (VHL) and is degraded by ubiquitination. But during hypoxia, PHD2 is inhibited which results in HIF-1α stabilization. Stabilized HIF-1α enters the nucleus and heterodimerizes with HIF-1β and binds the DNA via the Hypoxia Response Elements (HRE) within the promoter regions of the target genes. HIF-regulated target genes enable cells to induce an adaptive response by increasing glycolysis, angiogenesis and other patho-physiological events or undergo cell death by promoting apoptosis or necrosis. The decision of adaptation or cell death depends on the extent of hypoxic stress faced by the cells. The adaptive response during hypoxic stress is mainly observed in solid tumors, where the increased demand for oxygen is met by the up-regulation of genes involved in angiogenesis, vasculogenesis, glycolysis and other physiological events. Hence, proper understanding of hypoxia stress response pathway is critical for understanding the mechanism of tumor cell adaptation to hypoxia and to develop efficient therapeutic interventions. Using prior knowledge of hypoxia stress response pathways from the literature, a Boolean model of it is developed and simulated. This model allows for a better understanding of the perturbations of hypoxia response, which is derived from complex multivariate interactions of biological molecules.
只提供摘要形式。低氧应激是到达身体组织的氧气减少的结果。氧是线粒体电子传递链(ETC)的终端电子受体，对能量产生至关重要。这使得低氧条件(缺氧)对活细胞有害，细胞必须采用适当的适应技术才能生存。细胞分压改变和缺氧的主要传感器是三种羟化酶，称为脯氨酸羟化酶结构域蛋白(PHDs)，即PHD1, PHD2和PHD3。它们通过一系列转录因子启动一系列细胞信号传导，这些转录因子被恰当地命名为缺氧诱导因子(HIF)。目前有HIF-1、HIF-2和HIF-3三个成员，它们都是HIF异源二聚体，具有由6个基因编码的α和β因子亚基(HIF1α、ARNT (Aryl Hydrocarbon Nuclear Translocator)、EPAS1 (Endothelial PAS domain containing protein 1)、ARNT2、HIF3α和ARNT3)。当有足够的氧气可用时，HIF-1α氧依赖降解(ODD)区域中的脯氨酸残基在PHD2的存在下发生不可逆的羟基化。在正常缺氧状态下，HIF-1α被PHD2羟基化，羟基化的HIF-1α与von Hippel-Lindua肿瘤抑制蛋白(VHL)相互作用，并被泛素化降解。但在缺氧时，PHD2被抑制，导致HIF-1α稳定。稳定的HIF-1α进入细胞核，与HIF-1β异源二聚，并通过靶基因启动子区域的缺氧反应元件(HRE)与DNA结合。hif调控的靶基因使细胞通过增加糖酵解、血管生成和其他病理生理事件诱导适应性反应，或通过促进细胞凋亡或坏死导致细胞死亡。细胞适应或死亡的决定取决于细胞所面临的缺氧胁迫的程度。低氧胁迫下的适应性反应主要在实体肿瘤中观察到，对氧的需求增加是通过参与血管生成、血管生成、糖酵解等生理事件的基因上调来满足的。因此，正确认识低氧应激反应途径对于理解肿瘤细胞适应低氧的机制和制定有效的治疗干预措施至关重要。利用文献中关于缺氧应激反应途径的先验知识，建立了一个布尔模型并进行了模拟。该模型可以更好地理解缺氧反应的扰动，这是源于生物分子复杂的多元相互作用。
{"title":"Modeling hypoxia stress response pathways","authors":"Rajani Varghese, Sriram Sridharan, A. Datta, Jijayanagaram Venkatraj","doi":"10.1109/GENSIPS.2013.6735939","DOIUrl":"https://doi.org/10.1109/GENSIPS.2013.6735939","url":null,"abstract":"Summary form only given. Hypoxic stress is a consequence of the decrease in oxygen reaching the tissues of the body. Oxygen is essential for energy production, since it is the terminal electron acceptor in the Electron Transport Chain (ETC) of the mitochondria. This makes the condition of low oxygen availability (hypoxia) deleterious to living cells and proper adaptation techniques must be employed by the cell for survival. The main sensors for cellular partial pressure alterations and hypoxia are three hydroxylases, known as prolyl hydroxylase domain containing proteins (PHDs) namely PHD1, PHD2 and PHD3. They initiate a cascade of cell signaling through a family of transcription factors appropriately named as Hypoxia Inducible factor (HIF). There are currently three members HIF-1, HIF-2 and HIF-3, each of them is an HIF heterodimer, possessing α and β factor subunits coded by 6 genes (HIF1α, ARNT (Aryl Hydrocarbon Nuclear Translocator), EPAS1 (Endothelial PAS domain containing protein 1), ARNT2, HIF3α and ARNT3 respectively). When enough oxygen is available, the proline residue in the Oxygen Dependent Degradation (ODD) domain of HIF-1α undergoes non-reversible hydroxylation in the presence of PHD2. During normoxia, HIF-1α is hydroxylated by PHD2, and the hydroxylated HIF-1α interacts with von Hippel-Lindua tumor suppressor protein (VHL) and is degraded by ubiquitination. But during hypoxia, PHD2 is inhibited which results in HIF-1α stabilization. Stabilized HIF-1α enters the nucleus and heterodimerizes with HIF-1β and binds the DNA via the Hypoxia Response Elements (HRE) within the promoter regions of the target genes. HIF-regulated target genes enable cells to induce an adaptive response by increasing glycolysis, angiogenesis and other patho-physiological events or undergo cell death by promoting apoptosis or necrosis. The decision of adaptation or cell death depends on the extent of hypoxic stress faced by the cells. The adaptive response during hypoxic stress is mainly observed in solid tumors, where the increased demand for oxygen is met by the up-regulation of genes involved in angiogenesis, vasculogenesis, glycolysis and other physiological events. Hence, proper understanding of hypoxia stress response pathway is critical for understanding the mechanism of tumor cell adaptation to hypoxia and to develop efficient therapeutic interventions. Using prior knowledge of hypoxia stress response pathways from the literature, a Boolean model of it is developed and simulated. This model allows for a better understanding of the perturbations of hypoxia response, which is derived from complex multivariate interactions of biological molecules.","PeriodicalId":336511,"journal":{"name":"2013 IEEE International Workshop on Genomic Signal Processing and Statistics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127647389","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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