The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform
Edward R Dougherty - One of the best experts on this subject based on the ideXlab platform.
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learning restricted Boolean Network Model by time series data
Eurasip Journal on Bioinformatics and Systems Biology, 2014Co-Authors: Hongjia Ouyang, Jie Fang, Liangzhong Shen, Edward R DoughertyAbstract:Restricted Boolean Networks are simplified Boolean Networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean Network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean Networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic Networks and a well-studied budding yeast cell cycle Network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance μ e , the normalized Hamming distance of state transition μ st , and the steady-state distribution distance μ ssd . Results show that the proposed algorithm outperforms the others according to both μ e and μ st , whereas its performance according to μ ssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate
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learning restricted Boolean Network Model by time series data
Eurasip Journal on Bioinformatics and Systems Biology, 2014Co-Authors: Hongjia Ouyang, Jie Fang, Liangzhong Shen, Edward R Dougherty, Wenbin LiuAbstract:Restricted Boolean Networks are simplified Boolean Networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean Network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean Networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic Networks and a well-studied budding yeast cell cycle Network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance , the normalized Hamming distance of state transition , and the steady-state distribution distance μssd. Results show that the proposed algorithm outperforms the others according to both and , whereas its performance according to μssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.
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GENSiPS - A comparative study on sensitivities of Boolean Networks
2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), 2010Co-Authors: Xiaoning Qian, Edward R DoughertyAbstract:Sensitivity analysis is a critical yet challenging problem for understanding complex systems. In genomic signal processing, it has been recognized that many biological systems are asymptotically stable. The sensitivity regarding the structural and dynamical uncertainty of Network Models may provide a deep understanding of the robustness, adaptability, and controllability of biological processes. We focus on the Boolean Network Model, as it has been shown to be able to capture the switching behavior of many biological processes by appropriate Modeling of multivariate nonlinear relationships among genes. We study two different sensitivity measures for the Boolean Network Model, one directly related to individual predictor Boolean functions and the other to long-term Network dynamics. Although there is some correlation between the measures, our study shows that these different sensitivities characterize different aspects of Network behavior, so that their application depends on how they relate to specific translational goals.
Jijayanagaram Venkatraj - One of the best experts on this subject based on the ideXlab platform.
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Boolean Modeling and fault diagnosis in oxidative stress response
BMC Genomics, 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean Network Model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI 3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean Network Model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. In this paper, we have developed a Boolean Network Model for the oxidative stress response. This Model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the Model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI 3k/Akt pathway. It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological Network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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Boolean Modeling and fault diagnosis in oxidative stress response
BMC Genomics, 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean Network Model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI 3- Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean Network Model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean Network Model for the oxidative stress response. This Model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the Model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI 3 k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological Network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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Boolean Network Model of oxidative stress response pathways
2012 American Control Conference (ACC), 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network Model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.
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ACC - Boolean Network Model of oxidative stress response pathways
2012 American Control Conference (ACC), 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network Model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.
Wenbin Liu - One of the best experts on this subject based on the ideXlab platform.
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learning restricted Boolean Network Model by time series data
Eurasip Journal on Bioinformatics and Systems Biology, 2014Co-Authors: Hongjia Ouyang, Jie Fang, Liangzhong Shen, Edward R Dougherty, Wenbin LiuAbstract:Restricted Boolean Networks are simplified Boolean Networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean Network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean Networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic Networks and a well-studied budding yeast cell cycle Network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance , the normalized Hamming distance of state transition , and the steady-state distribution distance μssd. Results show that the proposed algorithm outperforms the others according to both and , whereas its performance according to μssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.
Ritwik Layek - One of the best experts on this subject based on the ideXlab platform.
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ACC - A linear approach to fault analysis in Boolean Networks
2017 American Control Conference (ACC), 2017Co-Authors: Anuj Deshpande, Ritwik LayekAbstract:Diagnostics and therapeutic interventions in complex systemic diseases like cancer can be mapped to fault identification and control problem. A complete linear framework has been developed in this manuscript to comprehend different classes of faults and controllability of the output for a class of homeostatic inputs in the Boolean Network framework. The problems undertaken in this manuscript are non-trivial. Interesting results have been derived for fault identification and control in the linearised Boolean Network Model of a proliferating cellular system.
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learning a probabilistic Boolean Network Model from biological pathways and time series expression data
International Conference of the IEEE Engineering in Medicine and Biology Society, 2016Co-Authors: Vardaan Pahuja, Ritwik Layek, Pabitra MitraAbstract:The problem of inferring a stochastic Model for gene regulatory Networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which Models the observed dynamics of genes with a high degree of precision. Our algorithm constructs a pathway tree and uses the time-series expression data to select an optimal level of tree, whose nodes are used to infer the PBN.
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Boolean Modeling and fault diagnosis in oxidative stress response
BMC Genomics, 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean Network Model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI 3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean Network Model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. In this paper, we have developed a Boolean Network Model for the oxidative stress response. This Model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the Model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI 3k/Akt pathway. It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological Network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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Boolean Modeling and fault diagnosis in oxidative stress response
BMC Genomics, 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean Network Model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI 3- Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean Network Model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean Network Model for the oxidative stress response. This Model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the Model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI 3 k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological Network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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Boolean Network Model of oxidative stress response pathways
2012 American Control Conference (ACC), 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network Model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.
Sriram Sridharan - One of the best experts on this subject based on the ideXlab platform.
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Boolean Modeling and fault diagnosis in oxidative stress response
BMC Genomics, 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean Network Model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI 3-Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean Network Model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. In this paper, we have developed a Boolean Network Model for the oxidative stress response. This Model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the Model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI 3k/Akt pathway. It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological Network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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Boolean Modeling and fault diagnosis in oxidative stress response
BMC Genomics, 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Background Oxidative stress is a consequence of normal and abnormal cellular metabolism and is linked to the development of human diseases. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage; conversely the failure of the oxidative stress response mechanism can induce aberrant cellular behavior leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signaling present in oxidative stress response pathways and determining possible signaling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, we developed a Boolean Network Model whose simulated behavior is consistent with earlier experimental observations from the literature. Concatenating the oxidative stress response pathways with the PI 3- Kinase-Akt pathway, the oxidative stress is linked to the phenotype of apoptosis, once again through a Boolean Network Model. Furthermore, we present an approach for pinpointing possible fault locations by using temporal variations in the oxidative stress input and observing the resulting deviations in the apoptotic signature from the normally predicted pathway. Such an approach could potentially form the basis for designing more effective combination therapies against complex diseases such as cancer. Results In this paper, we have developed a Boolean Network Model for the oxidative stress response. This Model was developed based on pathway information from the current literature pertaining to oxidative stress. Where applicable, the behaviour predicted by the Model is in agreement with experimental observations from the published literature. We have also linked the oxidative stress response to the phenomenon of apoptosis via the PI 3 k/Akt pathway. Conclusions It is our hope that some of the additional predictions here, such as those pertaining to the oscillatory behaviour of certain genes in the presence of oxidative stress, will be experimentally validated in the near future. Of course, it should be pointed out that the theoretical procedure presented here for pinpointing fault locations in a biological Network with feedback will need to be further simplified before it can be even considered for practical biological validation.
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Boolean Network Model of oxidative stress response pathways
2012 American Control Conference (ACC), 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network Model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.
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ACC - Boolean Network Model of oxidative stress response pathways
2012 American Control Conference (ACC), 2012Co-Authors: Sriram Sridharan, Ritwik Layek, Aniruddha Datta, Jijayanagaram VenkatrajAbstract:Oxidative stress is a consequence of normal cellular metabolism. The effective functioning of the pathway responding to oxidative stress protects the cellular DNA against oxidative damage and its failure can induce aberrant cellular behaviour leading to diseases such as neurodegenerative disorders and cancer. Thus, understanding the normal signalling present in oxidative stress response pathways and determining possible signalling alterations leading to disease could provide us with useful pointers for therapeutic purposes. Using knowledge of oxidative stress response pathways from the literature, a Boolean Network Model is developed whose simulated behaviour is consistent with earlier experimental observations from the literature.