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Alan Velizcuba - One of the best experts on this subject based on the ideXlab platform.
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identification of control targets in boolean Molecular Network models via computational algebra
BMC Systems Biology, 2016Co-Authors: David Murrugarra, Alan Velizcuba, Boris Aguilar, Reinhard LaubenbacherAbstract:Background Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the Molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean Networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system.
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identification of control targets in boolean Molecular Network models via computational algebra
arXiv: Molecular Networks, 2015Co-Authors: David Murrugarra, Alan Velizcuba, Boris Aguilar, Reinhard LaubenbacherAbstract:Motivation: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the Molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean Networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. Experimentally, node manipulation requires technology to completely repress or fully activate a particular gene product while edge manipulations only require a drug that inactivates the interaction between two gene products. Results: This paper presents a method for the identification of potential intervention targets in Boolean Molecular Network models using algebraic techniques. The approach exploits an algebraic representation of Boolean Networks to encode the control candidates in the Network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 Network and a blood T cell lymphocyte granular leukemia survival signaling Network.
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steady state analysis of boolean Molecular Network models via model reduction and computational algebra
BMC Bioinformatics, 2014Co-Authors: Alan Velizcuba, Boris Aguilar, Franziska Hinkelmann, Reinhard LaubenbacherAbstract:A key problem in the analysis of mathematical models of Molecular Networks is the determination of their steady states. The present paper addresses this problem for Boolean Network models, an increasingly popular modeling paradigm for Networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the Network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large Networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large Networks is still unsolved in general. This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the Network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean Network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean Network. The code for the algorithm, as well as the test suite of benchmark Networks, is available upon request from the corresponding author. The algorithm presented in this paper reliably determines all steady states of sparse Boolean Networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for large Boolean Networks with high average connectivity remains an open problem.
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steady state analysis of boolean Molecular Network models via model reduction and computational algebra
BMC Bioinformatics, 2014Co-Authors: Alan Velizcuba, Boris Aguilar, Franziska Hinkelmann, Reinhard LaubenbacherAbstract:Background A key problem in the analysis of mathematical models of Molecular Networks is the determination of their steady states. The present paper addresses this problem for Boolean Network models, an increasingly popular modeling paradigm for Networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the Network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large Networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large Networks is still unsolved in general.
Reinhard Laubenbacher - One of the best experts on this subject based on the ideXlab platform.
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identification of control targets in boolean Molecular Network models via computational algebra
BMC Systems Biology, 2016Co-Authors: David Murrugarra, Alan Velizcuba, Boris Aguilar, Reinhard LaubenbacherAbstract:Background Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the Molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean Networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system.
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identification of control targets in boolean Molecular Network models via computational algebra
arXiv: Molecular Networks, 2015Co-Authors: David Murrugarra, Alan Velizcuba, Boris Aguilar, Reinhard LaubenbacherAbstract:Motivation: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the Molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean Networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. Experimentally, node manipulation requires technology to completely repress or fully activate a particular gene product while edge manipulations only require a drug that inactivates the interaction between two gene products. Results: This paper presents a method for the identification of potential intervention targets in Boolean Molecular Network models using algebraic techniques. The approach exploits an algebraic representation of Boolean Networks to encode the control candidates in the Network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 Network and a blood T cell lymphocyte granular leukemia survival signaling Network.
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steady state analysis of boolean Molecular Network models via model reduction and computational algebra
BMC Bioinformatics, 2014Co-Authors: Alan Velizcuba, Boris Aguilar, Franziska Hinkelmann, Reinhard LaubenbacherAbstract:A key problem in the analysis of mathematical models of Molecular Networks is the determination of their steady states. The present paper addresses this problem for Boolean Network models, an increasingly popular modeling paradigm for Networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the Network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large Networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large Networks is still unsolved in general. This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the Network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean Network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean Network. The code for the algorithm, as well as the test suite of benchmark Networks, is available upon request from the corresponding author. The algorithm presented in this paper reliably determines all steady states of sparse Boolean Networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for large Boolean Networks with high average connectivity remains an open problem.
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steady state analysis of boolean Molecular Network models via model reduction and computational algebra
BMC Bioinformatics, 2014Co-Authors: Alan Velizcuba, Boris Aguilar, Franziska Hinkelmann, Reinhard LaubenbacherAbstract:Background A key problem in the analysis of mathematical models of Molecular Networks is the determination of their steady states. The present paper addresses this problem for Boolean Network models, an increasingly popular modeling paradigm for Networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the Network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large Networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large Networks is still unsolved in general.
Boris Aguilar - One of the best experts on this subject based on the ideXlab platform.
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identification of control targets in boolean Molecular Network models via computational algebra
BMC Systems Biology, 2016Co-Authors: David Murrugarra, Alan Velizcuba, Boris Aguilar, Reinhard LaubenbacherAbstract:Background Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the Molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean Networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system.
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identification of control targets in boolean Molecular Network models via computational algebra
arXiv: Molecular Networks, 2015Co-Authors: David Murrugarra, Alan Velizcuba, Boris Aguilar, Reinhard LaubenbacherAbstract:Motivation: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the Molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean Networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. Experimentally, node manipulation requires technology to completely repress or fully activate a particular gene product while edge manipulations only require a drug that inactivates the interaction between two gene products. Results: This paper presents a method for the identification of potential intervention targets in Boolean Molecular Network models using algebraic techniques. The approach exploits an algebraic representation of Boolean Networks to encode the control candidates in the Network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 Network and a blood T cell lymphocyte granular leukemia survival signaling Network.
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steady state analysis of boolean Molecular Network models via model reduction and computational algebra
BMC Bioinformatics, 2014Co-Authors: Alan Velizcuba, Boris Aguilar, Franziska Hinkelmann, Reinhard LaubenbacherAbstract:A key problem in the analysis of mathematical models of Molecular Networks is the determination of their steady states. The present paper addresses this problem for Boolean Network models, an increasingly popular modeling paradigm for Networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the Network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large Networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large Networks is still unsolved in general. This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the Network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean Network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean Network. The code for the algorithm, as well as the test suite of benchmark Networks, is available upon request from the corresponding author. The algorithm presented in this paper reliably determines all steady states of sparse Boolean Networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for large Boolean Networks with high average connectivity remains an open problem.
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steady state analysis of boolean Molecular Network models via model reduction and computational algebra
BMC Bioinformatics, 2014Co-Authors: Alan Velizcuba, Boris Aguilar, Franziska Hinkelmann, Reinhard LaubenbacherAbstract:Background A key problem in the analysis of mathematical models of Molecular Networks is the determination of their steady states. The present paper addresses this problem for Boolean Network models, an increasingly popular modeling paradigm for Networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the Network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large Networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large Networks is still unsolved in general.
Bradley A Maron - One of the best experts on this subject based on the ideXlab platform.
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abstract 12150 Molecular Network analysis identifies nedd9 as a critical node regulating vascular fibrosis in pulmonary arterial hypertension
Circulation, 2016Co-Authors: Thomas E Stephens, Ruisheng Wang, Andriy O Samokhin, Elena Arons, Sara O Vargas, Joseph Loscalzo, Jane A Leopold, Bradley A MaronAbstract:Vascular fibrosis in pulmonary arterial hypertension (PAH) is associated with increased morbidity. In PAH, elevated levels of aldosterone (ALDO) increase fibrillar collagen in pulmonary artery endothelial cells (PAECs). However, ALDO also promotes adaptive fibrosis in skin wound healing, suggesting that cell type-specific pathways regulated by ALDO distinguish fibrosis subtypes. To explore this concept further, published mRNA array data were used to model an interactome of fibrosis-related genes. Betweeness centrality analysis identified the Crk-associated protein NEDD9 as a critical ALDO-regulated node distinguishing vascular from dermal fibrosis (P=7.5x10 -7 vs. random Network). To validate this observation, PAECs and dermal microvascular ECs were treated with ALDO (10 -7 mol/L) or vehicle control (V) for 24 hr and NEDD9 levels were analyzed by immunoblot. Compared to V-treated cells, ALDO increased NEDD9 expression in PAECs (46 ± 12 vs. 90 ± 7 a.u., P in vitro . Compared to V-treated cells, ALDO increased collagen III expression (117 ± 16 vs. 225 ± 34 a.u., P
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abstract 12150 Molecular Network analysis identifies nedd9 as a critical node regulating vascular fibrosis in pulmonary arterial hypertension
Circulation, 2016Co-Authors: Thomas E Stephens, Ruisheng Wang, Andriy O Samokhin, Elena Arons, Sara O Vargas, Joseph Loscalzo, Jane A Leopold, Minwei Cao, Bradley A MaronAbstract:Vascular fibrosis in pulmonary arterial hypertension (PAH) is associated with increased morbidity. In PAH, elevated levels of aldosterone (ALDO) increase fibrillar collagen in pulmonary artery endo...
Rodrigo A Gutierrez - One of the best experts on this subject based on the ideXlab platform.
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qualitative Network models and genome wide expression data define carbon nitrogen responsive Molecular machines in arabidopsis
Genome Biology, 2007Co-Authors: Rodrigo A Gutierrez, Laurence V Lejay, Alexis Dean, Francesca Chiaromonte, Dennis Shasha, Gloria M CoruzziAbstract:Background: Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multiNetwork analysis of microarray data to identify Molecular Networks regulated by C and N in the Arabidopsis root system. Results: We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell Molecular Network is adjusted in response to the CN treatments, we constructed a qualitative multiNetwork model of the Arabidopsis metabolic and regulatory Molecular Network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multiNetwork, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. Conclusion: Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The Network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of Molecular machines in the plant cell.
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qualitative Network models and genome wide expression data define carbon nitrogen responsive Molecular machines in arabidopsis
Genome Biology, 2007Co-Authors: Rodrigo A Gutierrez, Laurence V Lejay, Alexis Dean, Francesca Chiaromonte, Dennis Shasha, Gloria M CoruzziAbstract:Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multiNetwork analysis of microarray data to identify Molecular Networks regulated by C and N in the Arabidopsis root system. We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell Molecular Network is adjusted in response to the CN treatments, we constructed a qualitative multiNetwork model of the Arabidopsis metabolic and regulatory Molecular Network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multiNetwork, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The Network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of Molecular machines in the plant cell.
