The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform
Edda Klipp - One of the best experts on this subject based on the ideXlab platform.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC systems biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC Systems Biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner. Results Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the Reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
Tomoya Mori - One of the best experts on this subject based on the ideXlab platform.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC systems biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC Systems Biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner. Results Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the Reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
Marcus Krantz - One of the best experts on this subject based on the ideXlab platform.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC systems biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC Systems Biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner. Results Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the Reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
Tatsuya Akutsu - One of the best experts on this subject based on the ideXlab platform.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC systems biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC Systems Biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner. Results Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the Reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
Max Flöttmann - One of the best experts on this subject based on the ideXlab platform.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC systems biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner.
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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks
BMC Systems Biology, 2015Co-Authors: Tomoya Mori, Max Flöttmann, Marcus Krantz, Tatsuya Akutsu, Edda KlippAbstract:Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical Details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular Detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical Reaction Detail in a quantitative or semi quantitative manner. Results Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the Reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
