The Experts below are selected from a list of 39957 Experts worldwide ranked by ideXlab platform
Mario Giacobini - One of the best experts on this subject based on the ideXlab platform.
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additive Functions in boolean models of gene regulatory network modules
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop Update Functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel Update Functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random Update Functions by a novel threshold-based dynamic Function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean Update Functions to validate the proposed Update Function. Results of this validation hint to increased biological plausibility of the threshold-based Function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The Update Function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
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validating a threshold based boolean model of regulatory networks on a biological organism
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean Update Functions attached to each element, to validate a previously proposed threshold-based additive Update Function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean Function to match this regime. Results show that there is a higher degree of overlap between the original Function and the additive Function than with random Update Function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.
Christian Darabos - One of the best experts on this subject based on the ideXlab platform.
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additive Functions in boolean models of gene regulatory network modules
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop Update Functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel Update Functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random Update Functions by a novel threshold-based dynamic Function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean Update Functions to validate the proposed Update Function. Results of this validation hint to increased biological plausibility of the threshold-based Function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The Update Function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
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validating a threshold based boolean model of regulatory networks on a biological organism
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean Update Functions attached to each element, to validate a previously proposed threshold-based additive Update Function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean Function to match this regime. Results show that there is a higher degree of overlap between the original Function and the additive Function than with random Update Function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.
Crutchfield, James P - One of the best experts on this subject based on the ideXlab platform.
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Spacetime Symmetries, Invariant Sets, and Additive Subdynamics of Cellular Automata
2020Co-Authors: Rupe Adam, Crutchfield, James PAbstract:Cellular automata are fully-discrete, spatially-extended dynamical systems that evolve by simultaneously applying a local Update Function. Despite their simplicity, the induced global dynamic produces a stunning array of richly-structured, complex behaviors. These behaviors present a challenge to traditional closed-form analytic methods. In certain cases, specifically when the local Update is additive, powerful techniques may be brought to bear, including characteristic polynomials, the ergodic theorem with Fourier analysis, and endomorphisms of compact Abelian groups. For general dynamics, though, where such analytics generically do not apply, behavior-driven analysis shows great promise in directly monitoring the emergence of structure and complexity in cellular automata. Here we detail a surprising connection between generalized symmetries in the spacetime fields of configuration orbits as revealed by the behavior-driven local causal states, invariant sets of spatial configurations, and additive subdynamics which allow for closed-form analytic methods
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Spacetime Symmetries, Invariant Sets, and Additive Subdynamics of Cellular Automata
2018Co-Authors: Rupe Adam, Crutchfield, James PAbstract:Cellular automata are fully-discrete, spatially-extended dynamical systems that evolve by simultaneously applying a local Update Function. Despite their simplicity, the induced global dynamic produces a stunning array of richly-structured, complex behaviors. These behaviors present a challenge to traditional closed-form analytic methods. In certain cases, specifically when the local Update is additive, powerful techniques may be brought to bear, including characteristic polynomials, the ergodic theorem with Fourier analysis, and endomorphisms of compact Abelian groups. For general dynamics, though, where such analytics generically do not apply, behavior-driven analysis shows great promise in directly monitoring the emergence of structure and complexity in cellular automata. Here we detail a surprising connection between generalized symmetries in the spacetime fields of configuration orbits as revealed by the behavior-driven local causal states, invariant sets of spatial configurations, and additive subdynamics which allow for closed-form analytic methods.Comment: 24 pages, 9 figures, 5 tables; http://csc.ucdavis.edu/~cmg/compmech/pubs/ssisad.ht
Paolo Provero - One of the best experts on this subject based on the ideXlab platform.
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additive Functions in boolean models of gene regulatory network modules
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop Update Functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel Update Functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random Update Functions by a novel threshold-based dynamic Function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean Update Functions to validate the proposed Update Function. Results of this validation hint to increased biological plausibility of the threshold-based Function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The Update Function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
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validating a threshold based boolean model of regulatory networks on a biological organism
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean Update Functions attached to each element, to validate a previously proposed threshold-based additive Update Function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean Function to match this regime. Results show that there is a higher degree of overlap between the original Function and the additive Function than with random Update Function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.
Jason H Moore - One of the best experts on this subject based on the ideXlab platform.
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additive Functions in boolean models of gene regulatory network modules
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop Update Functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel Update Functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random Update Functions by a novel threshold-based dynamic Function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean Update Functions to validate the proposed Update Function. Results of this validation hint to increased biological plausibility of the threshold-based Function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The Update Function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
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validating a threshold based boolean model of regulatory networks on a biological organism
2011Co-Authors: Christian Darabos, Ferdinando Di Cunto, Marco Tomassini, Jason H Moore, Paolo Provero, Mario GiacobiniAbstract:Boolean models of regulatory networks are very attractive due to their simplicity and flexibility to integrate new development. We use the signaling network of a plant, along with the Boolean Update Functions attached to each element, to validate a previously proposed threshold-based additive Update Function. To do that, we determine the dynamical regime of the original system, then setup the parameters of the Boolean Function to match this regime. Results show that there is a higher degree of overlap between the original Function and the additive Function than with random Update Function in the specific case at hand. This confirm a previous conjecture that the contribution of different transcription factors to the regulation of a target gene treated additively can explain a significant part of the variation in gene expression.
