The Experts below are selected from a list of 1797 Experts worldwide ranked by ideXlab platform
Stuart A Kauffman - One of the best experts on this subject based on the ideXlab platform.
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robustness analysis of a Boolean model of gene regulatory Network with memory
Journal of Computational Biology, 2011Co-Authors: Alex Graudenzi, Roberto Serra, Marco Villani, Annamaria Colacci, Stuart A KauffmanAbstract:Abstract The response to different kinds of perturbations of a discrete model of gene regulatory Network, which is a generalization of the Random Boolean Network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.
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why a simple model of genetic regulatory Networks describes the distribution of avalanches in gene expression data
Journal of Theoretical Biology, 2007Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Stuart A KauffmanAbstract:In a previous study it was shown that a simple Random Boolean Network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.
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coupled Random Boolean Network forming an artificial tissue
Cellular Automata for Research and Industry, 2006Co-Authors: Marco Villani, Roberto Serra, P Ingrami, Stuart A KauffmanAbstract:Random Boolean Networks (shortly, RBN) have proven useful in describing complex phenomena occurring at the unicellular level It is therefore interesting to investigate how their dynamical behavior is affected by cell-cell interactions, which mimics those occurring in tissues in multicellular organisms It has also been suggested that evolution may tend to adjust the parameters of the genetic Network so that it operates close to a critical state, which should provide evolutionary advantage ; this hypothesis has received intriguing, although not definitive support from recent findings It is therefore particularly interesting to consider how the tissue-like organization alters the dynamical behavior of the Networks close to a critical state In this paper we define a model tissue, which is a cellular automaton each of whose cells hosts a full RBN, and we report preliminary studies of the way in which the dynamics is affected.
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Random Boolean Network models and the yeast transcriptional Network
Proceedings of the National Academy of Sciences, 2003Co-Authors: Stuart A Kauffman, Carsten Peterson, Björn Samuelsson, Carl TroeinAbstract:The recently measured yeast transcriptional Network is analyzed in terms of simplified Boolean Network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean Networks. We find that, for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with Random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated Networks are frozen, in the sense that they reach the same state, regardless of initial state. Thus, our ensemble approach suggests that the yeast Network shows highly ordered dynamics.
Roberto Serra - One of the best experts on this subject based on the ideXlab platform.
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Dynamical regimes in non-ergodic Random Boolean Networks
Natural Computing, 2017Co-Authors: Marco Villani, Davide Campioli, Chiara Damiani, Andrea Roli, Alessandro Filisetti, Roberto SerraAbstract:Random Boolean Networks are a model of genetic regulatory Networks that has proven able to describe experimental data in biology. Random Boolean Networks not only reproduce important phenomena in cell dynamics, but they are also extremely interesting from a theoretical viewpoint, since it is possible to tune their asymptotic behaviour from order to disorder. The usual approach characterizes Network families as a whole, either by means of static or dynamic measures. We show here that a more detailed study, based on the properties of system’s attractors, can provide information that makes it possible to predict with higher precision important properties, such as system’s response to gene knock-out. A new set of principled measures is introduced, that explains some puzzling behaviours of these Networks. These results are not limited to Random Boolean Network models, but they are general and hold for any discrete model exhibiting similar dynamical characteristics.
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robustness analysis of a Boolean model of gene regulatory Network with memory
Journal of Computational Biology, 2011Co-Authors: Alex Graudenzi, Roberto Serra, Marco Villani, Annamaria Colacci, Stuart A KauffmanAbstract:Abstract The response to different kinds of perturbations of a discrete model of gene regulatory Network, which is a generalization of the Random Boolean Network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.
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the diffusion of perturbations in a model of coupled Random Boolean Networks
Cellular Automata for Research and Industry, 2008Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Chiara Damiani, Annamaria ColacciAbstract:Deciphering the influence of the interaction among the constituents of a complex system on the overall behaviour is one of the main goals of complex systems science. The model we present in this work is a 2D square cellular automaton whose of each cell is occupied by a complete Random Boolean Network. Random Boolean Networks are a well-known simplified model of genetic regulatory Networks and this model of interacting RBNs may be therefore regarded as a simplified model of a tissue or a monoclonal colony. The mechanism of cell-to-cell interaction is here simulated letting some nodes of a particular Network being influenced by the state of some nodes belonging to its neighbouring cells. One possible means to investigate the overall dynamics of a complex system is studying its response to perturbations. Our analyses follow this methodological approach. Even though the dynamics of the system is far from trivial we could show in a clear way how the interaction affects the dynamics and the global degree of order.
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why a simple model of genetic regulatory Networks describes the distribution of avalanches in gene expression data
Journal of Theoretical Biology, 2007Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Stuart A KauffmanAbstract:In a previous study it was shown that a simple Random Boolean Network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.
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coupled Random Boolean Network forming an artificial tissue
Cellular Automata for Research and Industry, 2006Co-Authors: Marco Villani, Roberto Serra, P Ingrami, Stuart A KauffmanAbstract:Random Boolean Networks (shortly, RBN) have proven useful in describing complex phenomena occurring at the unicellular level It is therefore interesting to investigate how their dynamical behavior is affected by cell-cell interactions, which mimics those occurring in tissues in multicellular organisms It has also been suggested that evolution may tend to adjust the parameters of the genetic Network so that it operates close to a critical state, which should provide evolutionary advantage ; this hypothesis has received intriguing, although not definitive support from recent findings It is therefore particularly interesting to consider how the tissue-like organization alters the dynamical behavior of the Networks close to a critical state In this paper we define a model tissue, which is a cellular automaton each of whose cells hosts a full RBN, and we report preliminary studies of the way in which the dynamics is affected.
Marco Villani - One of the best experts on this subject based on the ideXlab platform.
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Dynamical regimes in non-ergodic Random Boolean Networks
Natural Computing, 2017Co-Authors: Marco Villani, Davide Campioli, Chiara Damiani, Andrea Roli, Alessandro Filisetti, Roberto SerraAbstract:Random Boolean Networks are a model of genetic regulatory Networks that has proven able to describe experimental data in biology. Random Boolean Networks not only reproduce important phenomena in cell dynamics, but they are also extremely interesting from a theoretical viewpoint, since it is possible to tune their asymptotic behaviour from order to disorder. The usual approach characterizes Network families as a whole, either by means of static or dynamic measures. We show here that a more detailed study, based on the properties of system’s attractors, can provide information that makes it possible to predict with higher precision important properties, such as system’s response to gene knock-out. A new set of principled measures is introduced, that explains some puzzling behaviours of these Networks. These results are not limited to Random Boolean Network models, but they are general and hold for any discrete model exhibiting similar dynamical characteristics.
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robustness analysis of a Boolean model of gene regulatory Network with memory
Journal of Computational Biology, 2011Co-Authors: Alex Graudenzi, Roberto Serra, Marco Villani, Annamaria Colacci, Stuart A KauffmanAbstract:Abstract The response to different kinds of perturbations of a discrete model of gene regulatory Network, which is a generalization of the Random Boolean Network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.
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the diffusion of perturbations in a model of coupled Random Boolean Networks
Cellular Automata for Research and Industry, 2008Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Chiara Damiani, Annamaria ColacciAbstract:Deciphering the influence of the interaction among the constituents of a complex system on the overall behaviour is one of the main goals of complex systems science. The model we present in this work is a 2D square cellular automaton whose of each cell is occupied by a complete Random Boolean Network. Random Boolean Networks are a well-known simplified model of genetic regulatory Networks and this model of interacting RBNs may be therefore regarded as a simplified model of a tissue or a monoclonal colony. The mechanism of cell-to-cell interaction is here simulated letting some nodes of a particular Network being influenced by the state of some nodes belonging to its neighbouring cells. One possible means to investigate the overall dynamics of a complex system is studying its response to perturbations. Our analyses follow this methodological approach. Even though the dynamics of the system is far from trivial we could show in a clear way how the interaction affects the dynamics and the global degree of order.
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why a simple model of genetic regulatory Networks describes the distribution of avalanches in gene expression data
Journal of Theoretical Biology, 2007Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Stuart A KauffmanAbstract:In a previous study it was shown that a simple Random Boolean Network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.
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coupled Random Boolean Network forming an artificial tissue
Cellular Automata for Research and Industry, 2006Co-Authors: Marco Villani, Roberto Serra, P Ingrami, Stuart A KauffmanAbstract:Random Boolean Networks (shortly, RBN) have proven useful in describing complex phenomena occurring at the unicellular level It is therefore interesting to investigate how their dynamical behavior is affected by cell-cell interactions, which mimics those occurring in tissues in multicellular organisms It has also been suggested that evolution may tend to adjust the parameters of the genetic Network so that it operates close to a critical state, which should provide evolutionary advantage ; this hypothesis has received intriguing, although not definitive support from recent findings It is therefore particularly interesting to consider how the tissue-like organization alters the dynamical behavior of the Networks close to a critical state In this paper we define a model tissue, which is a cellular automaton each of whose cells hosts a full RBN, and we report preliminary studies of the way in which the dynamics is affected.
Alex Graudenzi - One of the best experts on this subject based on the ideXlab platform.
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A multiscale model of intestinal crypts dynamics
2012Co-Authors: Alex Graudenzi, Giulio Caravagna, Giovanni De Matteis, Giancarlo Mauri, Marco AntoniottiAbstract:Intestinal crypts are multicellular structures the properties of which have been partially characterized, both in the “normal” and in the transformed development, i.e. under specic mutations or pathway alterations eventually leading to the appearance of colorectal cancer. Only in the last years there has been an increasing interest in using mathematical and computational models to achieve new insights from a “systems point-of-view”. However, the overall picture lacks of a general model covering all the key distinct processes and phenomena involved in the activity of the crypt and, hence, a holistic and system-based picture of the overall dynamics is still missing. In this paper we propose a new multiscale model of crypt dynamics combining Gene Regulatory Networks at the intra-cellular level with a morphological model comprising spatial patterning, cell migration and crypt homeostasis at the inter-cellular level. The intra-cellular model is a Noisy Random Boolean Network ruling cell growth, division rate and lineage commitment in terms of emergent properties. The inter-cellular spatial dynamics is an extension of the Cellular Potts Model, a statistical mechanics model in which cells are represented as lattice sites in a 2D cellular automaton successfully used to model homeostasis in the crypts.
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robustness analysis of a Boolean model of gene regulatory Network with memory
Journal of Computational Biology, 2011Co-Authors: Alex Graudenzi, Roberto Serra, Marco Villani, Annamaria Colacci, Stuart A KauffmanAbstract:Abstract The response to different kinds of perturbations of a discrete model of gene regulatory Network, which is a generalization of the Random Boolean Network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.
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the diffusion of perturbations in a model of coupled Random Boolean Networks
Cellular Automata for Research and Industry, 2008Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Chiara Damiani, Annamaria ColacciAbstract:Deciphering the influence of the interaction among the constituents of a complex system on the overall behaviour is one of the main goals of complex systems science. The model we present in this work is a 2D square cellular automaton whose of each cell is occupied by a complete Random Boolean Network. Random Boolean Networks are a well-known simplified model of genetic regulatory Networks and this model of interacting RBNs may be therefore regarded as a simplified model of a tissue or a monoclonal colony. The mechanism of cell-to-cell interaction is here simulated letting some nodes of a particular Network being influenced by the state of some nodes belonging to its neighbouring cells. One possible means to investigate the overall dynamics of a complex system is studying its response to perturbations. Our analyses follow this methodological approach. Even though the dynamics of the system is far from trivial we could show in a clear way how the interaction affects the dynamics and the global degree of order.
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why a simple model of genetic regulatory Networks describes the distribution of avalanches in gene expression data
Journal of Theoretical Biology, 2007Co-Authors: Roberto Serra, Alex Graudenzi, Marco Villani, Stuart A KauffmanAbstract:In a previous study it was shown that a simple Random Boolean Network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.
Igor Jerman - One of the best experts on this subject based on the ideXlab platform.
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Boolean Networks with variable number of inputs k
Chaos, 2004Co-Authors: Metod Skarja, Barbara Remic, Igor JermanAbstract:We studied a Random Boolean Network model with a variable number of inputs K per element. An interesting feature of this model, compared to the well-known fixed-K Networks, is its higher orderliness. It seems that the distribution of connectivity alone contributes to a certain amount of order. In the present research, we tried to disentangle some of the reasons for this unexpected order. We also studied the influence of different numbers of source elements (elements with no inputs) on the Network’s dynamics. An analysis carried out on the Networks with an average value of K=2 revealed a correlation between the number of source elements and the dynamic diversity of the Network. As a diversity measure we used the number of attractors, their lengths and similarity. As a quantitative measure of the attractors’ similarity, we developed two methods, one taking into account the size and the overlapping of the frozen areas, and the other in which active elements are also taken into account. As the number of sourc...
