501,453 research outputs found
Evolution models for dynamic networks
This paper proposes a mathematical framework for
modelling the evolution of dynamic networks. Such framework allows the time analysis of the relationship between the dynamic laws and the network characterizing features (degree distribution, clustering coefficient, controllability indexes, etc.) providing new insight on the network properties. The framework also allows to relate and generalize existing inference procedures for modelling real world time evolving complex systems
Comment on "Regularizing capacity of metabolic networks"
In a recent paper, Marr, Muller-Linow and Hutt [Phys. Rev. E 75, 041917
(2007)] investigate an artificial dynamic system on metabolic networks. They
find a less complex time evolution of this dynamic system in real networks,
compared to networks of reference models. The authors argue that this suggests
that metabolic network structure is a major factor behind the stability of
biochemical steady states. We reanalyze the same kind of data using a dynamic
system modeling actual reaction kinetics. The conclusions about stability, from
our analysis, are inconsistent with those of Marr et al. We argue that this
issue calls for a more detailed type of modeling
A multi-agent-based evolution model of innovation networks in dynamic environments
An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization
A Separable Model for Dynamic Networks
Models of dynamic networks --- networks that evolve over time --- have
manifold applications. We develop a discrete-time generative model for social
network evolution that inherits the richness and flexibility of the class of
exponential-family random graph models. The model --- a Separable Temporal ERGM
(STERGM) --- facilitates separable modeling of the tie duration distributions
and the structural dynamics of tie formation. We develop likelihood-based
inference for the model, and provide computational algorithms for maximum
likelihood estimation. We illustrate the interpretability of the model in
analyzing a longitudinal network of friendship ties within a school.Comment: 28 pages (including a 4-page appendix); a substantial rewrite, with
many corrections, changes in terminology, and a different analysis for the
exampl
Evolving eco-system: a network of networks
Ecology and evolution are inseparable. Motivated by some recent experiments,
we have developed models of evolutionary ecology from the perspective of
dynamic networks. In these models, in addition to the intra-node dynamics,
which corresponds to an individual-based population dynamics of species, the
entire network itself changes slowly with time to capture evolutionary
processes. After a brief summary of our recent published works on these network
models of eco-systems, we extend the most recent version of the model
incorporating predators that wander into neighbouring spatial patches for food.Comment: 7 pages including 2 figure
BioJazz : In silico evolution of cellular networks with unbounded complexity using rule-based modeling
Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx
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