236,324 research outputs found
Achieving synchronization in arrays of coupled differential systems with time-varying couplings
In this paper, we study complete synchronization of the complex dynamical
networks described by linearly coupled ordinary differential equation systems
(LCODEs). The coupling considered here is time-varying in both the network
structure and the reaction dynamics. Inspired by our previous paper [6], the
extended Hajnal diameter is introduced and used to measure the synchronization
in a general differential system. Then we find that the Hajnal diameter of the
linear system induced by the time-varying coupling matrix and the largest
Lyapunov exponent of the synchronized system play the key roles in
synchronization analysis of LCODEs with the identity inner coupling matrix. As
an application, we obtain a general sufficient condition guaranteeing directed
time-varying graph to reach consensus. Example with numerical simulation is
provided to show the effectiveness the theoretical results.Comment: 22 pages, 4 figure
Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks
Background: Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time.
Results: In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator.
Availability: Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/
The effect of negative feedback loops on the dynamics of Boolean networks
Feedback loops in a dynamic network play an important role in determining the
dynamics of that network. Through a computational study, in this paper we show
that networks with fewer independent negative feedback loops tend to exhibit
more regular behavior than those with more negative loops. To be precise, we
study the relationship between the number of independent feedback loops and the
number and length of the limit cycles in the phase space of dynamic Boolean
networks. We show that, as the number of independent negative feedback loops
increases, the number (length) of limit cycles tends to decrease (increase).
These conclusions are consistent with the fact, for certain natural biological
networks, that they on the one hand exhibit generally regular behavior and on
the other hand show less negative feedback loops than randomized networks with
the same numbers of nodes and connectivity
Control of asymmetric Hopfield networks and application to cancer attractors
The asymmetric Hopfield model is used to simulate signaling dynamics in
gene/transcription factor networks. The model allows for a direct mapping of a
gene expression pattern into attractor states. We analyze different control
strategies aiming at disrupting attractor patterns using selective local fields
representing therapeutic interventions. The control strategies are based on the
identification of signaling , which are single nodes or strongly
connected clusters of nodes that have a large impact on the signaling. We
provide a theorem with bounds on the minimum number of nodes that guarantee
controllability of bottlenecks consisting of strongly connected components. The
control strategies are applied to the identification of sets of proteins that,
when inhibited, selectively disrupt the signaling of cancer cells while
preserving the signaling of normal cells. We use an experimentally validated
non-specific network and a specific B cell interactome reconstructed from gene
expression data to model cancer signaling in lung and B cells, respectively.
This model could help in the rational design of novel robust therapeutic
interventions based on our increasing knowledge of complex gene signaling
networks
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