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The route planning problem is a minimization problem based on static networks, while the resource allocation problem in ARG is a maximization problem based on dynamical networks.
For both human and plant diseases, a challenge in modelling disease spread in dynamic networks is the use of metrics that can account for dynamic properties (Natale et al. 2011), given that most network metrics are based on static networks (Bajardi et al. 2011 b ; Nöremark et al. 2011; Klemm et al. 2012).
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We define morphism between chemical reaction networks (CRNs) based on static network structure: connectivity, stoichiometry, and rate constants.
As the network is actually highly dynamic and completely cross-linked, there is still large space for quantitative prediction of synergistic combination based on static network [ 69, 70].
From this perspective, the protein complex discovery methods based on static PPI networks have limitations.
In a study based on static Bayesian networks Werhli et al. [ 33] come to a similar the conclusion.
Examples of this are methods that infer regulatory modules from expression data across different experimental conditions [ 1, 2], or methods based on (static) Bayesian network models [ 3, 4].
Many current methods are tailored to the inference of gene regulatory networks based on static measurements at one time point, reflecting the steady state of the system under consideration [ 48].
These measures are based on static, topological properties of the network and are intuitive extensions of centrality measures used to asses essentiality of proteins in protein-protein interaction networks.
Here, active interventions are necessary in order to resolve the ambiguity of certain edge directions intrinsic to the inference of network structures based on static and passive observations.
Solutions based on static measurements, with journey costs taken as units based on free-flow travel time; do not reflect real network performance at different times of the day.
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