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The models utilized these network topology variables to account for stochasticity in network design effects on pedestrian dynamics.
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Further, it would be also interesting to account for stochasticity in this framework.
For more details, see Text S1, part 7. To understand the present data on cell-cell variability, we employed models for stochasticity in protein expression.
Probabilistic reaction norms (PRNs) are an extension of the concept of reaction norms, developed to account for stochasticity in ontogenetic transitions.
Under either condition, a significant portion of initial states converged to the "other" attractor (neither cell cycle nor sporulation), which illustrates the stochasticity in the network.
This method focuses on distributions of pseudo-periodicities while accounting for any stochasticity in the signal.
The major source of phenotypic variation is noise or stochasticity in gene expression networks, which can directly promote the formation of population heterogeneity.
As a single-molecule level model, Gillespie's stochastic simulation algorithm (SSA) [ 9, 10] is based on the chemical master equation; it describes the interactions among single molecules and accounts for noise and stochasticity in the regulation of a genetic network.
This presents a novel structure of context-sensitive SBNs (CSSBNs) for modeling the stochasticity in a context-dependent GRN.
This yields a novel structure of context-sensitive SBNs (CSSBNs) for modeling the stochasticity in a GRN.
The results reveal how noise that cells encounter during growth and development shapes networks' robustness to stochasticity in gene expression, which in turn shapes networks' robustness to mutation.
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