Index of papers in April 2015 that mention

**steady state**

AMSN | It should however be noted, that our model eXplores the DTT under steady state conditions only. |

Effect of Dopamine on the DTT | This steady state effect of dopamine is often modelled by changing the weight of cortico-striatal synapses for D1 ([0) and for D2 (ICZ) [27]. |

Effect of Dopamine on the DTT | Consistent with experimental observations, our model suggests that an increase in steady state dopamine levels would (1) increase the firing rate of D1 MSNs [28] , thereby (2) introducing a preference for ‘Go’ type actions or dyskinesia. |

Model limitations | Thirdly, our model explores the striatal dynamics at steady states , since the model showed stable fixed points. |

Model predictions and explanation of experimental data | In our model, an increase in the steady state level of dopamine changes the DTT to higher cortical input rates, thereby increasing the region in which D1 MSNs can have their firing rate exceed those of D2 MSNs (Fig 7A). |

Model predictions and explanation of experimental data | In our model, a decrease in the steady state dopamine level results in an increase in the range of cortical input for which D2 MSNs have a higher firing rate than D1 MSNs. |

steady state is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- firing rates (89)
- basal ganglia (21)
- feedforward (15)

Discussion | Other modeling approaches using perturbation data to unravel the network structure rely on modular response analysis (MRA), which requires steady state assumptions and linear equation based modeling. |

Discussion | Positive feedback loops have been shown to cause bistable behavior [49] and must be contained in the system's structure to enable more than one steady state [50]. |

Introduction | In contrast to related methods, which rely on the concept of sign consistency and require a steady state assumption [30, 34], exploitation of the dependency matrix is well-suited for the analysis of transient effects. |

Ordinary differential equation modeling | During the parameter estimation, no steady state conditions have been utilized. |

Ordinary differential equation modeling | We assume that non-stimulated measurements of signalling components in our data set is sufficient for training the system to an initial steady state in an unstimulated setting. |

steady state is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- phosphorylation (52)
- signaling pathways (32)
- MAPK (29)

Competitive lL-2 uptake by regulatory T cells | Fig 3), the IL-2 concentration attains a spatially inhomogeneous steady state more rapidly, with the overall IL-2 concentration being lower (Fig 4C and 4D and 82A Fig). |

Discussion | In contrast to immune cell signaling with a typical time scale of many hours during which diffusive gradients reach steady state , the transient behavior on shorter time scales is of particular interest for morphogen gradients [51,54]. |

Homogeneous cytokine secretion and uptake | As diffusion is fast (D = 10um2/s, see Table 1), it reaches a steady state after about LZ/D = 0.5 s, Where L is the cell-to-cell distance in the case of high cell-density. |

Homogeneous cytokine secretion and uptake | Thus, it is sufficient to consider the diffusion equation in steady state in the extracellular domain with flux boundary condition at the cell surface: c(r) is the cytokine concentration at distance r from the center of the cell, A is the Laplace operator in spherical coordinates, p is the cell radius, and q is the cytokine secretion rate. |

steady state is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

- cytokine (143)
- T cells (36)
- cell-to-cell (12)