Index of papers in April 2015 that mention

**model parameters**

1. Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response

Core regulatory components in the immediate-early response | Lists of the genes assigned to kinetic signatures and the corresponding model parameters are provided in Supporting File 1. |

Definition of kinetic signatures | The inference of model parameters from CAGE data for the early peak and linear models using nested sampling and the 11 based likelihood is illustrated in Fig 1C. |

Definition of kinetic signatures | Bayesian evidence values and model parameter estimates (and their standard deviations) are computed using nested sampling for all signatures for each time series. |

Discussion | Model parameters also give the timing of potentially important events such as transitions in eXpression levels within the time course. |

Discussion | Models are specified in advance, and selection is based on the integration of model parameters rather than from a point estimate of best values, an approach which can be sensitive to the optimisation procedure used. |

Kinetics and chromatin features underlying IEG induction | Further exploration of model parameters yielded other insights. |

Results | Further details of the specification of priors for model parameters , and model selection are given in Materials and methods. |

peak category. | CAGE clusters and associated model parameters for all time courses. |

model parameters is mentioned in 8 sentences in this paper.

Topics mentioned in this paper:

- time course (23)
- time series (19)
- transcription factors (15)

A power-law summarizes uptake dependence on host receptors | Sequential perturbation of Zipper model parameters shows that these changes can be mapped to changes in both power-law parameters (84C Fig). |

Log 1IKM | (A) Mapping of 3-stage model parameters on power-law parameters. |

Log 1IKM | Each set of markers shows the effect of increasing a zipper model parameter over two orders of magnitude centered on a base value. |

Log 1IKM | This is reminiscent of modeling analysis showing that perturbation to Zipper model parameters modulates power-law parameter values along the same, restricted path (Fig 4A). |

Supporting Information | A power-law of the form P = RB/KDeff was fit to data for different values of each zipper model parameter . |

Supporting Information | The zipper model parameters are normalized with respect to their base values (S4 Table). |

model parameters is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- power-law (42)
- E. coli (20)
- HeLa cells (16)

Methods | The fitting was performed using Markov Chain Monte Carlo (MCMC) methods, which find an ensemble of model parameters that fit the data and allow a credible interval for each of these parameters to be determined. |

Statistical details | This LL sum can now be used as an objective function for our Markov Chain Monte Carlo scheme, to fit our model parameter values so that model outputs match the NNDSS data. |

Year | The black dots are US disease incidence data, and the shaded regions represent the credible intervals (50% and 95%) obtained through model parameter estimation of model 8. |

y vaccine- reinfectlon | State variables (population compartments) and model parameters for the model investigated. |

y vaccine- reinfectlon | Model parameters |

model parameters is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

Abstract | In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). |

GA optimization using a single action potential | Nine model parameters , describing maximal conductances of ionic currents [the sodium current (INa), the L-type calcium current (ICaL), the T-type calcium current (ICaT), the inwardly rectifying potassium (1K1), the rapid and slow delayed rectifier potassium currents (1Kr and IKS), the plateau potassium current (IKp), and the sarcolemmal calcium pump current (IpCa)] and the maximal flux of the sarcoplasmic reticulum Ca2+-ATPase (ISERCA) were estimated using a GA technique. |

Introduction | This step typically requires tuning of model parameters to reproduce Whole-cell behavior; this tuning is usually done manually in a laborious, iterative tweaking process, Which ends When the model output (e.g., an action potential) subjectively is deemed to adequately match the experimental counterparts. |

Parameter estimation shows changes compared to FR model and variability among individual cells | The dissimilarities between the original FR model and the experimental data led to considerable changes in the estimated values for the model parameters for all four cells (Fig 6). |

Parameter estimation shows changes compared to FR model and variability among individual cells | Considered together with the demonstration that the approach accurately identifies model parameters (Figs 2—4), these findings suggest that the approach significantly improves the fidelity of the model for cellular data, relative to the published generic model. |

model parameters is mentioned in 5 sentences in this paper.

Topics mentioned in this paper:

- action potential (38)
- experimental data (21)
- parameter estimation (19)

Equivalence of MID and maximum-likelihood LNP | Empirical single-spike information is therefore equal to LNP model log-likelihood per spike, plus a constant that does not depend on model parameters . |

Gradient and Hessian of LNP log-likelihood | Where 9 2 {Kg} are the model parameters , A is the time bin size, and 1 denotes a vector of ones. |

Gradient and Hessian of LNP log-likelihood | With respect to the model parameters can be written: inverse-link function g at its input, and ‘0’ denotes Hadamard or component-Wise vector product. |

V1 data analysis | The cbf- and rbf-LNP models were both fit by maximizing the likelihood for the model parameters 9 2 {Kg}. |

model parameters is mentioned in 4 sentences in this paper.

Topics mentioned in this paper:

- log-likelihood (30)
- basis functions (20)
- mutual information (14)

Sensitivity analysis of gene expression model | Previous studies using total dl as the input to the gene expression model have revealed a high level of sensitivity of the Type III genes to changes in model parameters [10]. |

Sensitivity analysis of gene expression model | To determine the sensitivity of our results using free dl with respect to changes in model parameters , we took the best fit parameters for both free dl and total dl and varied them by i 10%. |

Sensitivity analysis of gene expression model | The model with free dl is insensitive to 10% variations in the three model parameters (961;. |

model parameters is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- gene expression (42)
- parameter sets (10)
- Sensitivity analysis (6)

Impact of spontaneous rate on computational model | We examined spontaneous rates covering the entire range observed in our real neuronal population (0—40 spk/s) and found that the model parameters for generating synchronized and non-synchronized neurons were similar, albeit with a slight shift in the threshold I/E ratio for observing synchronized responses (Fig. |

Model parameters underlying rate and temporal representations | Model parameters underlying rate and temporal representations |

Model parameters underlying rate and temporal representations | We observed that synchronized, non-synchronized, and mixed responses were generated within three distinct regions of the model’s parameter space (Fig. |

model parameters is mentioned in 3 sentences in this paper.

Topics mentioned in this paper:

- simulated neurons (29)
- computational model (20)
- excitatory input (16)