|Compounds that drive insulin signaling|
Results of the sensitivity analysis on the initial model conditions showed that HIFloc and IGFBP2 levels in the insulin signaling system were most sensitive to reduced oxygen (2%) and also elevated IGFItotal levels (Fig 7).
Sensitivity analysis on initial conditions found the insulin signaling pathway to be most sensitive to IGFI concentration and oxygen levels.
Our sensitivity analysis on the rate constants showed that the contribution of basal HIFloc production to LN229 glioblastoma growth is greater than contribution of the IGFI-dependent HIFloc production.
|Global sensitivity analysis|
Global sensitivity analysis
The results from the complete sensitivity analysis can be found in 82 File.
Sensitivity analysis was summarized by calculating the sensitivity indeX (see below) at 40 days for the LN229 cell line in Table 1.
Complete sensitivity analysis results.
Sensitivity analysis of initial conditions and rate constants on IGFI, IGFBPZ, HIFloc and glioblastoma diameter for both U87 and LN229 glio-blastoma cell lines for 24 hour simulation.
We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses.
To holistically evaluate model behavior, we developed a multi-parameter sensitivity analysis for characterizing our HDC model and generated systems-level, parameter-independent descriptions of the early tumor-immune network.
Moreover, HDC model sensitivity analyses , such at those described here, could facilitate enhancing design robustness, for example by identifying cell-based therapy designs that perform well over parameter variations that represent patient-to-patient differences and other practical sources of variability.
|Evaluation of potential engineered cell-based therapy strategies|
Based upon our system-wide parameter sensitivity analysis (Fig.
|Functional and spatial predictors of tumor clearance|
This focused multiparametric sensitivity analysis revealed that MPI at t = 0.5 correlated with tumor survival probability across the range of parameters evaluated (Fig.
A central innovation of this investigation was the development and application of system-wide multiparametric sensitivity analyses of our HDC models, effectively providing parameter-independent understanding of tumor-im-mune interactions in the TME and establishing useful metrics for systems-level analysis of such models.
Expanded multi-parameter sensitivity analysis (MPSA).
|Systematic characterization of TME network robustness and the role of heterogeneities|
For models based upon differential equations, this task can be accomplished by multiparametric sensitivity analyses (MPSA), which can identify combinatorial influences of multiple parameters and elucidate systemic features of such a model, such as the boundaries of distinct regimes of model behavior .
|Sensitivity analysis of gene expression model|
Sensitivity analysis of gene expression model
Sensitivity analysis , free dl case.
Using free d1 as the input to the gene expression model, a sensitivity analysis shows little sensitivity to changes in the dl threshold parameters (BdlmRNA), lifetime parameters (Ti), and noise parameter (1]) for our genes of interest.
Sensitivity analysis , total dl case.
|Limitations and potential improvements|
As expected, local sensitivity analysis on simulated calcium traces demonstrates that they are most sensitive to changes in ICaL, IpCa, and ISERCA (SS Fig), which leads to the speculation that the estimation of these parameters could improve.
Local sensitivity analysis of FR model during stochastic pacing, voltage clamp, and combined protocol.
Multi-parameter sensitivity analysis of FR model during single action potential, stochastic pacing, and combined protocol.
Multi-parameter sensitivity analysis was carried out as detailed in 81 Text.
A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections.
As we cannot a priori be sure that the choice of a A and (9,1 does not influence our posterior as long as they are arbitrarily small, we performed a prior sensitivity analysis and reran the analysis with al 2 (9,1 = 0.01 and a; = (91 = 0.0001.
|Standard Hierarchical Weighting model|
We performed the corresponding sensitivity analysis for the hierarchical ensemble prediction by applying hyperpriors Aug = Bag = Am; = BM set to 0.01, 0.001 and 0.0001.
|Standard Hierarchical Weighting model|
Secondly, we performed a sensitivity analysis , hierarchical sensitivity setup two, Where we fixed the shape parameters Ad ,1 = Am; = 0.001 and only varied Bag 2 BM, again set to either 0.01, 0.001 or 0.0001.
|Data for the validation of the prediction model|
Sensitivity analyses which compare model predictions against observed impact after 4, 5 and 6 years are reported in the appendix.
Sensitivity analysis including Norway.
Sensitivity analysis including longer time-frames.