scholarly journals Sensitivity analysis of signaling pathway models based on discrete-time measurements

2017 ◽  
Vol 27 (2) ◽  
pp. 239-250 ◽  
Author(s):  
Malgorzata Kardynska ◽  
Jaroslaw Smieja
Keyword(s):  
Sensitivity Analysis ◽  
Signaling Pathways ◽  
Discrete Time ◽  
Large Scale ◽  
Model Development ◽  
Original Method ◽  
Single Parameter ◽  
Scale Models ◽  
Pathway Models ◽  
System Time

Abstract The paper is focused on sensitivity analysis of large-scale models of biological systems that describe dynamics of the so called signaling pathways. These systems are continuous in time but their models are based on discrete-time measurements. Therefore, if sensitivity analysis is used as a tool supporting model development and evaluation of its quality, it should take this fact into account. Such models are usually very complex and include many parameters difficult to estimate in an experimental way. Changes of many of those parameters have little effect on model dynamics, and therefore they are called sloppy. In contrast, other parameters, when changed, lead to substantial changes in model responses and these are called stiff parameters. While this is a well-known fact, and there are methods to discern sloppy parameters from the stiff ones, they have not been utilized, so far, to create parameter rankings and quantify the influence of single parameter changes on system time responses. These single parameter changes are particularly important in analysis of signalling pathways, because they may pinpoint parameters, associated with the processes to be targeted at the molecular level in laboratory experiments. In the paper we present a new, original method of creating parameter rankings, based on an Hessian of a cost function which describes the fit of the model to a discrete experimental data. Its application is explained with simple dynamical systems, representing two typical dynamics exhibited by the signaling pathways.

scholarly journals Machine learning-based feature importance approach for sensitivity analysis of steel frames

2021 ◽  
Author(s):  
Hyeyoung Koh ◽  
Hannah Beth Blum
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Large Scale ◽  
Failure Modes ◽  
Model Development ◽  
Predictive Performance ◽  
Computational Effort ◽  
Structural Systems ◽  
Feature Importance

This study presents a machine learning-based approach for sensitivity analysis to examine how parameters affect a given structural response while accounting for uncertainty. Reliability-based sensitivity analysis involves repeated evaluations of the performance function incorporating uncertainties to estimate the influence of a model parameter, which can lead to prohibitive computational costs. This challenge is exacerbated for large-scale engineering problems which often carry a large quantity of uncertain parameters. The proposed approach is based on feature selection algorithms that rank feature importance and remove redundant predictors during model development which improve model generality and training performance by focusing only on the significant features. The approach allows performing sensitivity analysis of structural systems by providing feature rankings with reduced computational effort. The proposed approach is demonstrated with two designs of a two-bay, two-story planar steel frame with different failure modes: inelastic instability of a single member and progressive yielding. The feature variables in the data are uncertainties including material yield strength, Young’s modulus, frame sway imperfection, and residual stress. The Monte Carlo sampling method is utilized to generate random realizations of the frames from published distributions of the feature parameters, and the response variable is the frame ultimate strength obtained from finite element analyses. Decision trees are trained to identify important features. Feature rankings are derived by four feature selection techniques including impurity-based, permutation, SHAP, and Spearman's correlation. Predictive performance of the model including the important features are discussed using the evaluation metric for imbalanced datasets, Matthews correlation coefficient. Finally, the results are compared with those from reliability-based sensitivity analysis on the same example frames to show the validity of the feature selection approach. As the proposed machine learning-based approach produces the same results as the reliability-based sensitivity analysis with improved computational efficiency and accuracy, it could be extended to other structural systems.

Advances and next steps exploring the role of groundwater storage in watershed variability across spatial scales

2021 ◽  
Author(s):  
Laura Condon
Keyword(s):  
Surface Water ◽  
Large Scale ◽  
Groundwater Modeling ◽  
Spatial Scales ◽  
Model Development ◽  
Continental Scale ◽  
Freshwater Resource ◽  
Scale Models ◽  
Watershed Dynamics ◽  
The Stability

<p>Groundwater is  by far the largest unfrozen freshwater resource on the planet. Yet it is often excluded or greatly simplified in global and  continental scale models.   It’s well established that feedbacks between groundwater depth, surface runoff and land energy fluxes can influence watershed dynamics. However, we still don’t understand the large scale implications of these exchanges in evolving systems.  Advances in continental scale integrated hydrologic modeling increasingly allow us to explore these interactions across spatial scales. With large scale models we can start to quantify the total impact that groundwater surface water exchanges have on the water balance as a whole, as well as watershed dynamics.  Here I will explore the buffering effect that groundwater can have on both human and natural water stressors across the US and the physical drivers of these connections.  I will also explore the impacts of long term trends on the stability of groundwater surface water exchanges. These results demonstrate the importance of the subsurface for future hydrologic predictions, and the potential gains from improved groundwater representations large scale simulations.  While there have been great advances in large scale groundwater modeling in recent years, there is still significant need for continued community model development and intercomparison.</p>

scholarly journals On the Need to Determine Accurately the Impact of Higher-Order Sensitivities on Model Sensitivity Analysis, Uncertainty Quantification and Best-Estimate Predictions

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6318
Author(s):  
Dan Gabriel Cacuci
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Confidence Intervals ◽  
Large Scale ◽  
Neutron Transport ◽  
Higher Order ◽  
High Order ◽  
Tolerance Limits ◽  
Scale Models ◽  
Accurate Quantification

This work aims at underscoring the need for the accurate quantification of the sensitivities (i.e., functional derivatives) of the results (a.k.a. “responses”) produced by large-scale computational models with respect to the models’ parameters, which are seldom known perfectly in practice. The large impact that can arise from sensitivities of order higher than first has been highlighted by the results of a third-order sensitivity and uncertainty analysis of an OECD/NEA reactor physics benchmark, which will be briefly reviewed in this work to underscore that neglecting the higher-order sensitivities causes substantial errors in predicting the expectation and variance of model responses. The importance of accurately computing the higher-order sensitivities is further highlighted in this work by presenting a text-book analytical example from the field of neutron transport, which impresses the need for the accurate quantification of higher-order response sensitivities by demonstrating that their neglect would lead to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis) of the model response’s distribution in the phase space of model parameters. The incorporation of response sensitivities in methodologies for uncertainty quantification, data adjustment and predictive modeling currently available for nuclear engineering systems is also reviewed. The fundamental conclusion highlighted by this work is that confidence intervals and tolerance limits on results predicted by models that only employ first-order sensitivities are likely to provide a false sense of confidence, unless such models also demonstrate quantitatively that the second- and higher-order sensitivities provide negligibly small contributions to the respective tolerance limits and confidence intervals. The high-order response sensitivities to parameters underlying large-scale models can be computed most accurately and most efficiently by employing the high-order comprehensive adjoint sensitivity analysis methodology, which overcomes the curse of dimensionality that hampers other methods when applied to large-scale models involving many parameters.

scholarly journals Gsmodutils: A python based framework for test-driven genome scale metabolic model development

2018 ◽  
Author(s):  
James P Gilbert ◽  
Nicole Pearcy ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Model Development ◽  
Metabolic Model ◽  
Validation Data ◽  
Engineering Research ◽  
Modelling Framework ◽  
Wide Range ◽  
Scale Models ◽  
Genome Scale

AbstractMotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed thegsmodutilsmodelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.AvailabilityThe software framework described within this paper is open source and freely available fromhttp://github.com/SBRCNottingham/gsmodutils

Sensitivity analysis of deterministic signaling pathways models

2012 ◽  
Vol 60 (3) ◽  
pp. 471-479 ◽  
Author(s):  
K. Puszyński ◽  
P. Lachor ◽  
M. Kardyńska ◽  
J. Śmieja
Keyword(s):  
Sensitivity Analysis ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Drug Targets ◽  
P53 Protein ◽  
General Outline ◽  
Sensitivity Functions ◽  
Regulatory Module ◽  
Pathway Models ◽  
Single Method

Abstract The paper is focused on application of sensitivity methods to analysis of signaling pathway models. Two basic methods are compared: local, based on standard sensitivity functions, and global, based on Sobol indices. Firstly, a general outline of modeling of signaling pathways by means of ordinary differential equations is briefly described. Afterwards, the methods of sensitivity analysis, known from literature, are introduced and illustrated with a simple example of a dynamical system of the second order. Subsequently, the analysis of the p53/Mdm2 regulatory module, which is a key element of any pathway involving p53 protein, is presented. The results of this analysis suggest that no single method should be chosen for investigation of any signaling pathway model but several of them should be applied to answer important questions about sources of heterogeneity in cells behavior, robustness of signaling pathways and possible molecular drug targets.

DMSP – Database for Modeling Signaling Pathways

2008 ◽  
Vol 47 (02) ◽  
pp. 104-148
Author(s):  
M. Breit ◽  
B. Pfeifer ◽  
C. Baumgartner ◽  
R. Modre-Osprian ◽  
B. Tilg ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Initial Step ◽  
Point Of View ◽  
Biological Knowledge ◽  
Literature Study ◽  
Online Databases ◽  
Qualitative Level ◽  
Pathway Models

Summary Objectives: Presently, the protein interaction information concerning different signaling pathways is available in a qualitative manner in different online protein interaction databases. The challenge here is to derive a quantitative way of modeling signaling pathways from qualitative way of modeling signaling pathways from a qualitative level. To address this issue we developed a database that includes mathematical modeling knowledge and biological knowledge about different signaling pathways. Methods: The database is part of an integrative environment that includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning pathways. The system is designed as a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. Results: DMSP – Database for Modeling Signaling Pathways incorporates biological datasets from online databases like BIND, DIP, PIP, and SPiD. The modeling knowledge that has been incorporated is based on a literature study. Pathway models can be designed, visualized and simulated based on the knowledge stored in the DMSP. The user can download the whole dataset and build pathway models using the knowledge stored in our database. As an example, the TNF? pathway model was implemented and tested using this approach. Conclusion: DMSP is an initial step towards the aim of combining modeling and biological knowledge concerning signaling pathways. It helps in understanding pathways in a qualitative manner from a qualitative level. Simulation results enable the interpretation of a biological system from a quantitative and systemtheoretic point of view.

Cyclic Breathing Simulations in Large Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

2012 ◽  
Author(s):  
D. Keith Walters ◽  
Greg W. Burgreen ◽  
Robert L. Hester ◽  
David S. Thompson ◽  
David M. Lavallee ◽  
...  
Keyword(s):  
Steady State ◽  
Boundary Conditions ◽  
Large Scale ◽  
Whole Body ◽  
Patient Specific ◽  
Cfd Simulations ◽  
Reduced Order ◽  
Scale Models ◽  
Steady State Simulation ◽  
Conducting Zone

Computational fluid dynamics (CFD) simulations were performed for unsteady periodic breathing conditions, using large-scale models of the human lung airway. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to G16) obtained from a stochastically generated airway tree with statistically realistic geometrical characteristics. A reduced-order geometry was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to G16. The inlet and outlet flow boundaries corresponded to the oronasal opening (superior), the inlet/outlet planes in terminal bronchioles (distal), and the unresolved airway boundaries arising from the truncation procedure (intermediate). The cyclic flow was specified according to the predicted ventilation patterns for a healthy adult male at three different activity levels, supplied by the whole-body modeling software HumMod. The CFD simulations were performed using Ansys FLUENT. The mass flow distribution at the distal boundaries was prescribed using a previously documented methodology, in which the percentage of the total flow for each boundary was first determined from a steady-state simulation with an applied flow rate equal to the average during the inhalation phase of the breathing cycle. The distal pressure boundary conditions for the steady-state simulation were set using a stochastic coupling procedure to ensure physiologically realistic flow conditions. The results show that: 1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; 2) the predicted alveolar pressure is in good agreement with previously documented values; and 3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

Sign in / Sign up

Export Citation Format

Share Document