L1and L2norms in sensitivity analysis of signaling pathway models

2016 ◽  
Author(s):  
Malgorzata Kardynska ◽  
Jaroslaw Smieja
Keyword(s):  
Sensitivity Analysis ◽  
Signaling Pathway ◽  
Pathway Models

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.

scholarly journals Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models

2016 ◽  
Vol 44 (W1) ◽  
pp. W212-W216 ◽  
Author(s):  
Francisco Salavert ◽  
Marta R. Hidago ◽  
Alicia Amadoz ◽  
Cankut Çubuk ◽  
Ignacio Medina ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Therapeutic Targets ◽  
Pathway Models

scholarly journals Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients

2015 ◽  
Vol 8 (408) ◽  
pp. ra130-ra130 ◽  
Author(s):  
D. Fey ◽  
M. Halasz ◽  
D. Dreidax ◽  
S. P. Kennedy ◽  
J. F. Hastings ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Patient Specific ◽  
Pathway Models

scholarly journals Hilbert-Schmidt and Sobol sensitivity indices for static and time series Wnt signaling measurements in colorectal cancer - Part A

2015 ◽  
Author(s):  
shriprakash sinha
Keyword(s):  
Colorectal Cancer ◽  
Time Series ◽  
Sensitivity Analysis ◽  
Wnt Signaling ◽  
Signaling Pathway ◽  
Expression Profiles ◽  
Biological Knowledge ◽  
Prior Biological Knowledge ◽  
Sensitivity Indices ◽  
Higher Dimensional

AbstractEver since the accidental discovery of Wingless [Sharma R.P., Drosophila information service, 1973, 50, p 134], research in the field of Wnt signaling pathway has taken significant strides in wet lab experiments and various cancer clinical trials, augmented by recent developments in advanced computational modeling of the pathway. Information rich gene expression profiles reveal various aspects of the signaling pathway and help in studying different issues simultaneously. Hitherto, not many computational studies exist which incorporate the simultaneous study of these issues. This manuscript • explores the strength of contributing factors in the signaling pathway, • analyzes the existing causal relations among the inter/extracellular factors effecting the pathway based on prior biological knowledge and • investigates the deviations in fold changes in the recently found prevalence of psychophysical laws working in the pathway. To achieve this goal, local and global sensitivity analysis is conducted on the (non)linear responses between the factors obtained from static and time series expression profiles using the density (Hilbert-Schmidt Information Criterion) and variance (Sobol) based sensitivity indices. The results show the advantage of using density based indices over variance based indices mainly due to the former’s employment of distance measures & the kernel trick via Reproducing kernel Hilbert space (RKHS) that capture nonlinear relations among various intra/extracellular factors of the pathway in a higher dimensional space. In time series data, using these indices it is now possible to observe where in time, which factors get influenced & contribute to the pathway, as changes in concentration of the other factors are made. This synergy of prior biological knowledge, sensitivity analysis & representations in higher dimensional spaces can facilitate in time based administration of target therapeutic drugs & reveal hidden biological information within colorectal cancer samples. Code has been made available at Google drive onhttps://drive.google.com/folderview?id=0B7Kkv8wlhPU-Q2NBZGt1ZERrSVE&usp=sharing

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