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A Methodology For Performing Global Uncertainty And Sensitivity Analysis In Systems Biology

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TLDR
This work develops methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models and provides a complete methodology for performing these analyses, in both deterministic and stochastic settings, and proposes novel techniques to handle problems encountered during these types of analyses.
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This article is published in Journal of Theoretical Biology.The article was published on 2008-09-07 and is currently open access. It has received 2014 citations till now. The article focuses on the topics: Uncertainty analysis & Sensitivity analysis.

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Multisite Phosphorylation Provides an Effective and Flexible Mechanism for Switch-Like Protein Degradation

TL;DR: This model explains the role of multiple phosphorylation sites, compared to a single site, in the regulation of protein degradation and suggests design principles for protein degradation switches which might be a widespread mechanism for precise regulation of cellular processes such as cell cycle progression.
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What Can We Learn from Global Sensitivity Analysis of Biochemical Systems

TL;DR: This work applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters, and suggested that this level of uncertainty can be used as a global measure of model robustness.
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A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology

TL;DR: This is the first mechanism-based, multi-pathway computational model that describes the integrated signal transduction and macrophage programming under M1 (IFN-γ), M2 (IL-4) and cell stress (hypoxia) stimulation and provides an important computational basis toward a more quantitative and network-centric understanding of the complex physiology and versatile functions of macrophages in human diseases.
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The Origin of GnRH Pulse Generation: An Integrative Mathematical-Experimental Approach

TL;DR: A mathematical model of the kisspeptin neuronal network is developed and predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically.
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Compartment Model Predicts VEGF Secretion and Investigates the Effects of VEGF Trap in Tumor-Bearing Mice

TL;DR: A computational model of VEGF transport and kinetics in the tumor-bearing mouse is developed and provides a framework with which to examine the effects of anti-VEGF agents, aiding in the optimization of such anti-angiogenic therapeutics as well as analysis of clinical data.
References
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Book

An Introduction to Multivariate Statistical Analysis

TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code

TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal Article

Factorial sampling plans for preliminary computational experiments

Max D. Morris
- 01 Jan 1992 - 
TL;DR: The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input.
Journal ArticleDOI

Factorial sampling plans for preliminary computational experiments

TL;DR: In this article, the problem of designing computational experiments to determine which inputs have important effects on an output is considered, and experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects.
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