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.About:
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.read more
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Dynamics of Elongation Factor 2 Kinase Regulation in Cortical Neurons in Response to Synaptic Activity
Justin W. Kenney,Oksana Sorokina,Maja Genheden,Anatoly Sorokin,J. D. Armstrong,Christopher G. Proud +5 more
TL;DR: It is demonstrated that AMP kinase (AMPK) is also an important regulator of synaptic activity-driven eEF2K dynamics in neurons, the first evidence that it is necessary to consider the combined interactions of Ca2+ with MEK/ERK, mTORC1, and AMPK to adequately explain e EF2K regulation in neurons.
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Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy.
TL;DR: Unlike typical sensitivity analyses of dynamic models that only address variation in parameters, the probabilistic uncertainty analysis described here enables modelers to convey the robustness of their predictions to policy makers, extending the power of epidemiological modeling to improve public health.
Journal ArticleDOI
Pulse HIV vaccination: feasibility for virus eradication and optimal vaccination schedule.
TL;DR: The classical virus dynamics model is modified by incorporating an immune response with fixed or fluctuating vaccination frequencies and dosages to obtain a system of impulsive differential equations for the virus dynamics of both the wild-type and mutant strains.
Journal ArticleDOI
A spatial model of the efficiency of T cell search in the influenza-infected lung.
Drew Levin,Stephanie Forrest,Soumya Banerjee,Candice C. Clay,Judy L. Cannon,Melanie E. Moses,Frederick Koster,Frederick Koster +7 more
TL;DR: An agent based spatial model to focus on T cell migration from lymph nodes through the vascular system to sites of infection is developed and reveals unique challenges for T cell recruitment that are not apparent in standard differential equation models.
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Mathematical Modeling of Streptococcus pneumoniae Colonization, Invasive Infection and Treatment.
TL;DR: A mechanistic mathematical model describing dynamic interplays between Sp, immune cells, and epithelial tissues, where the host-pathogen interactions initiate serves as a mathematical framework that coherently explains various in vitro and in vitro studies, to which the model parameters were fitted.
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.
Journal ArticleDOI
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
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.
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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.