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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Mathematical modeling of the COVID-19 pandemic with intervention strategies.
TL;DR: In this article, the authors proposed and extended classical SEIR compartment model refined by contact tracing and hospitalization strategies to explain the COVID-19 outbreak and calibrated their model with daily COVID19 data for the five provinces of India namely, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal and the overall India.
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Optimal Control Analysis of a Mathematical Model for Breast Cancer
TL;DR: A mathematical model of breast cancer governed by a system of ordinary differential equations in the presence of chemotherapy treatment and ketogenic diet is discussed and optimal control theory is applied to discover the optimal drug adjustment as an input control of the system therapies.
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The switching role of β-adrenergic receptor signalling in cell survival or death decision of cardiomyocytes.
Sung-Young Shin,Taeyong Kim,Ho-Sung Lee,Jun Hyuk Kang,Ji Young Lee,Kwang-Hyun Cho,Do Han Kim +6 more
TL;DR: It is discovered that the cell fate of cardiomyocytes switches from survival to death with the increase of β-adrenergic receptor (β-AR) stimulation, and β1-blocker treatment increases the survival effect of α-AR stimuli through the regulation of Bcl-2 expression leading to the resistance to cell death, providing new insight into the mechanism of therapeutic effects.
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Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory
TL;DR: In this article, the authors developed a spatially-explicit agent-based model for summer breeding, non-migratory female monarch butterfly movement and egg-laying on an Iowa, USA landscape.
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Forecasting COVID-19 pandemic: A data-driven analysis
TL;DR: A tentative prediction of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time is given by using a Trust-region-reflective (TRR) algorithm which one of the well-known real data fitting techniques.
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
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.