Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling

2016 ◽  
Vol 12 (3) ◽  
pp. 994-1005 ◽  
Author(s):  
Neha Chaudhary ◽  
Kristin Tøndel ◽  
Rakesh Bhatnagar ◽  
Vítor A. P. Martins dos Santos ◽  
Jacek Puchałka
Keyword(s):  
Monte Carlo ◽  
Distribution Pattern ◽  
Flux Distribution ◽  
Latin Hypercube Sampling ◽  
Monte Carlo Sampling ◽  
Latin Hypercube ◽  
Uniform Coverage ◽  
Flux Data ◽  
Genome Scale

Sampling of the optimal flux space using modified LHS gives a more uniform coverage than Monte-Carlo Sampling. Analysis of the flux data shows that majority of variation in the flux distribution pattern within the space arises due to the presence of few alternate pathways.

scholarly journals A comparison of latin hypercube sampling techniques for a supply chain network design problem

2018 ◽  
Vol 192 ◽  
pp. 01023
Author(s):  
Kantapit Kaewsuwan ◽  
Chumpol Yuangyai ◽  
Udom Janjarassuk ◽  
Kanokporn Rienkhemaniyom
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Latin Hypercube Sampling ◽  
Monte Carlo Sampling ◽  
Network Design Problem ◽  
Computational Time ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Latin Hypercube ◽  
Network Disruptions

Currently, supply chain network design becomes more complex. In designing a supply chain network to withstand changing events, it is necessary to consider the uncertainties and risks that cause network disruptions from unexpected events. The current research related to the designing problem considers network disruptions using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS) techniques. Both have a disadvantage that sample points or disruption locations are not scattered entirely sample space leading to high variation in objective function values. The purpose of this study is to apply a modified LHS or Improved Distributed Hypercube Sampling (IHS) techniques to reduce the variation. The results show that IHS techniques provide smaller standard deviation than that of the LHS technique. In addition, IHS can reduce not only the number of sample size but also and the computational time.

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