scholarly journals The domino shuffling algorithm and Anisotropic KPZ stochastic growth

2021 ◽  
Vol 4 ◽  
pp. 1005-1034
Author(s):  
Sunil Chhita ◽  
Fabio Toninelli
Keyword(s):  
Stochastic Growth

Recursive Utility in a Markov Environment with Stochastic Growth

2012 ◽  
Author(s):  
Lars Peter Hansen ◽  
Jose A. Scheinkman
Keyword(s):  
Recursive Utility ◽  
Stochastic Growth

Application of growth rate from kinetic model to calculate stochastic growth of a bacteria population at low contamination level

2021 ◽  
pp. 110758
Author(s):  
Kento Koyama ◽  
Satoko Hiura ◽  
Hiroki Abe ◽  
Shige Koseki
Keyword(s):  
Growth Rate ◽  
Kinetic Model ◽  
Contamination Level ◽  
Stochastic Growth

scholarly journals Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells

2013 ◽  
Vol 79 (7) ◽  
pp. 2294-2301 ◽  
Author(s):  
Konstantinos P. Koutsoumanis ◽  
Alexandra Lianou
Keyword(s):  
Kinetic Parameters ◽  
Single Cells ◽  
Growth Curves ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Initial Population ◽  
Bacterial Cells ◽  
Stochastic Growth ◽  
Bacterial Populations ◽  
Content Type

ABSTRACTConventional bacterial growth studies rely on large bacterial populations without considering the individual cells. Individual cells, however, can exhibit marked behavioral heterogeneity. Here, we present experimental observations on the colonial growth of 220 individual cells ofSalmonella entericaserotype Typhimurium using time-lapse microscopy videos. We found a highly heterogeneous behavior. Some cells did not grow, showing filamentation or lysis before division. Cells that were able to grow and form microcolonies showed highly diverse growth dynamics. The quality of the videos allowed for counting the cells over time and estimating the kinetic parameters lag time (λ) and maximum specific growth rate (μmax) for each microcolony originating from a single cell. To interpret the observations, the variability of the kinetic parameters was characterized using appropriate probability distributions and introduced to a stochastic model that allows for taking into account heterogeneity using Monte Carlo simulation. The model provides stochastic growth curves demonstrating that growth of single cells or small microbial populations is a pool of events each one of which has its own probability to occur. Simulations of the model illustrated how the apparent variability in population growth gradually decreases with increasing initial population size (N0). For bacterial populations withN0of >100 cells, the variability is almost eliminated and the system seems to behave deterministically, even though the underlying law is stochastic. We also used the model to demonstrate the effect of the presence and extent of a nongrowing population fraction on the stochastic growth of bacterial populations.

A nonlinear stochastic growth model on discrete time domains

2016 ◽  
Vol 22 (11) ◽  
pp. 1732-1746 ◽  
Author(s):  
Ferhan M. Atıcı ◽  
Gang Cheng ◽  
Alex Lebedinsky
Keyword(s):  
Growth Model ◽  
Discrete Time ◽  
Stochastic Growth ◽  
Stochastic Growth Model ◽  
Time Domains

scholarly journals Universal fractality of morphological transitions in stochastic growth processes

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
J. R. Nicolás-Carlock ◽  
J. L. Carrillo-Estrada ◽  
V. Dossetti
Keyword(s):  
Growth Processes ◽  
Stochastic Growth ◽  
Morphological Transitions

scholarly journals Competitive stochastic growth model for the 3D morphology of eutectic Si in Al–Si alloys

2013 ◽  
Vol 69 ◽  
pp. 289-298 ◽  
Author(s):  
Gerd Gaiselmann ◽  
Ole Stenzel ◽  
Anastasia Kruglova ◽  
Frank Muecklich ◽  
Volker Schmidt
Keyword(s):  
Growth Model ◽  
Stochastic Growth ◽  
Stochastic Growth Model ◽  
3D Morphology ◽  
Eutectic Si

Bio-inspired Stochastic Growth and Initialization for Artificial Neural Networks

2019 ◽  
pp. 88-100
Author(s):  
Kevin Dai ◽  
Amir Barati Farimani ◽  
Victoria A. Webster-Wood
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Growth ◽  
Artificial Neural
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