Constant number Monte Carlo simulation of population balances with multiple growth mechanisms

Khalili, Samira; Lin, Yulan; Armaou, Antonios; Matsoukas, Themis

doi:10.1002/aic.12233

Cited by 29 publications

(11 citation statements)

References 27 publications

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“…Namely, the time delay of mutation to percolate through the PF gene circuit can affect the outcome of the PF cell population (see SI Section 3.3). The fine-grain PDA accounts for the processes of probabilistic cell division, stochastic switching of phenotypes, and uses a constant-number Monte Carlo technique to accurately simulate the statistics of an exponentially growing cell population (Charlebois et al, 2011b; Khalili et al, 2010; Mantzaris, 2006; Mantzaris, 2007). Evolutionary dynamics are generally assumed to push the population towards a peak in the fitness landscape [for a review see (de Visser & Krug, 2014)].…”

Section: Resultsmentioning

confidence: 99%

Backward evolution from gene network dynamics

Belete

Charlebois

Balázsi

2018

Preprint

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Gene expression is controlled by regulator genes that together with effector genes form gene regulatory networks. How mutation in the genes comprising gene regulatory networks influences cell population dynamics has not been adequately investigated. In this study, we develop mathematical models to study how a mutation in a regulator gene that reaches the effector gene with a time delay affects short-term and long-term population growth. Using theory and experiment, we find a paradoxical outcome of evolution where a mutation in a regulator gene leads to an interaction between gene regulatory network and population dynamics, causing in certain cases a permanent decrease in population fitness in a constant environment. Significance Statement:The properties of a cell are largely the products of its proteins, synthesized at rates depending on the regulation of protein coding genes. Single-cell measurements show that genetically identical cells can differ radically in their protein levels, partially due to the random production and degradation of proteins. It is currently unknown how mutants arise and spread in populations affected by such biological variability. We use computer simulations and evolution experiments to study how a mutant spreads in a population that carries a synthetic drug resistance gene network. Our results show for the first time a paradoxical outcome of evolution, where an initially beneficial mutation can interact with gene regulatory network dynamics and cause a permanent decrease in population fitness in the same environment.3

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Section: Resultsmentioning

confidence: 99%

Backward evolution from gene network dynamics

Belete

Charlebois

Balázsi

2018

Preprint

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“…Numerous studies have investigated the evolution of aerosol size distributions using MC methods that have shown the capabilities of MC approach to approximate aerosol growth processes. Generally, MC methods can be divided into two classes: Constant‐Volume ( Constant‐V ) and Constant‐Number ( Constant‐N ).…”

Section: Kmc Model Developmentmentioning

confidence: 99%

Kinetic Monte Carlo simulation for homogeneous nucleation of metal nanoparticles during vapor phase synthesis

Davari

Mukherjee

2017

AIChE Journal

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We present a free-energy driven kinetic Monte Carlo model to simulate homogeneous nucleation of metal nanoparticles (NPs) from vapor phase. The model accounts for monomer-cluster condensations, cluster-cluster collisions, and cluster evaporations simultaneously. Specifically, we investigate the homogeneous nucleation of Al NPs starting with different initial background temperatures. Our results indicate good agreement with earlier phenomenological studies using the Gibbs' free energy formulation from Classical Nucleation Theory (CNT). Furthermore, nucleation rates for various clusters are calculated through direct cluster observations. The steady-state nucleation rate estimated using two different approaches namely, the Yasuoka-Matsumoto (YM) and mean first passage time (MFPT) methods indicate excellent agreement with each other. Finally, our simulation results depict the expected increase in the entropy of mixing as clusters approach the nucleation barrier, followed by its subsequent drastic loss after the critical cluster formation resulting from first-order phase transitions.In the first stage (S1), nucleation rates are approximately constant, while in the second stage (S2), generation of new nuclei stops.

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“…Stochastic simulations were based on Equation 1, which we considered as a stochastic processes, and simulated using a Constant-Number Monte Carlo approach [21][22][23][24]. Initially, we assigned N 0 = 10 4 cells equally distributed in the two states from a twovalued distribution.…”

Section: B Stochastic Simulationsmentioning

confidence: 99%

Optimality and adaptation of phenotypically switching cells in fluctuating environments

Belete

Balázsi

2015

Phys. Rev. E

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Stochastic switching between alternative phenotypic states is a common cellular survival strategy during unforeseen environmental fluctuations. Cells can switch between different subpopulations that proliferate at different rates in different environments. Optimal population growth is typically assumed to occur when phenotypic switching rates match environmental switching rates. However, it is not well understood how this optimum behaves as a function of the growth rates of phenotypically different cells. In this study, we use mathematical and computational models to test how the actual parameters associated with optimal population growth differ from those assumed to be optimal. We find that the predicted optimum is practically always valid if the environmental durations are long. However, the regime of validity narrows as environmental durations shorten, especially if subpopulation growth rate differences differ from each other (are asymmetric) in two environments. Furthermore, we study the fate of mutants with switching rates previously predicted to be optimal. We find that mutants which match their phenotypic switching rates with the environmental ones can only sweep the population if the assumed optimum is valid, but not otherwise.

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Constant number Monte Carlo simulation of population balances with multiple growth mechanisms

Cited by 29 publications

References 27 publications

Backward evolution from gene network dynamics

Backward evolution from gene network dynamics

Kinetic Monte Carlo simulation for homogeneous nucleation of metal nanoparticles during vapor phase synthesis

Optimality and adaptation of phenotypically switching cells in fluctuating environments

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