Evolution of innate behavioral strategies through competitive population dynamics

Mapping Intimacies ◽

10.1101/2021.06.24.449791 ◽

2021 ◽

Author(s):

Tong Liang ◽

Braden A. W. Brinkman

Keyword(s):

Objective Function ◽

Mean Field ◽

Stochastic Simulations ◽

Random Environments ◽

Behavioral Strategies ◽

Mean Field Model ◽

Agent Based ◽

High Resource ◽

Competitive Phenotypes ◽

Exploration Exploitation

Many organism behaviors are innate or instinctual and have been "hard-coded" through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a population of organisms in silico. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the existence of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.

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COMPETITION AND PREDATION IN A THREE SPECIES MODEL

International Journal of Modern Physics C ◽

10.1142/s0129183106010078 ◽

2006 ◽

Vol 17 (11) ◽

pp. 1629-1645 ◽

Cited By ~ 2

Author(s):

JANUSZ SZWABIǸSKI ◽

ANDRZEJ PȨKALSKI ◽

KAMIL TROJAN

Keyword(s):

Field Model ◽

Mean Field ◽

Mean Field Model ◽

Agent Based ◽

Interacting Species ◽

Stochastic Fluctuations ◽

The Mean ◽

Large Systems ◽

Access To Food ◽

Type Of Behavior

A model of dynamics of three interacting species is presented. Two of the species are prey and one is predator, which feeds on both prey, however with some preference to one type. Prey compete for space (breeding) although they always have access to food. Predators in order to survive and reproduce must catch prey, otherwise they die of hunger. The dynamics of the model is found via differential equations in the mean-field like approach and through computer simulations for agent-based method. We show that the coexistence of the three species is possible in the mean-field model, provided the preference of the predators is small, whereas from simulation it follows that the stochastic fluctuations drive, generally, one of the prey populations into extinction. We have found a different type of behavior for small and large systems and a rather unexpected dependence of the coexistence chance of the preference parameter in bigger lattices.

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Cluster mean-field theory accurately predicts statistical properties of large-scale DNA methylation patterns

10.1101/2021.09.03.458837 ◽

2021 ◽

Author(s):

Lyndsay Kerr ◽

Duncan Sproul ◽

Ramon Grima

Keyword(s):

Dna Methylation ◽

Large Scale ◽

Field Model ◽

Mean Field ◽

Statistical Properties ◽

Stochastic Simulations ◽

Model Parameters ◽

Mean Field Model ◽

Mean Field Models ◽

Methylation Patterns

The accurate establishment and maintenance of DNA methylation patterns is vital for mammalian development and disruption to these processes causes human disease. Our understanding of DNA methylation mechanisms has been facilitated by mathematical modelling, particularly stochastic simulations. Mega-base scale variation in DNA methylation patterns is observed in development, cancer and ageing and the mechanisms generating these patterns are little understood. However, the computational cost of stochastic simulations prevents them from modelling such large genomic regions. Here we test the utility of three different mean-field models to predict large-scale DNA methylation patterns. By comparison to stochastic simulations, we show that a cluster mean-field model accurately predicts the statistical properties of steady-state DNA methylation patterns, including the mean and variance of methylation levels calculated across a system of CpG sites, as well as the covariance and correlation of methylation levels between neighbouring sites. We also demonstrate that a cluster mean-field model can be used within an approximate Bayesian computation framework to accurately infer model parameters from data. As mean-field models can be solved numerically in a few seconds, our work demonstrates their utility for understanding the processes underpinning large-scale DNA methylation patterns.

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Path Integral Method in the Mean-field Model for the Magnetic Vector Potential

Geomagnetism and Aeronomy ◽

10.1134/s0016793220070300 ◽

2020 ◽

Vol 60 (7) ◽

pp. 989-992

Author(s):

E. V. Yushkov ◽

C. R. Kamaletdinov ◽

D. D. Sokoloff

Keyword(s):

Path Integral ◽

Vector Potential ◽

Integral Method ◽

Field Model ◽

Mean Field ◽

Magnetic Vector Potential ◽

Magnetic Vector ◽

Mean Field Model ◽

The Mean ◽

Path Integral Method

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KDP-type crystal spontaneous polarization behavior better explained by bound charge semiconductor model than by mean field model

Ferroelectrics ◽

10.1080/00150193.2020.1791663 ◽

2020 ◽

Vol 569 (1) ◽

pp. 70-81

Author(s):

V. Hugo Schmidt

Keyword(s):

Spontaneous Polarization ◽

Field Model ◽

Mean Field ◽

Mean Field Model ◽

Polarization Behavior ◽

Type Crystal

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On the Throughput Optimization in Large-Scale Batch-Processing Systems

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3453953.3453982 ◽

2021 ◽

Vol 48 (3) ◽

pp. 128-129

Author(s):

Sounak Kar ◽

Robin Rehrmann ◽

Arpan Mukhopadhyay ◽

Bastian Alt ◽

Florin Ciucu ◽

...

Keyword(s):

Large Scale ◽

Mean Field ◽

Queueing Network ◽

Processing System ◽

Batch Size ◽

System Throughput ◽

Throughput Optimization ◽

Mean Field Model ◽

Optimal Batch Size ◽

Globally Attractive

We analyze a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function that arises due to overhead amortization. In practice, throughput optimization relies on numerical searches for the optimal batch size which is computationally cumbersome. In this paper, we model this system in terms of a closed queueing network assuming certain forms of service speedup; a standard Markovian analysis yields the optimal throughput in w n4 time. Our main contribution is a mean-field model that has a unique, globally attractive stationary point, derivable in closed form. This point characterizes the asymptotic throughput as a function of the batch size that can be calculated in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

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Mean-Field Models for EEG/MEG: From Oscillations to Waves

Brain Topography ◽

10.1007/s10548-021-00842-4 ◽

2021 ◽

Author(s):

Áine Byrne ◽

James Ross ◽

Rachel Nicks ◽

Stephen Coombes

Keyword(s):

Gap Junction ◽

Large Scale ◽

Mean Field ◽

Coarse Grained ◽

Neural Mass Model ◽

Neuron Network ◽

Mass Model ◽

Mean Field Model ◽

Neural Mass ◽

The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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Quark mean-field model for single and double and hypernuclei

Progress of Theoretical and Experimental Physics ◽

10.1093/ptep/ptt119 ◽

2014 ◽

Vol 2014 (1) ◽

pp. 13D02-0 ◽

Cited By ~ 11

Author(s):

J. N. Hu ◽

A. Li ◽

H. Shen ◽

H. Toki

Keyword(s):

Field Model ◽

Mean Field ◽

Mean Field Model

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EVOLUTION OF SHELL STRUCTURE IN NUCLEI

International Journal of Modern Physics E ◽

10.1142/s0218301311019581 ◽

2011 ◽

Vol 20 (08) ◽

pp. 1663-1675 ◽

Cited By ~ 4

Author(s):

A. BHAGWAT ◽

Y. K. GAMBHIR

Keyword(s):

Shell Structure ◽

Crucial Role ◽

Field Model ◽

Mean Field ◽

Mass Region ◽

The Other ◽

Mean Field Model ◽

Relativistic Mean Field ◽

Shell Closures ◽

Low Mass

Systematic investigations of the pairing and two-neutron separation energies which play a crucial role in the evolution of shell structure in nuclei, are carried out within the framework of relativistic mean-field model. The shell closures are found to be robust, as expected, up to the lead region. New shell closures appear in low mass region. In the superheavy region, on the other hand, it is found that the shell closures are not as robust, and they depend on the particular combinations of neutron and proton numbers. Effect of deformation on the shell structure is found to be marginal.

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