[Population Modeling] PopModWkGrpIMAG-news Digest, Vol 28, Issue 2

Fri Feb 10 20:38:51 PST 2017

Love it!!

Also noted that they call their work individual based modeling which intuitively sounds directly opposite POPMOD.  Yet I think it is a better name than PopMod 

"We use approach named Individual Based Modelling in which modeler tries 
> 
> to present population of living organisms as being composed of discrete 
> individual organisms."  

Computer builds a population of synthetic entities which can then serve a agents in models.

John

Typed with two thumbs on my iPhone.  (757) 318-0671 

“The biggest risk you run in life is having fifteen people sitting around a table all agreeing with everything you say.  If that’s the environment you work in, you’re going to blow up one day.”
    - Ken Langone, Founder of Home Depot

> On Feb 10, 2017, at 15:00, popmodwkgrpimag-news-request at simtk.org wrote:
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> Today's Topics:
> 
>   1. eVOLUTUS: the simulator of multiscale evolutionary processes
>      tested on Foraminifera (Pawel Topa)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Fri, 10 Feb 2017 10:48:55 +0100
> From: Pawel Topa <topa at agh.edu.pl>
> To: popmodwkgrpimag-news at simtk.org
> Subject: [Population Modeling] eVOLUTUS: the simulator of multiscale
>    evolutionary processes tested on Foraminifera
> Message-ID: <3d1b47fb-9a73-1559-4485-1c5f0996d7d8 at agh.edu.pl>
> Content-Type: text/plain; charset="utf-8"; Format="flowed"
> 
> Dear members of Population Modeling Working Group,
> 
> let me introduce our project named "eVOLUTUS: the simulator of 
> multiscale evolutionary processes tested on Foraminifera":
> 
> 
> Our goal is to design a new algorithmic framework for testing and 
> simulating evolutionary principles and their consequences in defined 
> environments at various spatiotemporal scales. Due to biological 
> complexity of organisms and their interactions with the environment, it 
> has never been attempted to design such a numerical framework for the 
> simulation of evolutionary patterns derived from the fossil record of 
> real organisms. As a model organism we will implement Foraminifera, 
> single-celled eukaryotes that occupy marine benthic and pelagic zones 
> throughout the world andhave an extraordinary fossil record throughout 
> the Phanerozoic. This makes them an ideal model often used for testing 
> general evolutionary hypotheses.  Recent studies have shown that shell 
> architecture and most prominent morphogenetic trends in shell 
> development distinctly follow molecular phylogenetic patterns.
> 
> We use approach named Individual Based Modelling in which modeler tries 
> to present population of living organisms as being composed of discrete 
> individual organisms. The model that uses IBM in natural way can be 
> implemented using Agent-based Modeling computational paradigm. In order 
> to model evolutionary processes we use the well known genetic operators: 
> mutation and crossing over which are applied to the virtual genome 
> consisted of parameters that govern agent (foraminifers) behavior. 
> Selection is simply made by adaptation to environmental conditions in 
> virtual habitat.
> 
> References:
> 1) Pawe? Topa, ?ukasz Faber, Jaros?aw Tyszka, Maciej Komosinski, 
> Modelling ecology and evolution of Foraminifera in the agent-oriented 
> distributed platform, Journal of Computational Science, Volume 18, 
> January 2017, Pages 69-84, ISSN 1877-7503, 
> http://dx.doi.org/10.1016/j.jocs.2016.07.009.
> (//www.sciencedirect.com/science/article/pii/S1877750316301168)
> Abstract: Abstract
> We present a new software platform called eVolutus for simulating 
> evolution of living organisms. We choose Foraminifera as model organisms 
> that represent a group of single-cellular, mainly marine, organisms that 
> construct well fossilisable protective shells. They have lived on Earth 
> for more than 540 million years and have left an extraordinary fossil 
> record that is excellent for testing palaeoecological and evolutionary 
> hypotheses. We use the AgE platform, which is a lightweight 
> agent-oriented platform supporting distributed computation. The paper 
> presents the general architecture of this modelling environment as well 
> as more detailed descriptions of the implemented rules and applied 
> solutions. The utility of this software is demonstrated by presenting 
> the configuration and results of sample experiments.
> Keywords: Agent-based modelling; Artificial Life; Multi-agent systems; 
> Foraminifera
> 
> 2) Maciej Komosinski, Agnieszka Mensfelt, Jaros?aw Tyszka, Jan Gole?, 
> Multi-agent simulation of benthic foraminifera response to annual 
> variability of feeding fluxes, Journal of Computational Science, 
> Available online 3 October 2016, ISSN 1877-7503, 
> http://dx.doi.org/10.1016/j.jocs.2016.09.009.
> (//www.sciencedirect.com/science/article/pii/S1877750316301715)
> Abstract: Abstract
> In this work we describe a novel simulation model of foraminifera and 
> their microhabitat. The simulations reported here are focused on the 
> response of foraminiferal populations to environmental feeding fluxes. 
> The experiments allowed to calibrate the model and to simulate realistic 
> population patterns known from culture experiments, as well as from 
> oceanographic and paleoecologic studies. Variability of annual food flux 
> has a direct impact on productivity of foraminifera: population sizes 
> closely follow the intensity of constant and seasonal food fluxes in 
> both scenarios. This correlation between the food influx and population 
> size is interpreted as the consequence of changing the carrying capacity 
> of the system. Seasonal pulses of particulate organic matter enhance the 
> population size which is represented by a higher number of fossilized 
> shells. Our model offers a flexible experimental design to run 
> sophisticated in silico experiments. This approach reveals a novel 
> methodology for testing sensitivity of fossil and recent foraminiferal 
> assemblages to environmental changes. Furthermore, it facilitates 
> predictive applications for monitoring studies based on simulation of 
> various scenarios.
> Keywords: Foraminifera; Simulation; Population dynamics; Life cycles; 
> Seasonality
> 
>> 2. Categorize your modeling approach by research area. Keywords we 
>> used in the past are:
>> Managing disease spread
>> Resource planning and allocation
>> Predicting drug effects
>> Risk assessment
>> Ecosystem management
>> Testing theory
>> Epidemiology and public health
> Individual based modeling.
> 
> 
>> 
>> 3. Add keywords for computational methods you used. Examples include 
>> Agent Based Models, differential equations, Monte-Carlo simulation, 
>> yet there are many more options that you can find in: Table 1 in: 
>> https://simtk.org/docman/view.php/962/1963/SummerSim_2016_PopMod_Submit_2016_05_15_Robert_Smith.pdf
> * Agent Based Modeling
> * Evolutionary Computations
> 
> 
> Kind regards
> 
> Pawe? Topa
> Jarek Tyszka
> Maciek Komosinski
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