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slimemold.simple

Please note

The simulation needs a Java-Plugin (1.3.x) installed for your internet browser. If you do not already have one installed, the browser will prompt you to download the Plugin from “Sun”, who is the inventor of Java. Please download the JRE (=Java runtime environment) into a directory on your computer (e.g. “c:\temp”), execute the downloaded file for installation on your system (double-click on the file). Afterwards you will be able to reload the simulation page. Maybe you will have to restart your browser to succeed.

Run the simulation

Please click here.

Description of the simulation

This simulation is a quite historical aproach. It simulates a group of amoebes, that move around randomly. They emit a certain portion of cAMP, which diffuses in their environment and evaporates after some time. The levels of cAMP in each individuals’ environment influences the moving direction of the cells, they always move uphill.

Parameters

  • The slider number regulates the number of simulated amoebes
  • The sliders wiggle-bias and wiggle-angle regulate the basic movements performed in the random walk
  • The slider sniff-threshold determines the minimum amount of cAMP that can be detected by the cells in their surrounding.
  • The slider sniff-angle determines the direction of their “cAMP-sensors”.

Output

The plot in the lower fraction shows the result of the aggregation process.

Experiments

  • Change the number of slime mold cells and see what happens.
  • Test for the effect of the evaporation rate and of the diffusion rate.

Weak points of the model

As written before, this model provides a very simple approach. It does not simulate the release of the cAMP in a natural way. Instead of sudden cAMP-bursts produced by cells, itaims a steady cAMP release. Also the moving speed of the cAMP in contrast to the moving speed of the cells are not correct in their ratios.

Screenshot

Implementation

This model was adapted from the MIT Media Lab slime model. See Resnick, M. (1994) "Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds." Cambridge, Ma: MIT Press. Adapted to StarLogoT, 1997, as part of the Connected Mathematics Project.

This model was created as part of the project: CONNECTED MATHEMATICS: MAKING SENSE OF COMPLEX PHENOMENA THROUGH BUILDING OBJECT-BASED PARALLEL MODELS (OBPML). The project gratefully acknowledges the support of the National Science Foundation (Applications of Advanced Technologies Program) -- grant numbers RED #9552950 and REC #9632612. Copyright 1998 by Uri Wilensky. All rights reserved. Converted from StarLogoT to NetLogo, 2001.

To refer to this model in academic publications, please use: Wilensky, U. (1998).  NetLogo Slime model. http://ccl.northwestern.edu/netlogo/models/Slime. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Further readings

  • Camazine S., Deneubourg J.-L., Franks N.R., Sneyd J., Theraulaz G. and Bonabeau E. (2001) Self-Organization in biological systems. Princeton University Press.
  • Bonabeau E., Dorigo M. and Theraulaz G. (1999) Swarm intelligence. From natural to artificial systems. Santa Fee Institute studies in the sciences of complexity. Oxford University Press.
  • Resnick M. (2000) Turtles, termites and traffic jams. MIT Press.

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