![]() These in silico (or dry-laboratory) experiments are of course complementary to traditional wet-laboratory experimental approaches. Once developed and validated, models can be adapted in different ways (e.g., inputs can be altered to mimic different environments) to enable examination of different qualities. Moreover, it allowed experiments and/or measurements that cannot be easily achievable in a laboratory environment. This approach has helped the generation of novel insights and hypotheses for further research and development, with a considerable saving in terms of time and costs. IntroductionĬomplex biological scenarios have been recently investigated with the synergic union between computational modeling and high-throughput experimental data. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms. It is designed for both research and education and is used across a wide range of disciplines and education levels. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. They have shown the ability to see clearly and intuitively into the nature of immunological processes. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. This behavior is unpredictable, as it does not follow linear rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. Same code as above, but this time make 75% blue and 25% redĪsk n-of (0.75 *(item 0 popu1 / 10)) patches with ]]Īsk n-of (0.25 *(item 0 popu1 / 10)) patches with ]]Īsk n-of (0.Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Same code as above, but this time make 90% blue and 10% redĪsk n-of (0.9 *(item 0 popu1 / 10)) patches with ]]Īsk n-of (0.1 *(item 0 popu1 / 10)) patches with ]] Same code as above, but this time make 60% blue and 40% redĪsk n-of (0.6 *(item 0 popu1 / 10)) patches with ]]Īsk n-of (0.4 *(item 0 popu1 / 10)) patches with ]] Make 50% of the turtles red and the remaining 50% blue.Īsk n-of (0.5 *(item 0 popu1 / 10)) patches with [ Find the population and initial colour of all of the patches inside this polygon Gis:set-drawing-color 45 gis:fill feature 2.0Īsk patches with If gis:property-value feature "SOC" = "YELLOW" [ Gis:set-drawing-color 99 gis:fill feature 2.0 If gis:property-value feature "SOC" = "BLACK" [ Gis:set-drawing-color 7 gis:fill feature 2.0 If gis:property-value feature "SOC" = "GREY" [ Gis:set-drawing-color 95 gis:fill feature 2.0 If gis:property-value feature "SOC" = "BLUE" [ Gis:set-drawing-color 15 gis:fill feature 2.0 If gis:property-value feature "SOC" = "RED" [ use this line to verify if we get the right neighbors Set myneighbors (patch-set myneighbors patches with ) Set myneighbors n-of 0 patches empty agentsetĪsk patches with [ Please read NETLOGO FEATURES in info tab for more information. Find neighbors using a txt file produced by ArcGIS Polygon Neighbors. This is the code section I believe controls the initial population/location in the "segregation" model with some modifications. However, I am unable to get the model to do little else. It does appear the model was able to read the data to draw the polygons/map, separate the census tracts and apply the color modifiers I requested. Set popu gis:property-value feature "POP65UP" Let center-point gis:location-of gis:centroid-of featureĪsk patch item 0 center-point item 1 center-point [ each polygon identifies a patch at its centroid, which records the colorįoreach gis:feature-list-of city-dataset [ Gis:apply-coverage city-dataset "DISTRICT" DISTRICT Gis:apply-coverage city-dataset "SOC" mycolor copy the color information to patches (converting vector to raster) Gis:set-world-envelope gis:envelope-of city-dataset Set city-dataset gis:load-dataset "data/LA_Harbor_Region.shp" Gis:load-coordinate-system "data/LA_Harbor_Region.prj" Currently, I've modified an existing model and have been able to add my polygons (and associated attribute information) by copying the code from the provided "segregation" model in the NETlogo library. I'm hoping I can limit the "agents" to these polygons and base the number of them off of my population data. I'm working on a simple virus model in NETlogo, and attempting to integrate spatial data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |