[1] The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world.
[4] About two-thirds of the land in British Columbia, Canada is covered by forests that are constantly being modified by natural disturbances such as fire, disease, and insect infestation.
The Beetle Agent follows a series of rules to decide where to fly within the forest and to select a healthy tree to attack, feed, and breed.
The MPB outbreaks end when the food supply decreases to the point that there is not enough to sustain the population or when climatic conditions become unfavorable for the beetle.
The area consisted primarily of Lodgepole pine with smaller proportions of Douglas fir and White spruce.
In the model, the invasive species can only react to their surroundings, while the importers and border enforcement agents are able to make their own decisions based on their own goals and objectives.
California is by far the largest producer of broccoli in the United States and so the concern and potential impact of an invasive species introduction through the chosen ports of entry is significant.
BehaviorSpace,[9] a software tool integrated with NetLogo, was used to test the effects of different parameters (e.g. shipment value, pretreatment cost) in the model.
Another interesting outcome of the model is that when inspectors were not able to learn to respond to an importer with previously infested shipments, damage to California broccoli crops was estimated to be $150 million.
However, when inspectors were able to increase inspection rates of importers with previous violations, damage to the California broccoli crops was reduced by approximately 12%.
Equally as important, the model provides policy makers and border control agencies with a tool that can be used to determine the best allocation of inspectional resources.
Reproduction by the Aphid agents is dependent on age, morphology, and daily minimum, maximum, and mean temperatures.
The study started the simulation run with an initial population of 10,000 alate aphids distributed across a grid of 25 meter cells.
However, they may also have harmful impacts such as the excessive growth of non-native plants or eutrophication of the lakes in which they live leading to anoxic conditions.
Given these possibilities, it is important to understand how the environment and other organisms affect the growth of these aquatic plants to allow mitigation or prevention of these harmful impacts.
One major difference in the two plants is that the latter reproduces through the use of very small seeds called oospores and bulbills which are spread via the flow of water.
Flow conditions, although not of high importance to Potamogeton pectinatus, directly impact the seed dispersal of Chara aspera.
The lake is under eutrophication stress which means that nutrients are not a limiting factor for either of the plant agents in the model.
iDynoMiCS can be used to seek to understand how individual microbial dynamics lead to emergent population- or biofilm-level properties and behaviours.
[15] An agent-based modelling paradigm was employed to make it possible to explore how each individual bacterium, of a particular species, contributes to the development of the biofilm.
[14] The study explores the hypothesis that the existence of diverse strategies of denitrification in an environment can be explained by solely assuming that faster response incurs a higher cost.
However, where faster switching incurs a higher cost, there is a strategy with optimal response time for any frequency of environmental fluctuations.
The model was originally the result of years of work by Laurent Lardon, Brian Merkey, and Jan-Ulrich Kreft, with code contributions from Joao Xavier.
With additional funding from the National Centre for Replacement, Refinement, and Reduction of Animals in Research (NC3Rs) in 2013, the development of iDynoMiCS as a tool for biological exploration continues apace, with new features being added when appropriate.
Additionally, epidemiology data show that children exposed to ionizing radiation have a substantially greater breast cancer risk than adults.
Simulations were first run on the Lawrence Berkeley National Laboratory Lawrencium supercomputer to parameterize and benchmark the model against a variety of in vivo mammary gland measurements.
The model was then used to test the three different mechanisms to determine which one led to simulation results that matched in vivo experiments the best.
This latter prediction, however, contradicted the in vivo data; irradiation of adult mammary glands did not lead to increased stem cell frequency.
The combination of the two agent-based models and the in vitro/in vivo experiments provide insight into why children exposed to ionizing radiation have a substantially greater breast cancer risk than adults.
Together, they support the hypothesis that the breast is susceptible to a transient increase in stem cell self-renewal when exposed to radiation during puberty, which primes the adult tissue to develop cancer decades later.