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Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system.〔An G., Mi Q., Dutta-Moscato J., Vodovotz Y. (2009). Agent-based models in translational systems biology. Systems Biology and Medicine, 1(September/October), 159-171. 〕 The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior.〔 Several of the characteristics of agent-based models important to biological studies include: # Modular structure: The behavior of an agent-based model is defined by the rules of its agents. Existing agent rules can be modified or new agents can be added without having to modify the entire model. # Emergent properties: Through the use of the individual agents that interact locally with rules of behavior, agent-based models result in a synergy that leads to a higher level whole with much more intricate behavior than those of each individual agent.〔Politopoulos, I. (11 September 2007). Review and Analysis of Agent-based Models in Biology. Retrieved from http://www.csc.liv.ac.uk/research/techreports/tr2007/ulcs-07-021.pdf〕 # Abstraction: Either by excluding non-essential details or when details are not available, agent-based models can be constructed in the absence of complete knowledge of the system under study. This allows the model to be as simple and verifiable as possible.〔 # Stochasticity: Biological systems exhibit behavior that appears to be random. The probability of a particular behavior can be determined for a system as a whole and then be translated into rules for the individual agents.〔 Before the agent-based model can be developed, one must choose the appropriate software or modeling toolkit to be used. Madey and Nikolai provide an extensive list of toolkits in their paper "Tools of the Trade: A Survey of Various Agent Based Modeling Platforms".〔Madey, G. & Nikolai, C. (2009). Tools of the Trade: A Survey of Various Agent Based Modeling Platforms. Journal of Artificial Societies and Social Simulation, 12(2). Retrieved from http://jasss.soc.surrey.ac.uk/12/2/2/2.pdf〕 The paper seeks to provide users with a method of choosing a suitable toolkit by examining five characteristics across the spectrum of toolkits: the programming language required to create the model, the required operating system, availability of user support, the software license type, and the intended toolkit domain. Some of the more commonly used toolkits include (Swarm ), NetLogo, RePast, and (Mason ). Listed below are summaries of several articles describing agent-based models that have been employed in biological studies. The summaries will provide a description of the problem space, an overview of the agent-based model and the agents involved, and a brief discussion of the model results. ==Forest insect infestations== In the paper titled "Exploring Forest Management Practices Using an Agent-Based Model of Forest Insect Infestations", an agent-based model was developed to simulate attack behavior of the Mountain Pine Beetle, ''Dendroctonus ponderosae'', (MPB) in order to evaluate how different harvesting policies influence spatial characteristics of the forest and spatial propagation of the MPB infestation over time.〔Perez, L., & Dragucevic, S. (2010). Exploring Forest Management Practices Using and Agent-Based Model of Forest Insect Infestations. International Environmental Modeling and Software Society (iEMSs) 2010 International Congress on Environmental Modeling and Software, Ottawa, Canada.〕 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. Forest resources make up approximately 15% of the province’s economy, so infestations caused by insects such as the MPB can have significant impacts on the economy. The MPB outbreaks are considered a major natural disturbance that can result in widespread mortality of the Lodgepole pine tree, one of the most abundant commercial tree species in British Columbia. Insect outbreaks have resulted in the death of trees over areas of several thousand square kilometers. The agent-based model developed for this study was designed to simulate the MPB attack behavior in order to evaluate how management practices influence the spatial distribution and patterns of insect population and their preferences for attacked and killed trees. Three management strategies were considered by the model: 1) no management, 2) sanitation harvest and 3) salvage harvest. In the model, the Beetle Agent represented the MPB behavior; the Pine Agent represented the forest environment and tree health evolution; the Forest Management Agent represented the different management strategies. 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 typically kills host trees in its natural environment in order to successfully reproduce. The beetle larvae feed on the inner bark of mature host trees, eventually killing them. In order for the beetles to reproduce, the host tree must be sufficiently large and have thick inner bark. 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 Pine Agent simulates the resistance of the host tree, specifically the Lodgepole pine tree, and monitors the state and attributes of each stand of trees. At some point in the MPB attack, the number of beetles per tree reaches the host tree capacity. When this point is reached, the beetles release a chemical to direct beetles to attack other trees. The Pine Agent models this behavior by calculating the beetle population density per stand and passes the information to the Beetle Agents. The Forest Management Agent was used, at the stand level, to simulate two common silviculture practices (sanitation and salvage) as well as the strategy where no management practice was employed. With the sanitation harvest strategy, if a stand has an infestation rate greater than a set threshold, the stand is removed as well as any healthy neighbor stand when the average size of the trees exceeded a set threshold. For the salvage harvest strategy, a stand is removed even it is not under a MPB attack if a predetermined number of neighboring stands are under a MPB attack. The study considered a forested area in the North-Central Interior of British Columbia of approximately 560 hectare. The area consisted primarily of Lodgepole pine with smaller proportions of Douglas fir and White spruce. The model was executed for five time steps, each step representing a single year. Thirty simulation runs were conducted for each forest management strategy considered. The results of the simulation showed that when no management strategy was employed, the highest overall MPB infestation occurred. The results also showed that the salvage forest management technique resulted in a 25% reduction in the number of forest strands killed by the MPB, as opposed to a 19% reduction by the salvage forest management strategy. In summary, the results show that the model can be used as a tool to build forest management policies. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Agent-based model in biology」の詳細全文を読む スポンサード リンク
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