Agent-based model in biology

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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 matrix . [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. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior. [1]

Contents

Characteristics

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. [2]

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. [1]

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. [1] [3]

Modelling different species behaviour

In an ecological context, agent-based modeling can be used to model the behaviour of different species such as insects infestations, [4] other invasive species, [5] aphids, [6] aquatic populations, [7] and the evolution of innate foraging behaviors. [8]

Forest insect infestations

Agent-based modeling has been used to simulate attack behavior of the mountain pine beetle (MPB), Dendroctonus ponderosae, in order to evaluate how different harvesting policies influence spatial characteristics of the forest and spatial propagation of the MPB infestation over time. [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. 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 harvest 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 sanitation harvest management strategy. In summary, the results show that the model can be used as a tool to build forest management policies.

Invasive species

Invasive species refers to "non-native" plants and animals that adversely affect the environments they invade. The introduction of invasive species may have environmental, economic, and ecological implications. An agent-based model can developed to evaluate the impacts of port-specific and importer-specific enforcement regimes for a given agricultural commodity that presents invasive species risk with the goal of improving the allocation of enforcement resources and to provide a tool to policy makers to answer further questions concerning border enforcement and invasive species risk. [5]

The agent-based model developed for the study considered three types of agents: invasive species, importers, and border enforcement agents. 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. The invasive species has the ability to determine if it has been released in an area containing the target crop, and to spread to adjacent plots of the target crop. The model incorporates spatial probability maps that are used to determine if an invasive species becomes established. The study focused on shipments of broccoli from Mexico into California through the ports of entry Calexico, California and Otay Mesa, California. The selected invasive species of concern was the crucifer flea beetle (Phyllotreta cruciferae). 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. The model also incorporated a spatially explicit damage function that was used to model invasive species damage in a realistic manner. Agent-based modeling provides the ability to analyze the behavior of heterogeneous actors, so three different types of importers were considered that differed in terms of commodity infection rates (high, medium, and low), pretreatment choice, and cost of transportation to the ports. The model gave predictions on inspection rates for each port of entry and importer and determined the success rate of border agent inspection, not only for each port and importer but also for each potential level of pretreatment (no pretreatment, level one, level two, and level three).

The model was implemented and ran in NetLogo, version 3.1.5. Spatial information on the location of the ports of entry, major highways, and transportation routes was included in the analysis as well as a map of California broccoli crops layered with invasive species establishment probability maps. 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. On average, 100 iterations were calculated at each level of the parameter being used, where an iteration represented a one-year run.

The results of the model showed that as inspection efforts increase, importers increase due care, or the pretreatment of shipments, and the total monetary loss of California crops decreases. The model showed that importers respond to an increase in inspection effort in different ways. Some importers responded to increased inspection rate by increasing pretreatment effort, while others chose to avoid shipping to a specific port, or shopped for another port. An important result of the model results is that it can show or provide recommendations to policy makers about the point at which importers may start to shop for ports, such as the inspection rate at which port shopping is introduced and the importers associated with a certain level of pest risk or transportation cost are likely to make these changes. 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%. The model provides a mechanism to predict the introduction of invasive species from agricultural imports and their likely damage. 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.

Aphid population dynamics

An agent-based model can be used to study the population dynamics of the bird cherry-oat aphid, Rhopalosiphum padi. [6] The study was conducted in a five square kilometer region of North Yorkshire, a county located in the Yorkshire and the Humber region of England. The agent-based modeling method was chosen because of its focus on the behavior of the individual agents rather than the population as a whole. The authors propose that traditional models that focus on populations as a whole do not take into account the complexity of the concurrent interactions in ecosystems, such as reproduction and competition for resources which may have significant impacts on population trends. The agent-based modeling approach also allows modelers to create more generic and modular models that are more flexible and easier to maintain than modeling approaches that focus on the population as a whole. Other proposed advantages of agent-based models include realistic representation of a phenomenon of interest due to the interactions of a group of autonomous agents, and the capability to integrate quantitative variables, differential equations, and rule based behavior into the same model.

The model was implemented in the modeling toolkit Repast using the JAVA programming language. The model was run in daily time steps and focused on the autumn and winter seasons. Input data for the model included habitat data, daily minimum, maximum, and mean temperatures, and wind speed and direction. For the Aphid agents, age, position, and morphology (alate or apterous) were considered. Age ranged from 0.00 to 2.00, with 1.00 being the point at which the agent becomes an adult. Reproduction by the Aphid agents is dependent on age, morphology, and daily minimum, maximum, and mean temperatures. Once nymphs hatch, they remain in the same location as their parents. The morphology of the nymphs is related to population density and the nutrient quality of the aphid's food source. The model also considered mortality among the Aphid agents, which is dependent on age, temperatures, and quality of habitat. The speed at which an Aphid agent ages is determined by the daily minimum, maximum, and mean temperatures. The model considered movement of the Aphid agents to occur in two separate phases, a migratory phase and a foraging phase, both of which affect the overall population distribution.

The study started the simulation run with an initial population of 10,000 alate aphids distributed across a grid of 25 meter cells. The simulation results showed that there were two major population peaks, the first in early autumn due to an influx of alate immigrants and the second due to lower temperatures later in the year and a lack of immigrants. Ultimately, it is the goal of the researchers to adapt this model to simulate broader ecosystems and animal types.

Aquatic population dynamics

A model is proposed to study the population dynamics of two species of macrophytes. [7] Aquatic plants play a vital role in the ecosystems in which they live as they may provide shelter and food for other aquatic organisms. 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.

Potamogeton pectinatus is one of the aquatic plant agents in the model. It is an annual growth plant that absorbs nutrients from the soil and reproduces through root tubers and rhizomes. Reproduction of the plant is not impacted by water flow, but can be influenced by animals, other plants, and humans. The plant can grow up to two meters tall, which is a limiting condition because it can only grow in certain water depths, and most of its biomass is found at the top of the plant in order to capture the most sunlight possible. The second plant agent in the model is Chara aspera, also a rooted aquatic plant. 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. Chara aspera only grows up to 20 cm and requires very good light conditions as well as good water quality, all of which are limiting factors on the growth of the plant. Chara aspera has a higher growth rate than Potamogeton pectinatus but has a much shorter life span. The model also considered environmental and animal agents. Environmental agents considered included water flow, light penetration, and water depth. Flow conditions, although not of high importance to Potamogeton pectinatus, directly impact the seed dispersal of Chara aspera. Flow conditions affect the direction as well as the distance the seeds will be distributed. Light penetration strongly influences Chara aspera as it requires high water quality. Extinction coefficient (EC) is a measure of light penetration in water. As EC increases, the growth rate of Chara aspera decreases. Finally, depth is important to both species of plants. As water depth increases, the light penetration decreases making it difficult for either species to survive beyond certain depths.

The area of interest in the model was a lake in the Netherlands named Lake Veluwe. It is a relatively shallow lake with an average depth of 1.55 meters and covers about 30 square kilometers. The lake is under eutrophication stress which means that nutrients are not a limiting factor for either of the plant agents in the model. The initial position of the plant agents in the model was randomly determined. The model was implemented using Repast software package and was executed to simulate the growth and decay of the two different plant agents, taking into account the environmental agents previously discussed as well as interactions with other plant agents. The results of the model execution show that the population distribution of Chara aspera has a spatial pattern very similar to the GIS maps of observed distributions. The authors of the study conclude that the agent rules developed in the study are reasonable to simulate the spatial pattern of macrophyte growth in this particular lake.

Cell-based modeling

Agent-based modeling is increasingly used to model the behaviour of individual cells within a tissue. These models are divided into on- and off-lattice models with on-lattice models such as cellular automata and cellular potts model and off-lattice models such as center-based models, [10] vertex-based models, [11] immersed boundary method [12] models and models based on the subcellular element method. [13] Some examples of specific applications of cell-based modeling are:

Bacteria aggregation leading to biofilm formation

An agent-based model can be used model the colonisation of bacteria onto a surface, leading to the formation of biofilms. [14] The purpose of iDynoMiCS (individual-based Dynamics of Microbial Communities Simulator) is to simulate the growth of populations and communities of individual microbes (small unicellular organisms such as bacteria, archaea and protists) that compete for space and resources in biofilms immersed in aquatic environments. iDynoMiCS can be used to seek to understand how individual microbial dynamics lead to emergent population- or biofilm-level properties and behaviours. Examining such formations is important in soil and river studies, dental hygiene studies, infectious disease and medical implant related infection research, and for understanding biocorrosion. [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. The initial illustration of iDynoMiCS considered how environmentally fluctuating oxygen availability affects the diversity and composition of a community of denitrifying bacteria that induce the denitrification pathway under anoxic or low oxygen conditions. [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. The agent-based model suggests that if metabolic pathways can be switched without cost the faster the switching the better. However, where faster switching incurs a higher cost, there is a strategy with optimal response time for any frequency of environmental fluctuations. This suggests that different types of denitrifying strategies win in different biological environments. Since this introduction the applications of iDynoMiCS continues to increase: a recent exploration of the plasmid invasion in biofilms being one example. [16] This study explored the hypothesis that poor plasmid spread in biofilms is caused by a dependence of conjugation on the growth rate of the plasmid donor agent. Through simulation, the paper suggests that plasmid invasion into a resident biofilm is only limited when plasmid transfer depends on growth. Sensitivity analysis techniques were employed that suggests parameters relating to timing (lag before plasmid transfer between agents) and spatial reach are more important for plasmid invasion into a biofilm than the receiving agents growth rate or probability of segregational loss. Further examples that use iDynoMiCS continue to be published, including use of iDynoMiCS in modelling of a Pseudomonas aeruginosa biofilm with glucose substrate. [17]

iDynoMiCS has been developed by an international team of researchers in order to provide a common platform for further development of all individual-based models of microbial biofilms and such like. 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. From its inception, the team have committed to releasing iDynoMiCS as an open source platform, encouraging collaborators to develop additional functionality that can then be merged into the next stable release. IDynoMiCS has been implemented in the Java programming language, with MATLAB and R scripts provided to analyse results. Biofilm structures that are formed in simulation can be viewed as a movie using POV-Ray files that are generated as the simulation is run.

Mammary stem cell enrichment following irradiation during puberty

Experiments have shown that exposure to ionizing irradiation of pubertal mammary glands results in an increase in the ratio of mammary stem cells in the gland. [18] This is important because stem cells are thought to be key targets for cancer initiation by ionizing radiation because they have the greatest long-term proliferative potential and mutagenic events persist in multiple daughter cells. Additionally, epidemiology data show that children exposed to ionizing radiation have a substantially greater breast cancer risk than adults. [19] [20] These experiments thus prompted questions about the underlying mechanism for the increase in mammary stem cells following radiation which can be explored by two agent-based models used in parallel with in vivo and in vitro experiments to evaluate cell inactivation, dedifferentiation via epithelial-mesenchymal transition (EMT), and self-renewal (symmetric division) as mechanisms by which radiation could increase stem cells. [21]

The first agent-based model is a multiscale model of mammary gland development starting with a rudimentary mammary ductal tree at the onset of puberty (during active proliferation) all the way to a full mammary gland at adulthood (when there is little proliferation). The model consists of millions of agents, with each agent representing a mammary stem cell, a progenitor cell, or a differentiated cell in the breast. 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. Surprisingly, radiation-induced cell inactivation by death did not contribute to increased stem cell frequency independently of the dose delivered in the model. Instead the model revealed that the combination of increased self-renewal and cell proliferation during puberty led to stem cell enrichment. In contrast epithelial-mesenchymal transition in the model was shown to increase stem cell frequency not only in pubertal mammary glands but also in adult glands. This latter prediction, however, contradicted the in vivo data; irradiation of adult mammary glands did not lead to increased stem cell frequency. These simulations therefore suggested self-renewal as the primary mechanism behind pubertal stem cell increase.

To further evaluate self-renewal as the mechanism, a second agent-based model was created to simulate the growth dynamics of human mammary epithelial cells (containing stem/progenitor and differentiated cell subpopulations) in vitro after irradiation. By comparing the simulation results with data from the in vitro experiments, the second agent-based model further confirmed that cells must extensively proliferate to observe a self-renewal dependent increase in stem/progenitor cell numbers after irradiation.

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.

See also

Related Research Articles

<span class="mw-page-title-main">Outline of biology</span> Outline of subdisciplines within biology

Biology – The natural science that studies life. Areas of focus include structure, function, growth, origin, evolution, distribution, and taxonomy.

Morphogenesis is the biological process that causes a cell, tissue or organism to develop its shape. It is one of three fundamental aspects of developmental biology along with the control of tissue growth and patterning of cellular differentiation.

<span class="mw-page-title-main">Biofilm</span> Aggregation of bacteria or cells on a surface

A biofilm is a syntrophic community of microorganisms in which cells stick to each other and often also to a surface. These adherent cells become embedded within a slimy extracellular matrix that is composed of extracellular polymeric substances (EPSs). The cells within the biofilm produce the EPS components, which are typically a polymeric combination of extracellular polysaccharides, proteins, lipids and DNA. Because they have a three-dimensional structure and represent a community lifestyle for microorganisms, they have been metaphorically described as "cities for microbes".

<span class="mw-page-title-main">Biological pest control</span> Controlling pests using other organisms

Biological control or biocontrol is a method of controlling pests, whether pest animals such as insects and mites, weeds, or pathogens affecting animals or plants by using other organisms. It relies on predation, parasitism, herbivory, or other natural mechanisms, but typically also involves an active human management role. It can be an important component of integrated pest management (IPM) programs.

In biology, quorum sensing or quorum signaling (QS) is the ability to detect and respond to cell population density by gene regulation, typically as a means of acclimating to environmental disadvantages. Quorum sensing is a type of cellular signaling, and more specifically can be considered a type of paracrine signaling. However, it also contains traits of autocrine signaling: a cell produces both the autoinducer molecule and the receptor for the autoinducer. As one example, QS enables bacteria to restrict the expression of specific genes to the high cell densities at which the resulting phenotypes will be most beneficial, especially for phenotypes that would be ineffective at low cell densities and therefore too energetically costly to express. Many species of bacteria use quorum sensing to coordinate gene expression according to the density of their local population. In a similar fashion, some social insects use quorum sensing to determine where to nest. Quorum sensing in pathogenic bacteria activates host immune signaling and prolongs host survival, by limiting the bacterial intake of nutrients, such as tryptophan, which further is converted to serotonin. As such, quorum sensing allows a commensal interaction between host and pathogenic bacteria. Quorum sensing may also be useful for cancer cell communications.

<span class="mw-page-title-main">Aphid</span> Superfamily of insects

Aphids are small sap-sucking insects and members of the superfamily Aphidoidea. Common names include greenfly and blackfly, although individuals within a species can vary widely in color. The group includes the fluffy white woolly aphids. A typical life cycle involves flightless females giving live birth to female nymphs—who may also be already pregnant, an adaptation scientists call telescoping generations—without the involvement of males. Maturing rapidly, females breed profusely so that the number of these insects multiplies quickly. Winged females may develop later in the season, allowing the insects to colonize new plants. In temperate regions, a phase of sexual reproduction occurs in the autumn, with the insects often overwintering as eggs.

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

<span class="mw-page-title-main">Gravitropism</span> Plant growth in reaction to gravity and bending of leaves and roots

Gravitropism is a coordinated process of differential growth by a plant in response to gravity pulling on it. It also occurs in fungi. Gravity can be either "artificial gravity" or natural gravity. It is a general feature of all higher and many lower plants as well as other organisms. Charles Darwin was one of the first to scientifically document that roots show positive gravitropism and stems show negative gravitropism. That is, roots grow in the direction of gravitational pull and stems grow in the opposite direction. This behavior can be easily demonstrated with any potted plant. When laid onto its side, the growing parts of the stem begin to display negative gravitropism, growing upwards. Herbaceous (non-woody) stems are capable of a degree of actual bending, but most of the redirected movement occurs as a consequence of root or stem growth outside. The mechanism is based on the Cholodny–Went model which was proposed in 1927, and has since been modified. Although the model has been criticized and continues to be refined, it has largely stood the test of time.

<span class="mw-page-title-main">Aphididae</span> Family of true bugs

The Aphididae are a very large insect family in the aphid superfamily (Aphidoidea), of the order Hemiptera. These insects suck the sap from plant leaves. Several thousand species are placed in this family, many of which are considered plant/crop pests. They are the family of insects containing most plant virus vectors with the green peach aphid being one of the most prevalent and indiscriminate carriers.

Microbial intelligence is the intelligence shown by microorganisms. The concept encompasses complex adaptive behavior shown by single cells, and altruistic or cooperative behavior in populations of like or unlike cells mediated by chemical signalling that induces physiological or behavioral changes in cells and influences colony structures.

<span class="mw-page-title-main">Russian wheat aphid</span> Species of true bug

The Russian wheat aphid is an aphid that can cause significant losses in cereal crops. The species was introduced to the United States in 1986 and is considered an invasive species there. This aphid is pale green and up to 2 mm long. Cornicles are very short, rounded, and appear to be lacking. There is an appendage above the cauda giving the aphid the appearance of having two tails. The saliva of this aphid is toxic to the plant and causes whitish striping on cereal leaves. Feeding by this aphid will also cause the flag leaf to turn white and curl around the head causing incomplete head emergence. Its host plants are cereal grain crops including wheat and barley and to a lesser extent, wild grasses such as wheatgrasses, brome-grasses, ryegrasses and anything in the grass family.

<span class="mw-page-title-main">Prokaryote</span> Unicellular organism lacking a membrane-bound nucleus

A prokaryote is a single-cell organism whose cell lacks a nucleus and other membrane-bound organelles. The word prokaryote comes from the Ancient Greek πρό 'before' and κάρυον 'nut, kernel'. In the two-empire system arising from the work of Édouard Chatton, prokaryotes were classified within the empire Prokaryota. But in the three-domain system, based upon molecular analysis, prokaryotes are divided into two domains: Bacteria and Archaea. Organisms with nuclei are placed in a third domain, Eukaryota.

FORECAST is a management-oriented, stand-level, forest-growth and ecosystem-dynamics model. The model was designed to accommodate a wide variety of silvicultural and harvesting systems and natural disturbance events in order to compare and contrast their effect on forest productivity, stand dynamics, and a series of biophysical indicators of non-timber values.

<i>Diaphorina citri</i> Species of true bug

Diaphorina citri, the Asian citrus psyllid, is a sap-sucking, hemipteran bug in the family Psyllidae. It is one of two confirmed vectors of citrus greening disease. It has a wide distribution in southern Asia and has spread to other citrus growing regions.

<span class="mw-page-title-main">Artificial life</span> Field of study

Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987, Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.

Pemphigus betae, also known as the sugarbeet root aphid, is a species of gall-forming aphid that forms galls specifically on the commonly found narrowleaf cottonwood, Populus angustifolia. Sugarbeet root aphids have been found in North America and Europe. They infect sugarbeets, but also other plants like tablebeets and Swiss chard. Their size has been likened to that of a pinhead, and are pale white-yellow in color. Sugarbeet root aphids have soft bodies that are bulbous in shape, with mandibular parts that can pierce and suck and paired abdominal tubes that point backwards, and come in both winged and wingless forms. They are known for their consequential effects on agriculture due to infestation of plants, and efforts to control the pests have proved to be difficult.

Hamiltonella defensa is a species of bacteria. It is maternally or sexually transmitted and lives as an endosymbiont of whiteflies and aphids, meaning that it lives within a host, protecting its host from attack. It does this through bypassing the host's immune responses by protecting its host against parasitoid wasps. However, H. defensa is only defensive if infected by a virus. H. defensa shows a relationship with Photorhabdus species, together with Regiella insecticola. Together with other endosymbionts, it provides aphids protection against parasitoids. It is known to habitate Bemisia tabaci.

<span class="mw-page-title-main">Microscale and macroscale models</span> Classes of computational models

Microscale models form a broad class of computational models that simulate fine-scale details, in contrast with macroscale models, which amalgamate details into select categories. Microscale and macroscale models can be used together to understand different aspects of the same problem.

Cell-based models are mathematical models that represent biological cells as discrete entities. Within the field of computational biology they are often simply called agent-based models of which they are a specific application and they are used for simulating the biomechanics of multicellular structures such as tissues. to study the influence of these behaviors on how tissues are organised in time and space. Their main advantage is the easy integration of cell level processes such as cell division, intracellular processes and single-cell variability within a cell population.

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  9. ↑ BehaviorSpace Guide
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  11. ↑ Metzcar J, Wang Y, Heiland R, Macklin P (February 2019). "A Review of Cell-Based Computational Modeling in Cancer Biology". JCO Clinical Cancer Informatics. 3 (3): 1–13. doi:10.1200/CCI.18.00069. PMC   6584763 . PMID   30715927.
  12. ↑ Rejniak KA (July 2007). "An immersed boundary framework for modelling the growth of individual cells: an application to the early tumour development". Journal of Theoretical Biology. 247 (1): 186–204. Bibcode:2007JThBi.247..186R. doi:10.1016/j.jtbi.2007.02.019. PMID   17416390.
  13. ↑ Newman TJ (2007). "Modeling Multicellular Structures Using the Subcellular Element Model". Single-Cell-Based Models in Biology and Medicine. Mathematics and Biosciences in Interaction. Basel: Birkhäuser Basel. pp. 221–239. doi:10.1007/978-3-7643-8123-3_10. ISBN   978-3-7643-8101-1 . Retrieved 4 April 2022.
  14. 1 2 Lardon LA, Merkey BV, Martins S, Dötsch A, Picioreanu C, Kreft JU, Smets BF (September 2011). "iDynoMiCS: next-generation individual-based modelling of biofilms". Environmental Microbiology. 13 (9): 2416–2434. doi:10.1111/j.1462-2920.2011.02414.x. PMID   21410622. S2CID   19524967.
  15. ↑ Wanner O, Eberl H, Morgenroth E, Noguera D, Picioreanu C, Rittmann B, van Loosdrecht M (2006). Mathematical Modeling of Biofilms. London: IWA Publishing.
  16. ↑ Merkey BV, Lardon LA, Seoane JM, Kreft JU, Smets BF (September 2011). "Growth dependence of conjugation explains limited plasmid invasion in biofilms: an individual-based modelling study". Environmental Microbiology. 13 (9): 2435–2452. doi:10.1111/j.1462-2920.2011.02535.x. PMID   21906217.
  17. ↑ Steffens MJ, Clement BJ, Wentworth CD (2011). Individual-based Modeling of a Pseudomonas aeruginosa Biofilm with Glucose Substrate. Fall 2011 Meeting of the APS Prairie Section, November 10–12, 2011, abstract #E1.006. American Physical Society.
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