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Project Progress Report 2018. Dicty - social Amoeba dictyostelium discoideum as an inspiration for higher-order emergence in collective adaptive systems

Publication date2018
Abstract

Understanding collective behavior in nature and its potential links to the engineering of collective artificial behavior attracts many researchers from biology, computer science, and swarm robotics. It impacts different scientific and industrial topics such as cell-biology, cancer study, environment cleaning, swarms of drones, unmanned robots, and more generally collective adaptive systems based on IoT or massive ICT deployment. For instance, cancer cells exhibit collective behaviors, bio-medicine researchers look for different examples from nature to design anti-cancer drugs to shrink tumors in human bodies. An interesting form of collective system is demonstrated by Dictyostelium discoideum and its multicellular development process. D. discoideum1 is a social amoeba able to change its behavior to survive in response to nutrient starvation. Most of its life, the organism lives in the soil as a single amoeba and feeds on bacteria. Individual cells move around on their own when there is plenty of food. Then, when food is scarce, the cells start a multicellular developmental process. Up to a million amoeboid cells artfully self-aggregate via pattern formation (first-order emergent behavior) to build a coherent and cohesive super-organism, similar to a motile slug structure. This complex super-organism has several properties that none of the cells has on its own (e.g. sensitivity to light and heat). The slug moves as a whole (second-order emergent behavior) looking for a suitable place to transform into a fruiting body in which about 20% of the cells die to lift the remaining cells up to a better place for sporulation and dispersal on the surface of the soil [12]. Interestingly, at this point, the cells resume their individual behavior. D. discoideum life cycle is an excellent example of the emergent phenomenon. These characteristics inspire us to investigate the relationship between first-order and higher-order collective behaviors in terms of emergence. Second- order emergent behavior arises from the interactions of individuals, which are themselves the result of first-order emergent societies. According to Sawyer's book [10], second-order emergence, refers to systems in which agents recognize the existence of groups that emerged from their own collective behaviors. In the case of D. discoideum, higher-order emergent behavior refers to collective behavior at the level of slugs (themselves the result of collective behavior at the level of cells). Additionally, this social, relatively simple but yet powerful, the behavior is particularly appealing to inspire the engineering of collective adaptive systems, where a large number of simple homogeneous agents coordinate, self-organize and adapt themselves to environmental changes. The Dicty project, therefore, involves the combination of different disciplines - cell biology; self-organizing systems and swarm intelligence into one activity. On the biology field, although D. discoideum provides a promising research path, not all phases are currently understood and modeled at the micro-level. From the computer science and artificial systems perspectives, first- order emergence is well studied, but higher-order levels of emergent behavior have not received much attention yet. Finally, from the collective adaptive systems perspective, there is no attempt at applying higher-order emergent behavior to this type of systems. The main objectives of this project are to (1) provide agent-based models of the different phases of D. discoideum life cycle, (2) extract pertinent mechanisms for higher-order emergent behavior and provide them as design patterns for artificial systems.

Funding
  • Swiss National Science Foundation - 205321 179023
Citation (ISO format)
PARHIZKAR, Mohammad. Project Progress Report 2018. Dicty - social Amoeba dictyostelium discoideum as an inspiration for higher-order emergence in collective adaptive systems. 2018
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Identifiers
  • PID : unige:114231
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