Report
Reference
Project Progress Report 2018. Dicty - social Amoeba dictyostelium discoideum as an inspiration for higher-order emergence in collective
adaptive systems
PARHIZKAR, Mohammad
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 [...]
PARHIZKAR, Mohammad. Project Progress Report 2018. Dicty - social Amoeba
dictyostelium discoideum as an inspiration for higher-order emergence in collective adaptive systems . 2018
Available at:
http://archive-ouverte.unige.ch/unige:114231
Disclaimer: layout of this document may differ from the published version.
Ph.D. Progress Report 2018 Mohammad Parhizkar
Dicty - Social Amoeba Dictyostelium discoideum as an
Inspiration for Higher-Order Emergence in Collective Adaptive Systems
Information Systems Geneva School of Social Sciences Centre Universitaire d’Informatique Supervisor: Prof. Giovanna Di Marzo Serugendo
This project is supported by the Swiss National Science Foundation (SNSF)
Grant number205321 179023.
1 Summary
Understanding collective behaviour in nature and its potential links to engineering of collective artificial behaviour 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 behaviours, bio-medicine researchers look for different examples from nature to design anti- cancer drugs to shrink tumours 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 behaviour to survive in response to nutrient starvation. Most of its life, the organism lives in 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 behaviour) 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 behaviour) 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 surface of the soil [12]. Interestingly, at this point the cells resume their individual behaviour.
D. discoideum life cycle is an excellent example of emergent phenomenon. These characteristics inspire us to investigate the relation between first-order and higher-order collective behaviours in terms of emergence. Second- order emergent behaviour 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 behaviours. In the case ofD. discoideum, higher-order emergent behaviour refers to collective behaviour at the level of slugs (themselves the result of collective behaviour at the level of cells). Additionally, this social, relatively simple but yet powerful, behaviour 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, althoughD. discoideum provides a promising research path, not all phases are currently understood and modelled at the micro-level. From the computer science and artificial systems perspectives, first- order emergence is well studied, but higher-order levels of emergent behaviour have not received many attention yet.
Finally, from the collective adaptive systems perspective, there is no attempt at applying higher-order emergent behaviour to this type of systems. The main objectives of this project are to: (1) provide agent-based models of the different phases ofD. discoideum life cycle, (2) extract pertinent mechanisms for higher-order emergent behaviour and provide them as design patterns for artificial systems.
1.1 Motivation
The early interest and motivation in this project is related to the greatest phenomenon in the evolutionary history of life on earth: appearance and behaviour of multicellular organisms (see [7]). Although we do not know the evolutionary origin of multicellular organisms, we know they are self-adaptive, self-regulative and self-developing [6].
One of the excellent instruments to explore and reflect such unknown natural issues are provided by artificial organisms, such as agent-based systems. Artificial organisms can be viewed as simplified analogues of living
1Dictyostelium discoideum
Dictyostelium discoideum life cycle Unicellular to Multicellular
Figure 1: ProjectDicty starts fromD. discoideumlife cycle until swarm of small Kilotos.
organisms. Like living organisms, long-term and short-term developed artificial organisms face similar problems such as, getting energy, surviving in unpredictable environment and self-awareness [5].
Exploitation of nature examples provides several practical advantages for the engineering of artificial systems, such as reliability, robustness to environmental changes, adaptivity, self-evolution, and autonomy. Although today, various bio-inspired collective adaptive systems display autonomous capabilities, they are still far behind the capabilities of real natural organisms. D. discoideum shows a variety of behaviours from individual cells, called swarm mode2(first-order emergent collective behaviours) to the organism mode (higher-order emergent collective behaviours). Additionally, the organism mode exhibits properties that the swarm mode or the individual cells do not have (e.g. sensitivity to the light and heat after the slug formation). Dictyproject includes morphological, developmental collective behaviours ofD. discoideumand apply them to a collective adaptive systems, investigating all levels of collective behaviour (individuals, swarm mode and organism mode behaviour).
D. discoideum (social amoeba: 8 - 12µm)is an eukaryote related to animals and fungi [2]. It feeds on bacteria in the top few centimetres of soil and multiplies by binary fission. The striking feature of D. discoideum cells is that they undergo a relatively simple program of multicellular development, which in many ways resembles animal development [2]. This development process helps individual cells to switch their behaviours and survive despite the lack of food: individual cells move around on their own when there is plenty of food (vegetative phase); but when food is scarce, cells self-organize and aggregate (swarm mode and first-order emergent behaviour) to build a coherent and cohesive organism, similar to a slug (approximately of the same size - 2×104to 1×105cells) moving in a coordinated way towards areas with food [1] (organism mode and higher-order emergent behaviours). Under specific conditions, slugs may merge together or divide. Afterwards, each slug transforms into a new organism called fruiting body consisting of a globule of spore cells and a slender stalk. Stalk is responsible for holding the spore mass as high off the ground as possible, for optimal spore dispersal [1].
2 Phases of the Project:
The main goal ofDictyis to provide advances in higher-order emergent behaviours by understanding and modelling the behaviour of D. discoideum. Figure 2shows the main phases and tasks ofDicty.
Phase 1 Understanding and Modeling
D. discoideum First-order Emergent Behaviors Understanding D. discoideum first-order emergent behaviors (vegetative, streaming, pre-aggregation)
Applying emergence framework to D.
Discoideum (first- order) D. discoideum agent-based model of first-order emergent behaviors Algorithms and simulations
Biological Validation 1.1
1.2 1.3
1.4 1.5
Phase 2 Understanding and Modeling
D. discoideum Higher-order Emergent Behaviors Understanding D. discoideum higher-order emergent behaviors (aggregation, migration, culmination)
Applying emergence framework to D.
Discoideum (higher- order) D. discoideum agent-based model of higher-order emergent behaviors Algorithms and simulations
Biological Validation 2.1
2.2 2.3
2.4 2.5
Phase 3 Identifying Mechanisms and Defining
Design Patterns for Higher-order
Identifying mechanisms for higher-order emergent behaviors
Defining design patterns Validation 3.1
3.2 3.3
Emergent Behaviors
Phase 4 Translating D. discoideum
higher-order emergent behaviors to robots Understanding swarm and collective robotics (software and hardware perspective) Translating first- and higher order collective model into the small Kilobots Self-reconfiguration algorithms Finalizing writing PhD manuscript 4.1
4.2 4.3 4.4
Figure 2: Four phases ofDictyproject and their main tasks.
2We use the terminology of [5] from swarm robotics: the disaggregated cells are in swarm mode, whereas aggregated cells are in organism mode.
3 Research Questions
Dictyaddresses these points and tackles the following research questions:
• What are the social relations and configurations of D. discoideum behaviours at the different phases of its life cycle and and how to model them?
• What are the mechanisms favouring higher-order emergence in swarms and artificial collective behaviour?
• How to translate and implement those mechanisms into collective adaptive systems?
Dictysubstantially advances the state of the art by providing:
• Fine-grained understanding of D. discoideum individual cells behaviours at all phases of its life cycle and provision of corresponding agent-based models validated with actual biological experiments;
• Novel self-organizing mechanisms for higher-order emergent behaviours, expressed and defined as design patterns for artificial systems.
4 Status of the Project
• The research topic and plan were official presented and approved as the Sujet de Th`ese to the faculty of social sciences.
• The research proposal was officially presented and approved by the Swiss National Science Foundation,March 2018- (SNSF) Grant number 205321 179023-http: // p3. snf. ch/ project-179023.
• The efforts in 2018 were spent in all phases of the project,Phase 1,Phase 2 andphase3 in parallel:
– Progress in phase 1
Our agent-based - “bottom-up” - model exhibits a series of individual, collective behaviours and emer- gent properties of social amoeba D. discoideum. During 2018, we extended previous models of the aggregation phase with: a pre-aggregation phase; and three different levels of quorum sensing allowing collective decisions to be taken in a decentralised manner for: (1) identifying the time for aggregation phase; (2) providing aggregation territories of homogeneous size; (3) allowing the appearance of late centers. The key and unique character of our model is the cells’ self-assessment and self-generated gradients arising from six chemical factors: PSF, CMF, Adenosine, cAMP, PDE and CF released by an individual amoeba. We programmed and simulated our model in MATLAB.
∗ Stream breaking model inD. discoideum aggregation
∗ Considering eachD. discoideummore than a discreet point in the simulations – Progress in phase 2, 3
The focus of this part is to analyze and mimic the second-order collective behaviours inD. discoideum life cycle, regarding the different spatial horizontal and vertical aspects of the slug formation and slug’s behaviours. To this purpose, we continued using MATLAB and Python, as well.
During this year and next year, our goal was and will be to define design patterns supporting the design of higher-order emergent behaviour. Towards this goal, we identified the following three elements, which we leveraged to shapeD. discoideummodel behaviour.
1. Generic Emergence Framework [3]:enables shaping the different levels of emergence, in par- ticular, it helps in identifying what relates to first-order (swarm) behaviour, and what relates to second-order (or any higher-order) (slug/organism) behaviour;
2. MASQ Quadrants [11] :enables shaping collective behaviour, in particular understanding what relates to each quadrant: internal state (parameters, internal state values); individual actions (in- ternal behaviour); social structures (used for reification); interactions among cells; – and how this happens in first- and any higher-order emergent behaviour respectively.
3. Transitions throughout levels of emergence:Besides individual behaviour, we also looked at the transitions throughout levels of emergence and how they are achieved with micro-behaviour.
We considered three possible ways of modelling these transitions.
Various levels of the signal as a trigger for change: Various levels of signals (e.g. various levels of cAMP, nutrient and other chemical signals) act as the threshold and trigger various behaviours, and thus achieve the transition from the first-order emergence to higher-order emergence. In our
model, this type of transition occurs once aggregation territories are formed, and mound formation starts.
Downward causation as a trigger for change:Higher-order behaviours occur when agents recognize emergent phenomena, such as societies, clubs, formal organizations, institutions, localities. An agent, realizing it belongs to a group, changes the rules of interaction between itself and other agents [4]. Here we investigated various criteria, such as: (1) density levels, territory sizes, distance from centre of territory (during the aggregation phase) to trigger mound formation and differentia- tion (pre-spore, pre-stalk); (2) environmental information about heat, light, and nutrient to trigger slug motion; (3) size of slug to trigger slug splitting, slug merging (during slug motion); In partic- ular, in our model, cells at the tip of the slug recognize they are at the tip (i.e. they realize that they are surrounded by cells similar to themselves), identify a slug is formed, and start following the light or merging with other slugs.
Emergent Representation: In this step, we considered that the notion of Representation relates to the Collective-External quadrant. However, Carvalho [3] goes beyond this and considers that in each quadrant a representation can be at work to trigger changes from one emergent order to the other. Regarding of these representations further helps in to design the changes within a collective system.
– Progress in phase 4
We recently bought 50 Kilobots to mimic the first and second- order collective behaviors of D. dis- coideum. To understand the basic behaviors of cells, such as movement, signaling, gradient and neigh- bor sensing, we had some experiments with these small robots that allow one to see the real, physical collective swarm behaviors. Kilobots interact with each other via an infrared transceiver placed at the base of them, with a communication range of up to three body lengths. This current channel is shared utilizing a carrier sense multiple access with collision detection protocol (CSMA/CD). Upon obtaining a broadcast message, a Kilobot can approximately determine the distance of the transmitting Kilobot based on the signal strength.
5 Results
Our model and results show a series of behaviour close to individual and collective behaviour of livingD. discoideum.
• First-order collective behaviours: in phase 1 we observed: inhibition of centers too close to each other;
appearance of late centers when aggregation territories are too large; and measured homogeneous size ag- gregation territories emerging from the cells’ behaviour. Most parts of the model have been validated with biological experiments [8].
• Second-order collective behaviours: inphase 2 we observed: slug collective movement, phototaxis, merging mechanism and ammonia affect. This model needs to be validated with biological experiments [9].
• Our results of Phase 4, during 2018 is agent-agent signalling, distance measurements based on wireless signal strength, light gradient.
6 Next Steps
• First-order emergence:
– Biological validation: Most parts of the model have been validated by prof. Thierry Soldati’s group.
However the rest of it still need to be validated by them, soon.
– Simulation: adding an already aggregated cluster to a new field and observe the competition between centers.
• Second-order emergence:
– Improving the phototaxis mechanism
– Improving the model: slug orientation angle to the light source – Continuation of the work of slug’s age simulation
– Biological validation – New design patterns
• Robotic part:
– First-order collective behaviors: Introducing self-selection centers and centers competition models into the kilobots.
– Second-order collective behaviors: Introducing leader-follower and block-movement models into the kilobots.
7 Given Talks and Presentations
• Seminar for biochemistry group, Sciences II, November 2018
8 Participation in Scientific Events
• CUSO- Informatique
1. Winter school- Modelling of knowledge and the cyber-physical systems-Champ´ery 05/02/2018-09/02/2018
2. Winter school- Deep learning- Lenk 21/01/2019-25/01/2019
• Young researchers day- Geneva CMU- October 2018
9 University Responsibilities
• TA: Bases de donn´ees- Semester printemps 2018
• Cit´e des M´etiers- Present the Swarm Robotics content, poster, Palexpo, 2018
10 Publications
• Published
– Agent-Based Models for First- and Second-Order Emergent Collective Behaviors of Social Amoeba Dic- tyostelium discoideum Aggregation and Migration Phases-Springer, Artificial life and robotic journal, 2018
• Ongoing Publications
– Methods, Frameworks and Design Patterns for Higher-Order Emergence of Collective Behaviors: A case study for Multicellular Development in Social Amoeba Dictyostelium Discoideum -accomplishment: 90%
– Modeling and Simulation of Stream breaking Mechanism inD. discoideumAggregation Phase-accom- plishment: 60%
– Swarm Robotic, first-order and second-order emergence: Dictyostelium discoideum and Kilobots -ac- complishment: 25%
– Swarm Robotics: The age-dependent speed of Dictyostelium discoideum motile slug, simulation and validation with Kilobots -accomplishment: 60%
References
[1] Debra A Brock and Richard H Gomer. A cell-counting factor regulating structure size in Dictyostelium.Genes
& development, 13(15):1960–1969, 1999.
[2] Juliet C Coates and Adrian J Harwood. Cell-cell adhesion and signal transduction during Dictyostelium development. Journal of Cell Science, 114(24):4349–4358, 2001.
[3] L de Carvalho. Reprsentations Emergentes - Une approche Multi-Agents des Systmes Complexes Adaptatifs en Psychologie Cognitive. PhD thesis, Universit Lumire Lyon II, 2008.
[4] N Gilbert. Varieties of Emergents. Social agents: Ecology, Exchange, and Evolution, 6(2):1–12, 2002.
[5] Serge Kernbach, Heiko Hamann, J¨urgen Stradner, Ronald Thenius, Thomas Schmickl, Karl Crailsheim, Anne C van Rossum, Michele Sebag, Nicolas Bredeche, Yao Yao, and others. On adaptive self-organization in artificial robot organisms. InFuture Computing, Service Computation, Cognitive, Adaptive, Content, Pat- terns, 2009. COMPUTATIONWORLD’09. Computation World:, pages 33–43. IEEE, 2009.
[6] Paul Levi and Serge Kernbach. Symbiotic Multi-Robot Organism: Reliability, Adaptability, Evolution. chapter One, pages 1–25. Springer Berlin Heidelberg, 2010.
[7] Mohammad Parhizkar and Giovanna Di Marzo Serugendo. Social Amoeba Dictyostelium Discoideum as an Inspiration for Swarm Robotics. InIEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2015, pages 162–163, 2015.
[8] Mohammad Parhizkar and Giovanna Di Marzo Serugendo. An Agent-Based Model for Collective Behaviors of Social Amoeba Dictyostelium discoideum Morphogenesis: Aggregation Phase. InInternational Conference on : SWARM’17, 2017.
[9] Mohammad Parhizkar and Giovanna Di Marzo Serugendo. Agent-based models for first- and second-order emergent collective behaviours of social amoeba Dictyostelium discoideum aggregation and migration phases.
Artificial Life and Robotics-Springer, 23(4):498–507, 2018.
[10] Robert Keith Sawyer. Social emergence: Societies as complex systems. Cambridge University Press, 2005.
[11] Tiberiu Stratulat, Jacques Ferber, and John Tranier. MASQ: Towards an Integral Approach to Interaction.
InProceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2, AAMAS ’09, pages 813–820, Richland, SC, 2009. International Foundation for Autonomous Agents and Multiagent Systems.
[12] Cornelis J Weijer. Collective cell migration in development. Journal of cell science, 122(18):3215–3223, 2009.
