Morphogenetic Engineering:
Reconciling Architecture and Self-Organization
Through Programmable Complex Systems
BMES Semina...
Doursat, Sayama & Michel (2012)
Morphogenetic Engineering
Doursat, Sayama & Michel (2012)
Morphogenetic Engineering
illustrationofMorphogeneticEngineeringideas
Systems that are self-organized and architectured
 Embryogenesis to Simulated...
 Emergence on multiple levels of self-organization
“complex systems” 
a) large number of elementary agents interacting l...
Mammal fur, seashells, and insect wings
(Scott Camazine, http://www.scottcamazine.com)
Biological pattern formation: Anima...
Swarm intelligence: Insect colonies (ant trails, termite mounds)
Termite mound
(J. McLaughlin, Penn State University)
http...
Bison herd
(Center for Bison Studies, Montana State University, Bozeman)
Fish school
(Eric T. Schultz, University of Conne...
Multilevel Techno-social networks and human organizations
SimCity
(http://simcitysocieties.ea.com)
enterprise urban system...
the brain
organisms
ant trails
termite
mounds
animal
flocks
social networks
markets,
economy
Internet,
Web
physical
patter...
 Emergence
 the system has properties that the elements do not have
 these properties cannot be easily inferred or dedu...
 Positive feedback, circularity
 creation of structure by amplification of fluctuations
(homogeneity is unstable)
 ex: ...
 Note: decentralized processes are far more
abundant than leader-guided processes, in
nature and human societies
 ... an...
 Precursor and neighboring disciplines
dynamics: behavior and activity of a
system over time multitude, statistics: large...
the brain organisms ant trails
termite
mounds
animal
flocks
physical
patterns
living cell
biological
patterns
 biology st...
 ME brings a new focus in complex systems engineering
 exploring the artificial design and implementation of decentraliz...
http://iscpif.fr/MEW2009
1st Morphogenetic Engineering Workshop, ISC, Paris 2009
http://iridia.ulb.ac.be/ants2010
2nd Morp...
Doursat, Sayama & Michel, eds. (2012)
Chap 2 – O'Grady, Christensen & Dorigo
Chap 3 – Jin & Meng
Chap 4 – Liu & Winfield
C...
 Between natural and engineered emergence
CS (ICT) Engineering: creating and programming
a new, artificial self-organizat...
Complex Systems (CS) Engineering:
creating artificial emergence capable of functioning/computing
 From biomodels to bio-i...
 From biological to artificial development to synthetic biology
 designing multi-agent models for decentralized systems ...
illustrationofMorphogeneticEngineeringideas
Systems that are self-organized and architectured
 Embryogenesis to Simulated...
Processing
Simulation
Phenomenological
reconstruction
2
Model3
Validation5
Raw imaging data1
4
Computational
reconstructio...
Tensionalintegrity
DonaldIngber,Harvard
CellularPottsmodel
Graner,Glazier,Hogeweg
http://www.compucell3d.org
Deformablevol...
illustrationofMorphogeneticEngineeringideas
Systems that are self-organized and architectured
 Embryogenesis to Simulated...
pA
BV
rr0rerc
GSA: rc < re = 1 << r0
p = 0.05
3. MAPDEVO – Modular Architecture by Programmable Development
all 3D+t simul...
27
Bi = (Li(X, Y)) = (wix X + wiy Y  i), Ik = i |w'ki|(w'kiBi + (1w'ki)/2)
all 2D+t simulations:
Rene Doursat (tool:...
I4 I6
rc = .8, re = 1, r0 = 
r'e= r'0=1, p =.01
GSA
SA
PF
SA4
PF4
SA6
PF6
SA
PF
SA4
PF4
SA6
PF6
all cells have same GRN, ...
 Limbs’ development
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
all 3D+t simulations:
Carlos Sanchez (tool: ODE)
3...
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
 3D Development
all 3D+t simulations:
Carlos Sanchez (tool: ODE)
3. MA...
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
all 3D+t simulations:
Carlos Sanchez (tool: ODE) Locomotion and behavi...
 Quantitative mutations: limb thickness
GPF
GSA
33
1, 1
p = .05
g = 15
4 6
disc
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
1...
all 3D+t simulations:
Carlos Sanchez (tool: ODE)
 Stair climbing challenge Hooves and horseshoes
all 3D+t simulations:
C...
 Stair climbing challenge:  better body and limb sizes...
... i.e., better
developmental genomes! all 3D+t simulations:
...
(a) (b) (c)
antennapedia duplication
(three-limb)
divergence
(short & long-limb)
PF
SA
11
tip p’= .05
GPF
GSA
33
p = .05...
BIOMODELING
illustrationofMorphogeneticEngineeringideas
Systems that are self-organized and architectured
 Embryogenesis ...
 Other scale: potential
applications in self-
constructing techno-social
networks by preferential
programmed attachment
5...
 Preferential
Programmed
Attachment
Networking
(ProgNet)
 Diffusion-
Program-
Limited
Aggregation
(ProgLim)
Doursat, Fou...
 Programmed
stereotyped
development
 Environment-
Induced
Polyphenism
Doursat, Fourquet, Dordea & Kowaliw (2012)
evol ev...
5. PROGLIM – Program-Limited Aggregation
Carlos Sánchez
Doctoral Student
Taras Kowaliw, PhD
Research Scientist
Julien Delile
Doctoral Student
Acknowledgments
David...
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Morphogenetic Engineering: Reconciling Architecture and Self-Organization Through Programmable Complex Systems - Rene Doursat

  1. 1. Morphogenetic Engineering: Reconciling Architecture and Self-Organization Through Programmable Complex Systems BMES Seminar – April 18, 2013 René Doursat AWASS 2013 Summer School Lucca, Italy
  2. 2. Doursat, Sayama & Michel (2012) Morphogenetic Engineering
  3. 3. Doursat, Sayama & Michel (2012) Morphogenetic Engineering
  4. 4. illustrationofMorphogeneticEngineeringideas Systems that are self-organized and architectured  Embryogenesis to Simulated Development to Synthetic Biology 2. MECAGEN – Mechano-Genetic Model of Embryogenesis 4. SYNBIOTIC – Synthetic Biology: From Design to Compilation 3. MAPDEVO – Modular Architecture by Programmable Development BIOMODELING (LIFE) BIOENGINEERING (HYBRID LIFE) Delile, Doursat & Peyrieras Kowaliw & Doursat Doursat, Sanchez, Fernandez, Kowaliw & Vico mutually beneficial transfers between biology & CS animal inspiration 5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation plant inspiration Doursat, Fourquet & Kowaliw 1. Toward a New Kind of Engineering Based on Complex Systems Looking at Natural Complex Systems Focusing on "Architectures Without Architects" Proposing Morphogenetic Engineering BIO-INSPIRED ENGINEERING (“ALIFE”)
  5. 5.  Emergence on multiple levels of self-organization “complex systems”  a) large number of elementary agents interacting locally b) simple individual behaviors creating a complex emergent collective behavior c) decentralized dynamics: no blueprint or architect Looking at Natural Complex Systems
  6. 6. Mammal fur, seashells, and insect wings (Scott Camazine, http://www.scottcamazine.com) Biological pattern formation: Animal colors ctivator nhibitor NetLogo fur coat simulation, after David Young’s model of fur spots and stripes (Michael Frame & Benoit Mandelbrot, Yale University)  animal patterns (for warning, mimicry, attraction) can be caused by pigment cells trying to copy their nearest neighbors but differentiating from farther cells HOW?WHAT? Looking at Natural Complex Systems
  7. 7. Swarm intelligence: Insect colonies (ant trails, termite mounds) Termite mound (J. McLaughlin, Penn State University) http://cas.bellarmine.edu/tietjen/ TermiteMound%20CS.gif Termite stigmergy (after Paul Grassé; from Solé and Goodwin, “Signs of Life”, Perseus Books) Harvester ant (Deborah Gordon, Stanford University) http://taos-telecommunity.org/epow/epow-archive/ archive_2003/EPOW-030811_files/matabele_ants.jpg http://picasaweb.google.com/ tridentoriginal/Ghana  ants form trails by following and reinforcing each other’s pheromone path  termite colonies build complex mounds by “stigmergy” WHAT? Looking at Natural Complex Systems NetLogo Ants simulation HOW?
  8. 8. Bison herd (Center for Bison Studies, Montana State University, Bozeman) Fish school (Eric T. Schultz, University of Connecticut) Separation, alignment and cohesion (“Boids” model, Craig Reynolds, http://www.red3d.com/cwr/boids) S A C Collective motion: flocking, schooling, herding  coordinated collective movement of dozens or thousands of individualsWHAT? HOW? Looking at Natural Complex Systems (to confuse predators, close in on prey, improve motion efficiency)  each individual adjusts its position, orientation & speed according to its nearest neighbors NetLogo Flocking simulation
  9. 9. Multilevel Techno-social networks and human organizations SimCity (http://simcitysocieties.ea.com) enterprise urban systems (Thomas Thü Hürlimann, http://ecliptic.ch) NSFNet Internet (w2.eff.org) national gridsglobal networks NetLogo urban sprawl simulation NetLogo preferential attachment simulation cellular automata model “scale-free” network model HOW? WHAT? Looking at Natural Complex Systems
  10. 10. the brain organisms ant trails termite mounds animal flocks social networks markets, economy Internet, Web physical patterns living cell biological patterns animals humans & tech molecules cells  All agent types: molecules, cells, animals, humans & tech Looking at Natural Complex Systems cities, populations
  11. 11.  Emergence  the system has properties that the elements do not have  these properties cannot be easily inferred or deduced  different properties can emerge from the same elements  Self-organization  “order” of the system increases without external intervention  originates purely from interactions among the agents (possibly via cues in the environment)  Counter-examples: emergence without self-organization  ex: well-informed leader (orchestra conductor, military officer)  ex: global plan (construction area), full instructions (program) Common Properties of Complex Systems Looking at Natural Complex Systems
  12. 12.  Positive feedback, circularity  creation of structure by amplification of fluctuations (homogeneity is unstable)  ex: termites bring pellets of soil where there is a heap of soil  ex: cars speed up when there are fast cars in front of them  ex: the media talk about what is currently talked about in the media  Decentralization  the “invisible hand”: order without a leader  ex: the queen ant is not a manager  ex: the first bird in a V-shaped flock is not a leader  distribution: each agent carry a small piece of the global information  ignorance: agents don’t have explicit group-level knowledge/goals  parallelism: agents act simultaneously Common Properties of Complex Systems Looking at Natural Complex Systems
  13. 13.  Note: decentralized processes are far more abundant than leader-guided processes, in nature and human societies  ... and yet, the notion of decentralization is still counterintuitive  decentralized phenomena are still poorly understood  a “leader-less” or “designer-less” explanation still meets with resistance  this is due to a strong human perceptual bias toward an identifiable source or primary cause  hence our irresistible tendency to simplify, idealize and over-design Common Properties of Complex Systems Looking at Natural Complex Systems
  14. 14.  Precursor and neighboring disciplines dynamics: behavior and activity of a system over time multitude, statistics: large-scale properties of systems adaptation: change in typical functional regime of a system complexity: measuring the length to describe, time to build, or resources to run, a system dynamics: behavior and activity of a system over time  nonlinear dynamics & chaos  stochastic processes  systems dynamics (macro variables) adaptation: change in typical functional regime of a system  evolutionary methods  genetic algorithms  machine learning complexity: measuring the length to describe, time to build, or resources to run, a system  information theory (Shannon; entropy)  computational complexity (P, NP)  Turing machines & cellular automata systems sciences: holistic (non- reductionist) view on interacting parts systems sciences: holistic (non- reductionist) view on interacting parts  systems theory (von Bertalanffy)  systems engineering (design)  cybernetics (Wiener; goals & feedback)  control theory (negative feedback)  Toward a unified “complex systems” science and engineering? multitude, statistics: large-scale properties of systems  graph theory & networks  statistical physics  agent-based modeling  distributed AI systems Looking at Natural Complex Systems
  15. 15. the brain organisms ant trails termite mounds animal flocks physical patterns living cell biological patterns  biology strikingly demonstrates the possibility of combining pure self-organization and an elaborate architecture  there is a whole class of morphological (self-dissimilar) systems, in which morphogenesis > pattern formation  “Simple”/“random” vs. architectured complex systems 3. Architectures Without Architects “The stripes are easy, it’s the horse part that troubles me” —attributed to A. Turing, after his 1952 paper on morphogenesis Focusing on "Architectures Without Architects"
  16. 16.  ME brings a new focus in complex systems engineering  exploring the artificial design and implementation of decentralized systems capable of developing elaborate, heterogeneous morphologies without central planning or external lead Morphogenetic Engineering (ME) is about designing the agents of self-organized architectures... not the architectures directly  swarm robotics, modular/reconfigurable robotics  mobile ad hoc networks, sensor-actuator networks  synthetic biology, etc.  Related emerging ICT disciplines and application domains  amorphous/spatial computing (MIT, Fr.)  organic computing (DFG, Germany)  pervasive adaptation (FET, EU)  ubiquitous computing (PARC)  programmable matter (CMU) Morphogenetic Engineering
  17. 17. http://iscpif.fr/MEW2009 1st Morphogenetic Engineering Workshop, ISC, Paris 2009 http://iridia.ulb.ac.be/ants2010 2nd Morphogenetic Engineering Session, ANTS 2010, Brussels Morphogenetic Engineering: Toward Programmable Complex Systems Fall 2012, SpringerR. Doursat, H. Sayama & O. Michel, eds. http://ecal11.org/workshops#mew 3rd Morphogenetic Engineering Workshop, ECAL 2011, Paris Morphogenetic Engineering
  18. 18. Doursat, Sayama & Michel, eds. (2012) Chap 2 – O'Grady, Christensen & Dorigo Chap 3 – Jin & Meng Chap 4 – Liu & Winfield Chap 5 – Werfel Chap 6 – Arbuckle & Requicha Chap 7 – Bhalla & Bentley Chap 8 – Sayama Chap 9 – Bai & Breen Chap 10 – Nembrini & Winfield Chap 11 – Doursat, Sanchez, Dordea, Fourquet & Kowaliw Chap 12 – Beal Chap 13 – Kowaliw & Banzhaf Chap 14 – Cussat-Blanc, Pascalie, Mazac, Luga & Duthen Chap 15 – Montagna & Viroli Chap 16 – Michel, Spicher & Giavitto Chap 17 – Lobo, Fernandez & Vico Chap 18 – von Mammen, Phillips, Davison, Jamniczky, Hallgrimsson & Jacob Chap 19 – Verdenal, Combes & Escobar- Gutierrez
  19. 19.  Between natural and engineered emergence CS (ICT) Engineering: creating and programming a new, artificial self-organization / emergence  (Complex) Multi-Agent Systems (MAS) CS Science: observing and understanding "natural", spontaneous emergence (including human-caused)  Agent-Based Modeling (ABM) Engineering & Control of Self-Organization: fostering and guiding complex systems at the level of their elements From CS Modeling to CS Engineering and back
  20. 20. Complex Systems (CS) Engineering: creating artificial emergence capable of functioning/computing  From biomodels to bio-inspiration  already somewhat a tradition (although too much "de-complexification")... From CS Modeling to CS Engineering and back Complex Systems (CS) Science: observing and understanding natural, spontaneous emergence ex3: genes & evolution laws of genetics genetic program, binary code, mutation genetic algorithms (GAs), & evolutionary computation for search & optimization ex1: neurons & brain biological neural models binary neuron, linear synapse artificial neural networks (ANNs) applied to machine learning & classification ex2: ant colonies trails, swarms move, deposit, follow "pheromone" ant colony optimization (ACO) graph theoretic & networking problems tiissue engineering biochemical computing synthetic biology neuromorphic chips nanotube computing swarm robotics Complex Systems (CS) Engineering: creating artificial emergence capable of functioning/computingthen reimplementing it in bioware, nanoware, roboware, etc. to bio-engineering
  21. 21.  From biological to artificial development to synthetic biology  designing multi-agent models for decentralized systems engineering Doursat (2006) Doursat & Ulieru (2009)Doursat (2008, 2009) Doursat, Fourquet, Dordea & Kowaliw (2012) Morphogenesis Doursat, Sanchez, Fernandez Kowaliw & Vico (2012) Morphogenetic Engineering Synthetic Biology Morphogenetic Engineering
  22. 22. illustrationofMorphogeneticEngineeringideas Systems that are self-organized and architectured  Embryogenesis to Simulated Development to Synthetic Biology 2. MECAGEN – Mechano-Genetic Model of Embryogenesis 4. SYNBIOTIC – Synthetic Biology: From Design to Compilation 3. MAPDEVO – Modular Architecture by Programmable Development BIOENGINEERING BIO-INSPIRED ENGINEERING Delile, Doursat & Peyrieras Kowaliw & Doursat Doursat, Sanchez, Fernandez, Kowaliw & Vico mutually beneficial transfers between biology & CS Doursat, Fourquet & Kowaliw 5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation 1. Toward Complex Systems Design 1.1. Looking at Natural Complex Systems 1.2. Noticing Architectures Without Architects 1.3. Conceiving Morphogenetic Engineering BIOMODELING
  23. 23. Processing Simulation Phenomenological reconstruction 2 Model3 Validation5 Raw imaging data1 4 Computational reconstruction Hypotheses 2. MECAGEN – Mechano-Genetic Model of Embryogenesis  Methodology and workflow PhD thesis: Julien Delile (ISC-PIF) supervisors: René Doursat, Nadine Peyriéras
  24. 24. Tensionalintegrity DonaldIngber,Harvard CellularPottsmodel Graner,Glazier,Hogeweg http://www.compucell3d.org Deformablevolume Doursat,simul.byDelile Morphogenesis essentially couples mechanics and genetics Spring-massmodel Doursat(2009)ALIFEXI [A] Cell mechanics (“self-sculpting”) gene regulation differential adhesion modification of cell size and shape growth, division, apoptosis changes in cell-to-cell contacts changes in signals, chemical messengers diffusion gradients ("morphogens") motility, migration [B] Gene regulation (“self-painting”) schema of Drosophila embryo, after Carroll, S. B. (2005) "Endless Forms Most Beautiful", p117 2. MECAGEN – Mechano-Genetic Model of Embryogenesis
  25. 25. illustrationofMorphogeneticEngineeringideas Systems that are self-organized and architectured  Embryogenesis to Simulated Development to Synthetic Biology 2. MECAGEN – Mechano-Genetic Model of Embryogenesis 4. SYNBIOTIC – Synthetic Biology: From Design to Compilation 3. MAPDEVO – Modular Architecture by Programmable Development BIOENGINEERING Delile, Doursat & Peyrieras Kowaliw & Doursat Doursat, Sanchez, Fernandez, Kowaliw & Vico mutually beneficial transfers between biology & CS Doursat, Fourquet & Kowaliw 5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation 1. Toward Complex Systems Design 1.1. Looking at Natural Complex Systems 1.2. Noticing Architectures Without Architects 1.3. Conceiving Morphogenetic Engineering BIOMODELING BIO-INSPIRED ENGINEERING
  26. 26. pA BV rr0rerc GSA: rc < re = 1 << r0 p = 0.05 3. MAPDEVO – Modular Architecture by Programmable Development all 3D+t simulations: Carlos Sanchez (tool: ODE) Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012) [A] “mechanics” 
  27. 27. 27 Bi = (Li(X, Y)) = (wix X + wiy Y  i), Ik = i |w'ki|(w'kiBi + (1w'ki)/2) all 2D+t simulations: Rene Doursat (tool: Java) 3. MAPDEVO – Modular Architecture by Programmable Development
  28. 28. I4 I6 rc = .8, re = 1, r0 =  r'e= r'0=1, p =.01 GSA SA PF SA4 PF4 SA6 PF6 SA PF SA4 PF4 SA6 PF6 all cells have same GRN, but execute different expression paths  determination / differentiation microscopic (cell) randomness, but mesoscopic (region) predictability Doursat (2008, 2009) N(4) S(4) W(4) E(4) fromCoen,E.(2000) TheArtofGenes,pp131-135 all 2D+t simulations: R. Doursat (tool: Java) 3. MAPDEVO – Modular Architecture by Programmable Development
  29. 29.  Limbs’ development Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012) all 3D+t simulations: Carlos Sanchez (tool: ODE) 3. MAPDEVO – Modular Architecture by Programmable Development
  30. 30. Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)  3D Development all 3D+t simulations: Carlos Sanchez (tool: ODE) 3. MAPDEVO – Modular Architecture by Programmable Development
  31. 31. Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012) all 3D+t simulations: Carlos Sanchez (tool: ODE) Locomotion and behavior by muscle contraction  Bones & muscles: structural differentiation and properties 3. MAPDEVO – Modular Architecture by Programmable Development
  32. 32.  Quantitative mutations: limb thickness GPF GSA 33 1, 1 p = .05 g = 15 4 6 disc GPF GSA 11 tip p’= .05 g’= 15 GPF GSA 11 tip p’= .05 g’= 15 GPF GSA 33 2, 1 4 6 disc p = .05 g = 15 GPF GSA 11 tip p’= .05 g’= 15 GPF GSA 33 0.5, 1 4 6 disc p = .05 g = 15 (a) (b) (c) wild type thin-limb thick-limb body plan module limb module 4 6 Doursat (2009) all 2D+t simulations: Rene Doursat (tool: Java) 3. MAPDEVO – Modular Architecture by Programmable Development
  33. 33. all 3D+t simulations: Carlos Sanchez (tool: ODE)  Stair climbing challenge Hooves and horseshoes all 3D+t simulations: Carlos Sanchez (tool: ODE) 3. MAPDEVO – Modular Architecture by Programmable Development Fitness = | end – start | / path length < 1
  34. 34.  Stair climbing challenge:  better body and limb sizes... ... i.e., better developmental genomes! all 3D+t simulations: Carlos Sanchez (tool: ODE) 3. MAPDEVO – Modular Architecture by Programmable Development
  35. 35. (a) (b) (c) antennapedia duplication (three-limb) divergence (short & long-limb) PF SA 11 tip p’= .05 GPF GSA 33 p = .05 4 2 disc 6 PF SA 11 tip p’= .1 PF SA 11 tip p’= .03 GPF GSA 33 p = .05 4 2 disc 6 GPF GSA 11 p’= .05tip GPF GSA 33 p = .05 4 2 disc GPF GSA 11 p’= .05tip 4 2 6  To be explored: qualitative mutations in limb structure antennapedia homology by duplication divergence of the homology Doursat (2009) all 2D+t simulations: Rene Doursat (tool: Java) 3. MAPDEVO – Modular Architecture by Programmable Development
  36. 36. BIOMODELING illustrationofMorphogeneticEngineeringideas Systems that are self-organized and architectured  Embryogenesis to Simulated Development to Synthetic Biology 2. MECAGEN – Mechano-Genetic Model of Embryogenesis 4. SYNBIOTIC – Synthetic Biology: From Design to Compilation 3. MAPDEVO – Modular Architecture by Programmable Development BIOENGINEERING Delile, Doursat & Peyrieras Kowaliw & Doursat Doursat, Sanchez, Fernandez, Kowaliw & Vico mutually beneficial transfers between biology & CS Doursat, Fourquet & Kowaliw 5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation 1. Toward Complex Systems Design 1.1. Looking at Natural Complex Systems 1.2. Noticing Architectures Without Architects 1.3. Conceiving Morphogenetic Engineering BIO-INSPIRED ENGINEERING
  37. 37.  Other scale: potential applications in self- constructing techno-social networks by preferential programmed attachment 5. PROGLIM – Program-Limited Aggregation
  38. 38.  Preferential Programmed Attachment Networking (ProgNet)  Diffusion- Program- Limited Aggregation (ProgLim) Doursat, Fourquet, Dordea & Kowaliw (2013) 5. PROGLIM – Program-Limited Aggregation
  39. 39.  Programmed stereotyped development  Environment- Induced Polyphenism Doursat, Fourquet, Dordea & Kowaliw (2012) evol evol 5. PROGLIM – Program-Limited Aggregation
  40. 40. 5. PROGLIM – Program-Limited Aggregation
  41. 41. Carlos Sánchez Doctoral Student Taras Kowaliw, PhD Research Scientist Julien Delile Doctoral Student Acknowledgments David Fourquet Complex Systems MSc Student Razvan Dordea Complex Systems MSc Student

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