This document discusses the concept of morphogenetic engineering, which aims to design artificial self-organized systems capable of developing elaborate architectures without central planning. It begins by looking at natural complex systems like animal flocking and termite mounds that self-organize. The focus is on "architectures without architects" in biological systems. Morphogenetic engineering is proposed as a new type of engineering that designs self-organizing agents, not the architectures directly, taking inspiration from embryogenesis, simulated development and synthetic biology. Several research projects are summarized that aim to model biological development and create modular, programmable artificial self-construction.
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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. 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
Bi = (Li(X, Y)) = (wix X + wiy Y i), Ik = i |w'ki|(w'kiBi + (1w'ki)/2)
all 2D+t simulations:
Rene Doursat (tool: Java)
3. MAPDEVO – Modular Architecture by Programmable Development
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. Limbs’ development
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
all 3D+t simulations:
Carlos Sanchez (tool: ODE)
3. MAPDEVO – Modular Architecture by Programmable Development
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. 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. Quantitative mutations: limb thickness
GPF
GSA
33
1, 1
p = .05
g = 15
4 6
disc
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
33
2, 1 4 6
disc
p = .05
g = 15
GPF
GSA
11
tip p’= .05
g’= 15
GPF
GSA
33
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. 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. 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. (a) (b) (c)
antennapedia duplication
(three-limb)
divergence
(short & long-limb)
PF
SA
11
tip p’= .05
GPF
GSA
33
p = .05
4 2
disc
6
PF
SA
11
tip p’= .1
PF
SA
11
tip p’= .03
GPF
GSA
33
p = .05
4 2
disc
6
GPF
GSA
11
p’= .05tip
GPF
GSA
33
p = .05
4 2
disc
GPF
GSA
11
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. 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. Other scale: potential
applications in self-
constructing techno-social
networks by preferential
programmed attachment
5. PROGLIM – Program-Limited Aggregation