Morphogenetic Engineering: Reconciling Architecture and Self-Organization Through Programmable Complex Systems - Rene Doursat

Morphogenetic Engineering:
Reconciling Architecture and Self-Organization
Through Programmable Complex Systems
BMES Seminar – April 18, 2013
René Doursat
AWASS 2013 Summer School
Lucca, Italy

Doursat, Sayama & Michel (2012)
Morphogenetic Engineering

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”)

 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

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?

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?
NetLogo Ants simulation
HOW?

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?
(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

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?

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
cities,
populations

 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

 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

 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

 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

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"

 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)

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

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

 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

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

 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)
Synthetic
Biology

of Embryogenesis
BIOENGINEERING
BIO-INSPIRED
ENGINEERING
Delile,
1. Toward Complex Systems Design
1.1. Looking at Natural Complex Systems
1.2. Noticing Architectures Without Architects
1.3. Conceiving Morphogenetic Engineering
BIOMODELING

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

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

of Embryogenesis
BIOENGINEERING
Delile,
BIOMODELING
BIO-INSPIRED
ENGINEERING

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
Bi = (Li(X, Y)) = (wix X + wiy Y  i), Ik = i |w'ki|(w'kiBi + (1w'ki)/2)
Rene Doursat (tool: Java)

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
R. Doursat (tool: Java)

 Limbs’ development

 3D Development

Carlos Sanchez (tool: ODE) Locomotion and behavior by muscle contraction
 Bones & muscles: structural differentiation and properties

 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)

 Stair climbing challenge Hooves and horseshoes
Fitness = | end – start | / path length < 1

 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
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)

BIOMODELING
of Embryogenesis
BIOENGINEERING
Delile,
BIO-INSPIRED
ENGINEERING

 Other scale: potential
applications in self-
constructing techno-social
networks by preferential
programmed attachment
5. PROGLIM – Program-Limited Aggregation

 Preferential
Programmed
Attachment
Networking
(ProgNet)
 Diffusion-
Program-
Limited
Aggregation
(ProgLim)
Doursat, Fourquet, Dordea & Kowaliw (2013)

 Programmed
stereotyped
development
 Environment-
Induced
Polyphenism
Doursat, Fourquet, Dordea & Kowaliw (2012)
evol evol

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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