morphogenetic engineering: reconciling architecture and self-organization through programmable...
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Rene Doursat's talk from AWASS 2013TRANSCRIPT
Morphogenetic Engineering:Reconciling Architecture and Self-Organization
Through Programmable Complex Systems
BMES Seminar – April 18, 2013
René Doursat
AWASS 2013 Summer SchoolLucca, Italy
Doursat, Sayama & Michel (2012)
Morphogenetic Engineering
Doursat, Sayama & Michel (2012)
Morphogenetic Engineering
illus
tratio
n of
Mor
phog
enet
ic E
ngin
eerin
gid
eas
Systems that are self-organized and architectured
Embryogenesis to Simulated Development to Synthetic Biology2. 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 transfersbetween biology & CS
animalinspiration
5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation
plantinspiration
Doursat, Fourquet & Kowaliw
1. Toward a New Kind of Engineering Based on Complex Systems
Looking at Natural Complex SystemsFocusing on "Architectures Without Architects"Proposing Morphogenetic Engineering
BIO-INSPIREDENGINEERING (“ALIFE”)
Emergence on multiple levels of self-organization“complex systems” a) large number of elementary agents interacting locallyb) simple individual behaviors creating a complex
emergent collective behaviorc) decentralized dynamics: no blueprint or architect
Looking at Natural Complex Systems
Mammal fur, seashells, and insect wings(Scott Camazine, http://www.scottcamazine.com)
Biological pattern formation: Animal colors
ctivator
nhibitor
NetLogo fur coat simulation, afterDavid 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
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?
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
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
the brain organisms ant trails
termitemounds
animalflocks
social networksmarkets,economy
Internet,Web
physicalpatterns
living cell
biologicalpatterns
animals
humans& tech
molecules
cells
All agent types: molecules, cells, animals, humans & techLooking at Natural Complex Systems
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 SystemsLooking at Natural 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
Common Properties of Complex SystemsLooking at Natural Complex Systems
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 SystemsLooking at Natural Complex Systems
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 partssystems 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
the brain organisms ant trails
termitemounds
animalflocks
physicalpatterns
living cell
biologicalpatterns
biology strikingly demonstrates the possibility of combining pureself-organization and anelaborate architecture
there is a whole class of morphological (self-dissimilar) systems, in whichmorphogenesis > pattern formation
“Simple”/“random” vs. architectured complex systems3. 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)
Morphogenetic Engineering
http://iscpif.fr/MEW20091st Morphogenetic Engineering Workshop, ISC, Paris 2009
http://iridia.ulb.ac.be/ants20102nd 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#mew3rd Morphogenetic Engineering Workshop, ECAL 2011, Paris
Morphogenetic Engineering
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 programminga 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 computationfor 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
biochemicalcomputing
syntheticbiology
neuromorphicchips
nanotubecomputing
swarmrobotics
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)
Morphogenetic Engineering
SyntheticBiology
Morphogenetic Engineering
illus
tratio
n of
Mor
phog
enet
ic E
ngin
eerin
gid
eas
Systems that are self-organized and architectured
Embryogenesis to Simulated Development to Synthetic Biology2. MECAGEN – Mechano-Genetic Model of Embryogenesis
4. SYNBIOTIC – Synthetic Biology: From Design to Compilation
3. MAPDEVO – Modular Architecture by Programmable Development
BIOENGINEERING
BIO-INSPIREDENGINEERING
Delile,Doursat & Peyrieras Kowaliw & Doursat
Doursat, Sanchez, Fernandez, Kowaliw & Vico
mutually beneficial transfersbetween biology & CS
Doursat, Fourquet & Kowaliw
5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation
1. Toward Complex Systems Design1.1. Looking at Natural Complex Systems1.2. Noticing Architectures Without Architects1.3. Conceiving Morphogenetic Engineering
BIOMODELING
Processing
Simulation
Phenomenologicalreconstruction2
Model3
Validation5
Raw imaging data1
4Computationalreconstruction
Hypotheses
2. MECAGEN – Mechano-Genetic Model of Embryogenesis
Methodology andworkflow
PhD thesis: Julien Delile (ISC-PIF)supervisors: René Doursat, Nadine Peyriéras
Tens
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Morphogenesis essentially couples mechanics and genetics
Sprin
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ass m
odel
Dour
sat (
2009
) ALI
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I
[A] Cell mechanics (“self-sculpting”)
generegulation
differentialadhesion
modification of cellsize and shape
growth, division,apoptosis
changes incell-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
illus
tratio
n of
Mor
phog
enet
ic E
ngin
eerin
gid
eas
Systems that are self-organized and architectured
Embryogenesis to Simulated Development to Synthetic Biology2. 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 transfersbetween biology & CS
Doursat, Fourquet & Kowaliw
5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation
1. Toward Complex Systems Design1.1. Looking at Natural Complex Systems1.2. Noticing Architectures Without Architects1.3. Conceiving Morphogenetic Engineering
BIOMODELING
BIO-INSPIREDENGINEERING
pA
BV
rr0rerc
GSA: rc < re = 1 << r0p = 0.05
3. MAPDEVO – Modular Architecture by Programmable Developmentall 3D+t simulations:
Carlos Sanchez (tool: ODE)
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
[A] “mechanics”
27Bi = (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
I4 I6
rc = .8, re = 1, r0 = r'e= r'0=1, p =.01GSA
SAPF
SA4PF4
SA6PF6
SAPF
SA4PF4
SA6PF6
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)
from Coen, E. (2000)The Art of Genes, pp131-135
all 2D+t simulations:R. Doursat (tool: Java)
3. MAPDEVO – Modular Architecture by Programmable Development
Limbs’ development
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
all 3D+t simulations:Carlos Sanchez (tool: ODE)
3. MAPDEVO – Modular Architecture by Programmable Development
Doursat, Sanchez, Fernandez, Kowaliw & Vico (2012)
3D Development all 3D+t simulations:Carlos Sanchez (tool: ODE)
3. MAPDEVO – Modular Architecture by Programmable Development
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 properties3. MAPDEVO – Modular Architecture by Programmable Development
Quantitative mutations: limb thickness
GPF
GSA
331, 1
p = .05g = 15
4 6
disc
GPF
GSA
11
tip p’= .05g’= 15
GPF
GSA
11
tip p’= .05g’= 15
GPF
GSA
332, 1 4 6
disc p = .05g = 15
GPF
GSA
11
tip p’= .05g’= 15
GPF
GSA
330.5, 1 4 6
disc p = .05g = 15
(a) (b) (c)
wild type thin-limb thick-limb
body planmodule
limbmodule
4 6
Doursat (2009)all 2D+t simulations:Rene Doursat (tool: Java)
3. MAPDEVO – Modular Architecture by Programmable Development
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
Stair climbing challenge: better body and limb sizes...
... i.e., betterdevelopmental genomes! all 3D+t simulations:
Carlos Sanchez (tool: ODE)
3. MAPDEVO – Modular Architecture by Programmable Development
(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
42
6
To be explored: qualitative mutations in limb structureantennapedia homology by duplication divergence of the homology
Doursat (2009)all 2D+t simulations:Rene Doursat (tool: Java)
3. MAPDEVO – Modular Architecture by Programmable Development
BIOMODELING
illus
tratio
n of
Mor
phog
enet
ic E
ngin
eerin
gid
eas
Systems that are self-organized and architectured
Embryogenesis to Simulated Development to Synthetic Biology2. 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 transfersbetween biology & CS
Doursat, Fourquet & Kowaliw
5. PROGLIM – Self-Constructed Network by Program-Limited Aggregation
1. Toward Complex Systems Design1.1. Looking at Natural Complex Systems1.2. Noticing Architectures Without Architects1.3. Conceiving Morphogenetic Engineering
BIO-INSPIREDENGINEERING
Other scale: potential applications in self-constructing techno-social networks by preferentialprogrammed attachment
5. PROGLIM – Program-Limited Aggregation
PreferentialProgrammed Attachment Networking (ProgNet)
Diffusion-Program-Limited Aggregation (ProgLim)
Doursat, Fourquet, Dordea & Kowaliw (2013)
5. PROGLIM – Program-Limited Aggregation
Programmed stereotyped development
Environment-Induced Polyphenism
Doursat, Fourquet, Dordea & Kowaliw (2012)
evol evol
5. PROGLIM – Program-Limited Aggregation
5. PROGLIM – Program-Limited Aggregation
Carlos SánchezDoctoral Student
Taras Kowaliw, PhDResearch Scientist
Julien DelileDoctoral Student
Acknowledgments
David FourquetComplex Systems MSc Student
Razvan DordeaComplex Systems MSc Student