Embryomorphic
Engineering: From biological development to self-organized computational architectures René Doursat http://www.iscpif.fr/~doursat
1
Systems that are self-organized and architectured
free self-organization
make components evolve
the engineering challenge of complicated systems: how can they integrate selforganization?
Peugeot Picasso
the scientific challenge of complex systems: how can they integrate a true architecture?
(evolutionary) design
decompose the system
Peugeot Picasso
self-organized architecture / architectured self-organization
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Toward programmable self-organization ¾ Self-organized (complex) systems 9 9 9 9 9
a myriad of self-positioning, self-assembling agents collective order is not imposed from outside (only influenced) comes from purely local information & interaction around each agent no agent possesses the global map or goal of the system but every agent may contain all the rules that contribute to it
¾ Architectured systems 9 true intrinsic structure: non-trivial, complicated morphology hierarchical, multi-scale: regions, parts, details, agents modular: reuse, quasi-repetition heterogeneous: differentiation & divergence in the repetition
9 random at the microscopic level, but reproducible (quasi deterministic) at the mesoscopic and macroscopic levels 3
Embryomorphic Engineering 1.
Toward self-organized and architectured systems
2.
Biological development as a two-side challenge Heterogeneous motion vs. moving patterns
3.
Embryomorphic engineering Morphogenesis as a multi-agent self-assembly process
4.
Evo-devo engineering Evolutionary innovation by development
5.
Extension to self-knitting network topologies 4
¾ Complex systems in many domains
large number of elementary agents interacting locally
simple individual behaviors creating a complex emergent collective behavior
decentralized dynamics: no master blueprint or grand architect
9 physical, biological, technical, social systems (natural or artificial) pattern formation = matter
insect colonies = ant
the brain & cognition = neuron
biological development = cell
Internet & Web = host/page
social networks = person 5
CS in geographical space (F)
Paris Ile-de-France 4th French Complex Systems Summer School, 2010
National
Lyon Rhône-Alpes
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CS in conceptual space: a vast archipelago ¾ Precursor and neighboring disciplines 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
→ Toward a unified “complex systems” science and engineering?
dynamics: dynamics:behavior behaviorand andactivity activityof ofaa system systemover overtime 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
systems sciences: holistic (nonreductionist) view on interacting parts systems theory (von Bertalanffy) systems engineering (design) cybernetics (Wiener; goals & feedback) control theory (negative feedback) multitude, statistics: large-scale properties of systems graph theory & networks statistical physics agent-based modeling distributed AI systems 7
Emergence ¾ The system has properties that the elements do not have 9 ex: micro units form macro patterns: rolls, spiral waves, stripes, spots 9 ex: “ignorant” individuals make intelligent collective decisions: insect colonies, neurons, market traders(?)
¾ These properties cannot be easily inferred or deduced 9 ex: liquid water or ice emerging from H2O molecules 9 ex: cognition and consciousness emerging from neurons
¾ Different properties can emerge from the same elements/rules 9 ex: the same molecules of water combine to form liquid or ice crystals 9 ex: the same cellular automaton rules change behavior with initial state
¾ Global properties make local (sophisticated) rules at a higher level → jumping from level to level through emergence
¾ Counter-examples of emergence without self-organization 9 ex: well-informed leader (orchestra conductor, military officer) 9 ex: global plan (construction area), full instructions (orchestra) 8
From natural to engineered emergence (and back) ¾ The challenges of complex systems (CS) research Transfers among systems
CS science: understanding & modeling "natural" CS (spontaneously emergent, including human-made): morphogenesis, neural dynamics, cooperative co-evolution, swarm intelligence
Exports decentralization autonomy, homeostasis learning, evolution
Imports observe, model control, harness design, use
CS engineering: designing a new generation of "artificial" CS (harnessed & tamed, including nature): collective robotics, synthetic biology, energy networks 9
From “statistical” to “morphological” CS ¾ Most self-organized systems form “simple”/random patterns
texture-like order: repetitive, statistically uniform, information-poor – arising from amplification of fluctuations: unpredictable number/position of mesoscopic entities (spots, groups) – OR determined by the environment (trails)
more architecture
(a) simple/random SO: pattern formation (spots, stripes), swarms (clusters, flocks), complex networks (hubs)...
... while “complicated” architectures are designed by humans (d) direct design (top-down)
more self-organization
gap to fill
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From “statistical” to “morphological” ... to artificial CS (a) natural random self-organization
mesoscopic organs and limbs have intrinsic, nonrandom morphologies – development is highly reproducible in number and position of body parts – heterogeneous elements arise under information-rich genetic control
natural
(b) natural self-organized architectures
more architecture
¾ the only natural emergent and structured forms are biological
....
→ can we reproduce them in artificial systems?
(d) direct design (top-down)
....
more self-organization
(c) engineered self-organization (bottom-up)
artificial
MESOBIONICS
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De facto complexity of engineering (ICT) systems ¾ Ineluctable breakup into myriads of modules/components,
in hardware,
software,
?
number of transistors/year
or networks, ...
?
number of O/S lines of code/year
?
number of network hosts/year 12
Taming complex ICT toward morphogenetic abilities ¾ Self-architecturing in natural systems → artificial systems 9 morphogenetic abilities in biological modeling organism development brain development
9 need for morphogenetic abilities in computer science & AI self-forming robot swarm self-architecturing software self-connecting micro-components http://www.symbrion.eu
9 need for morphogenetic abilities in techno-social networked systems self-reconfiguring manufacturing plant self-stabilizing energy grid self-deploying emergency taskforce
MAST agents, Rockwell Automation Research Center 13 {pvrba, vmarik}@ra.rockwell.com
Toward “evo-devo” engineering ¾ From design to “meta-design” 9 organisms endogenously grow but artificial systems are built genetic engineering exogenously indirect (implicit)
systems design systems "meta-design" www.infovisual.info
9 could engineers “step back” from their creation and only set generic conditions for systems to self-assemble? instead of building the system from the top (phenotype), program the components from the bottom (genotype)
direct (explicit)
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The meta-design of complexity ¾ Pushing design toward evolutionary biology
intelligent & evolutionary “meta-design”
intelligent “hands-on” design heteronomous order centralised control designer as a micromanager rigidly placing components sensitive to part failures need to control and redesign complicated systems: planes, computers
autonomous order decentralised control designer as a lawmaker allowing fuzzy self-placement insensitive to part failures prepare for adaptation & evolution complex multi-component systems 15
The evolutionary “self-made puzzle” paradigm a. Construe systems as selfassembling (developing) puzzles b. Design and program their pieces (the “genotype”) c. Let them evolve by variation of the pieces and selection of the architecture (the “phenotype”)
¾ Genotype: rules at the micro level of agents 9 9 9 9
ability to search and connect to other agents ability to interact with them over those connections ability to modify one’s internal state (differentiate) and rules (evolve) ability to provide a specialized local function
¾ Phenotype: collective behavior, visible at the macro level 16
The evolutionary “self-made puzzle” paradigm a. Construe systems as selfassembling (developing) puzzles b. Design and program their pieces (the “genotype”)
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mutation
mutation 6
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differentiation
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mutation
c. Let them evolve by variation of the pieces and selection of the architecture (the “phenotype”)
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Embryomorphic Engineering 1.
Toward self-organized and architectured systems
2.
Biological development as a two-side challenge Heterogeneous motion vs. moving patterns
3.
Embryomorphic engineering Morphogenesis as a multi-agent self-assembly process
4.
Evo-devo engineering Evolutionary innovation by development
5.
Extension to self-knitting network topologies 18
Devo-inspired engineering ¾ Replacing biological development in the center Computational, spatially explicit models of development and evolution with possible outcomes toward hyperdistributed, decentralized engineering systems
genetics
DEVO
development
PROGNET
Nadine Peyriéras, Paul Bourgine et al. (Embryomics & BioEmergences)
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evolution
EVOSPACE
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ant trail
network of ant trails
mor e in trins i c, so p
ant nest
histi c ated
http://cas.bellarmine.edu/tietjen/TermiteMound%20CS.gif
http://www.bio.fsu.edu/faculty-tschinkel.php
http://picasaweb.google.com/tridentoriginal/Ghana
http://taos-telecommunity.org/epow/epow-archive/ archive_2003/EPOW-030811_files/matabele_ants.jpg
From “statistical” to “morphological” CS in social insect constructions
arch it e ctu re
termite mound 20
From “statistical” to “morphological” CS in inert matter / insect constructions / multicellular organisms mor e in
physical pattern formation
trins i
c, so p
histi c
ated
ant trail
arch it
e ctu
re
network of ant trails
social insect constructions
ant nest
termite mound
biological morphogenesis grains of sand + air
insects
new inspiration cells 21
Morphological (self-dissimilar) systems
compositional systems: pattern formation ≠ morphogenesis
“The stripes are easy, it’s the horse part that troubles me” —attributed to A. Turing, after his 1952 paper on morphogenesis 22
From “statistical” to “morphological” CS ¾ Physical pattern formation is “free” – Biological (multicellular) pattern formation is “guided”
reaction-diffusion
fruit fly embryo
with NetLogo
Sean Caroll, U of Wisconsin
≠
larval axolotl limb condensations Gerd B. Müller
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From “statistical” to “morphological” CS ¾ Multicellular forms = a bit of “free” + a lot of “guided” 9 domains of free patterning embedded in a guided morphology unlike Drosophila’s stripes, these pattern primitives are not regulated by different sets of genes depending on their position spots, stripes in skin angelfish, www.sheddaquarium.org
ommatidia in compound eye dragonfly, www.phy.duke.edu/~hsg/54
9 repeated copies of a guided form, distributed in free patterns entire structures (flowers, segments) can become modules showing up in random positions and/or numbers flowers in tree cherry tree, www.phy.duke.edu/~fortney
segments in insect centipede, images.encarta.msn.com
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Evo-devo engineering ¾ Model embryogenesis ↔ import/export to engineering 9 automated observation and reconstruction of developing organisms by image processing and learning/optimization methods 9 mathematical and computational (agent-based) modeling 9 simulation of recalculated embryos, real and fictitious
RAW embryo images
MEASURED cell coordinates
RECALCULATED embryonic development
ARTIFICIAL embryomorphic engineering
FP6 Projects Embryomics, BioEmergences Submitted ANR Projects MEC@GEN, SYNBIOTIC 25
Overview of morphogenesis ¾ An abstract computational approach to development 9 as a fundamentally spatial phenomenon 9 highlighting its broad principles and proposing a computational model of these principles
¾ Broad principles 1. biomechanics → collective motion → "sculpture" of the embryo 2. gene regulation → gene expression patterns → "painting" of the embryo + coupling between shapes and colors
¾ Multi-agent models 9 best positioned to integrate both 9 account for heterogeneity, modularity, hierarchy 9 each agent carries a combined set of biomechanical and regulatory rules 26
Morphogenesis couples assembly and patterning Ádám Szabó, The chicken or the egg (2005) http://www.szaboadam.hu
¾ Sculpture → forms
¾ Painting → colors
“shape from patterning” 9 the forms are “sculpted” by the selfassembly of the elements, whose behavior is triggered by the colors
“patterns from shaping” ki Ni de Sa int e al l Ph
9 new color regions appear (domains of genetic expression) triggered by deformations
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Morphogenesis couples assembly and patterning SA = self-assembly (“sculpture”) PF = pattern formation (“painting”)
(c)
(e)
(a)
α SA1
α1 α2 . . . α3 PF1
SA3
α3,1
SA2
PF2
...
α3,2 α3,3 PF3
α (d)
(b)
α3
(f)
α3,1
genotype 28
adhesion deformation / reformation migration (motility) division / death
(Doursat)
cellular Potts model (Graner, Glazier, Hogeweg)
9 9 9 9
(Delile & Doursat)
¾ Cellular mechanics
tensional integrity (Ingber)
Morphogenesis couples mechanics and regulation
r
¾ Genetic regulation X
GENE B
GENE B GENE CC GENE
GENE A GENE A
Y
“key” PROT A
A
PROT B
PROT C GENE I “lock”
B Drosophila embryo
I
GENE I after Carroll, S. B. (2005) Endless Forms Most Beautiful, p117
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Gene regulatory pattern formation ¾ Segmentation & identity domains in Drosophila 9
periodic A/P band patterns are controlled by a 5-tier gene regulatory hierarchy
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intersection with other axes creates organ primordia and imaginal discs (identity domains of future legs, wings, antennae, etc.)
from Carroll, S. B., et al. (2001) From DNA to Diversity, p63 30
Morphogenesis couples mechanics and regulation ¾ Cellular mechanics modification of cell size and shape differential adhesion
¾ Genetic regulation gene regulation diffusion gradients ("morphogens")
mechanical stress, mechano-sensitivity growth, division, apoptosis
change of cell-to-cell contacts change of signals, chemical messengers
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Nadine Peyriéras, Paul Bourgine, Thierry Savy, Benoît Lombardot, Emmanuel Faure et al.
¾ Collective motion regionalized into patterns
http://zool33.uni-graz.at/schmickl
Hiroki Sayama (Swarm Chemistry) http://bingweb.binghamton.edu/~sayama/ SwarmChemistry/
zebrafish
Embryomics & BioEmergences
Morphogenesis couples motion and patterns
¾ Pattern formation that triggers motion
Doursat 32
Embryomorphic Engineering 1.
Toward self-organized and architectured systems
2.
Biological development as a two-side challenge Heterogeneous motion vs. moving patterns
3.
Embryomorphic engineering Morphogenesis as a multi-agent self-assembly process
4.
Evo-devo engineering Evolutionary innovation by development
5.
Extension to self-knitting network topologies 33
Overview of an embryomorphic system patt1
¾ Recursive morphogenesis
div2
grad1
genotype
...
patt3
grad3
div1 grad2
div3
patt2 34
div
GSA: rc < re = 1
