Morphogenetic Engineering: Reconciling Architecture and Self-Organization Through Programmable...

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)

tratio

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?

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”

NetLogo Ants simulation

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)

Collective motion: flocking, schooling, herding

coordinated collective movement of dozens or thousands of individualsWHAT?

(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

the brain organisms ant trails

termitemounds

animalflocks

social networksmarkets,economy

Internet,Web

physicalpatterns

living cell

biologicalpatterns

animals

humans& tech

molecules

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

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 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

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)

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

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)

SyntheticBiology

tratio

BIOENGINEERING

BIO-INSPIREDENGINEERING

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

l inte

, Glaz

ier, H

t, sim

y Deli

Morphogenesis essentially couples mechanics and genetics

[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

tratio

BIOENGINEERING

BIOMODELING

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)

rc = .8, re = 1, r0 = r'e= r'0=1, p =.01GSA

SA4PF4

SA6PF6

SA4PF4

SA6PF6

all cells have same GRN, but execute different expression paths determination / differentiation

microscopic (cell) randomness, but mesoscopic (region) predictability

Doursat(2008, 2009)

S(4)W(4) E(4)

from Coen, E. (2000)The Art of Genes, pp131-135

all 2D+t simulations:R. Doursat (tool: Java)

Limbs’ development

all 3D+t simulations:Carlos Sanchez (tool: ODE)

3D Development all 3D+t simulations:Carlos Sanchez (tool: ODE)

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

331, 1

p = .05g = 15

tip p’= .05g’= 15

332, 1 4 6

disc p = .05g = 15

tip p’= .05g’= 15

330.5, 1 4 6

disc p = .05g = 15

(a) (b) (c)

wild type thin-limb thick-limb

body planmodule

limbmodule

Doursat (2009)all 2D+t simulations:Rene Doursat (tool: Java)

Stair climbing challenge Hooves and horseshoes

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)

(a) (b) (c)antennapedia duplication

(three-limb)divergence

(short & long-limb)

tip p’= .05

p = .05

tip p’= .1

tip p’= .03

p = .05

p’= .05tip

p = .05

p’= .05tip

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)

BIOMODELING

tratio

BIOENGINEERING

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)

Programmed stereotyped development

Environment-Induced Polyphenism

Doursat, Fourquet, Dordea & Kowaliw (2012)

evol evol

Carlos SánchezDoctoral Student

Taras Kowaliw, PhDResearch Scientist

Julien DelileDoctoral Student

Acknowledgments

David FourquetComplex Systems MSc Student

Razvan DordeaComplex Systems MSc Student

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