Nicolò Friedman

Mach Graduate Architectural Design_Bartlett School of Architecture_University College London
Tutors: Alisa Andrasek, Jose Manuel Sanchez Student: Nicolò Friedman

BIOmimetic-Fabric

Nature as a “recipe” for Generative Design

Mach Graduate Architectural Design_Bartlett School of Architecture_University College London
Tutors: Alisa Andrasek, Jose Manuel Sanchez Student: Nicolò Friedman

BIOmimetic-Fabric
Nature as a “recipe” for Generative Design
1// ABSTRACT
• ………………………………….……………………………………..…………….………...i
2// INTRODUCTION
• ……………………………………………………..……………………………………….p.1
3// THEORETICAL BASIS
• 1// The role of variation in evolution:
……………………………………………...…..…..……………..………………….…….p.2
• 2// Biology and computational embriology:
1a) Developmental Biology and Embriology…..…..……………..………………….…….p.4
2a) Computational embryology…………….…..…..……………..………………….…….p.6
3a) The Evolutionary Development System.…..…..……………..………………….…….p.7
• 3// Self-organization for Collective behavior:
1b) Complex systems……...………………….…..…..…………..………………….…….p.9
2b) Self-organization……………………….…..…..……………..……………...……….p.10
3b) Basic group behaviors……………………….…..…..………………………….…….p.11
• 4// Self-organization for patterns formation:
1c) Self-organization……...…..……………….……...…………..…………..…….…….p.13
2c) Reaction-diffusion in animal coat pattern………………...…..………………...…….p.17
4// THE RESEARCH
• The concept………………………………………..………………………………..…….p.20
• Selected images…………………………………..……………………………………….p.21

5// CONCLUSION
• ………………………………….……………………………………..…………….…….p.31
6// SELECTED REFERENCES
• ………………………………….……………………………………..…………….…….p.32

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i

2//ABSTRACT
Nowadays Generative design is becoming one of the most powerful processes to build up forms
with an architectural aim. Actually, these fabrics have the ability to be extremely versatile and able
to adapt according to external demands of the environment. One the most fascinating aspect of this
method is represented by its theoretical bases which take their inspiration from biological processes
related to the ability of generate forms. The unrelenting rise of this theory has been possible thanks
to the research concerning Computation and the introduction of self-organization concept and
complex systems theory.
This paper briefly analyzes some of these theoretical aspects and provides a practical example of
application through some images of the Research1
which demonstrate how to set up a design project
within this territory. After describing the role of variation in evolution, three main concepts are
introduced: computational embryology, collective behavior and patterns formation through self-
organized systems.
All these “ingredients” are accurately mixed in order to compose a “recipe” for designing adding
imagination and speculative ability to push the system for aesthetic and functional expression.

1
" The research project referred to in this report is the collective effort of Team Variance, a research group comprised
of the following four MArch Graduate Architectural Design students: Vincenzo D'Auria, Nicolo Friedman, Mark
Geoffrey Muscat and Pallavi Sharma. Each member's report deals with a different aspect of the research project. "

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1
2//INTRODUCTION
Biology, considered as a set of configurations to generate living forms, has rapidly grown to be a
crucial part of the theory behind generative process both in engineering and architectural practice.
This phenomenon is mainly due to two different reasons, the availability of a great number of
inexpensive simulation software and the introduction of complex systems theory -and of the science
of complexity into the design theory and practice.
After a brief description of the role of variation in evolution (necessary to introduce the meaning of
Evolvability and Selection as key concepts in the generation of forms or organisms), this paper will
go through some of the fundamental theoretical basis related to this territory.
Firstly, the concept of computational embryology is introduced focusing on his close relationship
with developmental biology and providing a practical example of the construction of the
Evolutionary Development System (EDS), an object-oriented model referring to these natural
processes. Looking at embryology as a reference means understanding the process of growth and
the evolution of the zygote, the way in which different components can act together for ending in an
high-specialized form.
Secondly, a concise definition of complex systems and self-organization helps us to introduce the
theory of collective behavior and patterns formation. While collective behavior means dealing with
agents (the basic components of complex systems), which have the ability to produce global
behavior starting from local neighborhood interactions, animal coat patterns are scientifically
explained through Reaction-Diffusion system. This system, which was firstly investigated by Alan
Turing, starting from a mixture of two chemical compounds, can generate a huge catalogue of
different patterns.
Concluding, some meaningful images of the Research1
are included to demonstrate how these
theoretical bases can be used as a source to generate design in the field of Architecture. In fact, for
instance, agency logic allows to create high-resolution patterns that change locally conformation
depending on the global behavior of agents and the versatility of the Reaction-Diffusion algorithm
provides an intricate distribution of matter in an organic form.

1
" The research project referred to in this report is the collective effort of Team Variance, a research group comprised
of the following four MArch Graduate Architectural Design students: Vincenzo D'Auria, Nicolo Friedman, Mark
Geoffrey Muscat and Pallavi Sharma. Each member's report deals with a different aspect of the research project. "

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2
3//THEORETICAL BASIS
1// The role of variation in evolution
In order to completely understand evolution, we have to focus our attention on the process of
generating structure. Nowadays research related to modern biology is mainly concerned with the
question of how form is generated and how important is evolution during this phenomenon.
Darwin’s theory2
clearly shows the idea of variability of organisms seconded by their own fitness
during each generation but do not answer the question of how these organisms achieve their genetic
variation. On the other hand, biologists of nineteenth century focused their research in this process.
Going deeply in the the theory of evolution we need to think about a group of individuals, better
defined as population, which change their traits. Some of these traits, called dominant, are more
suitable to environmental conditions and inevitably affect the next generation. For this reason the
theory of evolution is closely linked to the concept of natural selection which forces each individual
to be in competition with others. The dominant traits, generation after generation, lead to a gradual
mutation and are always connected to a need. For instance, the giraffe’s neck is stretched to allow
the animal to eat higher leaves.
Another important aspect in the evolutionary process is represented by the concept of evolvability.
Evolvability is the ability of a population of organisms to generate adaptivegenetic diversity and it
has to have three characteristics. Firstly, the possibilities of variation are relatively limited,
secondly, the fallacy of variation is suppressed because it does not contribute to evolution and
finally the idea of provision of useful variation.
Concluding, we realize that the evolutionary process can not be considered random although the
perturbation and the mutation of the system is completely random. Selection becomes the main
factor that affects evolution.
Wanting to create a connection between evolutionary novelty and human design, it is clearly shown
that the interest is not defined in a geometric form, but in the characteristics of the process that is
regulated by constraints and deconstraints.
“ …These are constraints, because any change in them will be lethal. They are also deconstraints,
because they allow a generation of novelty. This is something we see in other form of human
behaviour, in art and architecture, and in social organization. Evolvability itself has evolved...”3

2
Darwin’s theory is based on three fundamental principles: the theory of mutation, which could also be called theory of
inheritance, the theory of variation and the theory of selection.

3
Mark Kirschner, “Variations in Evolutionary Biology”, Lars Spuybroek ,The Architecture of Variation, (Thames &
Hudson, 2009), pp. 32-33.

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3
(a)
(a) Poodles
AKC/UKC approved Puppy Clip4

4
Lars Spuybroek, The Architecture of Variation, (Thames & Hudson, 2009), pp. 76-83.

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2// Biology and computational embryology
1a) Developmental Biology and Embryology
“…Life is clearly the most complex of all designs to have evolved,
making our very best evolved designs look absurd in their simplicity …”
- Bentley, P. J. & Kumar, S.
Three Ways to Grow Designs5
–
Nowadays Developmental Biology and in particular, Embryology is one of the most exiting subject
for research and a substantial reference for Evolutionary Computation. Today we consider
Embryology the study of formation and development of animal and plant embryos. The role of
development is the key to understanding the actions that lead from egg to embryo to adult. Three
fundamental processes are involved: morphogenesis 6
, regional specification 7
, cellular
differentiation8
. These actions collaborate in certain areas of the embryo at different times and
during specific phases in according to a “recipe” known as an embryogeny9
.
In nature, development begins with a single cell: the fertilized egg, or zygote. In addition to
receiving genetic material from its own parents, the zygote is seeded with a set of proteins – the so-
called “material factors” deposited in the egg by the mother (Wolpert, 1998). After that the material
factors force the zygote to cleave10
, cells begin to divide and differentiate in order to develop a not
homogenous embryo. DNA controls the development and proteins express or repress genes through
signals within cells and other nearby cells in order to standardize the complex processes of cellular
differentiation, patter formation, morphogenesis and growth.

5
(Bentley, P. J. & Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies
for an Evolutionary Design Problem. In Genetic and Evolutionary Computation
Conference (GECCO) Orlando, Florida, USA., introduction, p.1)

6
Morphogenesis – which involves the emergence and change of form (Bard, 1990).
(Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future )
7
regional specification ( pattern formation ) – in which compartmentalization of the embryo into specific regions
occurs (Slack, 1991).
8
cellular differentiation – in which cells become specialized for particular functions (Wolpert, 1998).
9
An embryogeny is the process of growth that defines how a genotype is mapped onto a phenotype.
(Bentley, P. J. & Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies
Conference (GECCO) Orlando, Florida, USA.)
Genotype: the entire genetic constitution of an individual;
Phenotype: the observable physical or biochemical characteristics of an organism, as determined by both genetic
makeup and environmental influences;
(“Medical Dictionary”, http://medical-dictionary.thefreedictionary.com, (accessed 2 June 2012) )
10
cleave (fast cell division with no growth)
(Sanjeev Kumar , Peter J. Bentley, Biologically Inspired Evolutionary Development, p.2)

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Gallus gallus.
“The Chicken embryo is a staple educational tool in developmental biology. Their availability and
similarities with mammalian embryo, help shape our present understanding of embryology. After 21
days of incubation, the chick attempts to break out of its shell, pushing its beak through the air cell.
Since the specimens were received out of the egg and without its yoke, I lacked the ability to
document the chicken’s interaction in its element. The specimens document a range from 5, 6, 9,
12, to 18 days of development11
”.

11
“Chicken
embryo
–
Microscopy
UK”,
http://www.microscopy-uk.org.uk/mag/artnov04macro/mlchicken.html
(accessed 22 April 2012)

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2a) Computational embryology
“…Nature has been successfully evolving complex animals for millions of years.
It is the concept of an embryogeny (which itself evolved in nature)
that has allowed the evolution of these complex design…”
- Bentley, P. J. & Kumar, S.
Computational Embryology12
–
Evolutionary computation (EC), actualized through several types of evolutionary algorithms
inspired from nature, is one of the most successful area of computer science. The possibility to
investigate these territories was materialized for the first time by Alan Turing in 1952 with his
studies related to morphogenesis. However, The first studies used crude approximations of the
natural embryological processes ignoring important factors such as Regional specification.
Current computational embryology can be divided into three different type: external, explicit and
implicit. Briefly, while external embryogenies are defined globally and externally to genotypes,
explicit models specify each step of the growth process in the form of explicit instructions13
. Lastly,
in an implicit embryogeny the growth process is implicitly specified by a set of rules similar to a
“recipe” that govern the growth of a shape.
Looking in detail at the four major types of evolutionary algorithm (EA),[the genetic algorithm
(GA), evolutionary programming (EP), evolutionary strategies (ES) and genetic programming (GP)]
the genetic algorithm (GA) can be considered the most accurate. In fact the GA is the only one able
to deal separately genotype from phenotype, using a mapping stage from the beginning of its
development.
Figure a: Schematic illustrating a basic concept in genetic algorithms - that there are two components to a
representation: the phenotype and the genotype. The genotype is an encoded set of parameters that determine attributes
in the phenotype. The phenotype is evaluated by an objective fitness function or a human evaluator, and the genotype is
affected by this evaluation, through the operators of the genetic algorithm14

12
(Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future, conclusions )

13
(Sanjeev Kumar , Peter J. Bentley, (2003), Computational embryology: past, present and future, computational
embryology )

14
“Background
and
Related
Work”,
http://www.ventrella.com/Alife/Thesis/background.html, (accessed 15 April
2012)

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3a) The Evolutionary Development System
After having defined the relationship between Developmental Biology and Computational
Embryology, i present an overview of a plausible model of development for evolutionary design
very close to biological growth in order to discover the key components and their potential for
computer science.
The Evolutionary Development System (EDS) is an object-oriented model comprising individual
characters of these biological processes within a computer model. Genes, proteins and cells can be
considered the basic ingredients of the system and the embryo is composed by a collection of cells
and proteins contained by them.
Figure b: A single cell in the EDS15

15

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8
In the process of growth, proteins may be regarded as the engine for the development. The EDS
treats the proteins as objects and gives them an ID tag (integer number). Moreover, proteins do not
exist in isolation but are linked to cells and have specific spatial coordinates. In addition, protein
molecules diffuse. Diffusion is the process by which molecules spread or wander due to thermal
motions (Alberts et al., 1994)16
. It represents an efficient method for molecules to move short
distances, but an inefficient method to move over large distances. Generally, small molecules move
faster than large molecules (Alberts et al., 1994)17
.
Two genomes are employed by the system. The first contains protein specific values, the second
describes the architecture of the genome to be used for development. In embryology, the role of
genes is twofold: they comprise the cis-regulatory region18
( Davidson, 2001) and the coding
region19
. The EDS considers a “one gene, one protein” simplification rule and the activation of a
single gene results in the transcription of a single protein.
Finally, cells are the the last ingredient of the “recipe”. They are viewed as agents performing
different behaviors ( multiply, differentiate, and die). In addition, the cell membrane plays an
important role as it becomes a receptor of the presence of certain molecules within the environment.
To develop the growth of the model a genetic algorithm (GA) is used. The algorithm provides
genotype for development and a task of function, Individuals within the population of the genetic
algorithm comprise a genotype, a phenotype and a fitness score. After the population is created,
each individual has its fitness assessed through the process of development and is permitted to
execute its program according to the instructions in the genome20
.

16

17

18
Cis-regulatory regions are located just before their associated coding regions and effectively serve as switches that
integrate signals received from both the extra-cellular environment and the cytoplasm.

19
Coding regions specify a protein to be transcribed upon successful occupation of the cis-regulatory region by
assembling transcription machinery.

20

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3// Self-organization for Collective behavior
1b) Complex Systems
“…Simple and complex systems exhibit…
the spontaneous emergence of order, the occurrence of self-organization…”
- S. A. Kauffman, The Origins of Order:
Self-Organization and Selection in Evolution –
Complex systems have emergent properties and their behaviors are unpredictable and
uncontrollable. On the other hand, they are characterized by an irreversible evolution, by an “arrow
of time” that points unambiguously from the past to the future, and that allows no turning back
(Prigogine & Stengers, 1984).
Looking at their multiple positive features, i can underline adaptivity, autonomy, robustness and
other aspects all related to the process of self-organization. Moreover, these systems spontaneously
organize themselves to cope external and internal stresses and, after evolving, they become more
complex, “mind-like” and less “matter-like”21
. Thus, the arrow of time tends to point towards an
improved, better organized or more adapted version of the evolving system (Stewart, 2000).22
The components of complex systems are called agents. Generally, agents individually follow a
simple cause-and-effect or condition-action logic but as the same time are affected by other agents’
activities. Although these interactions are initially local and only related to neighborhood
conditions, consequently they become global and affect the system as a whole.
Another important aspect linked to agency logic is that their interactions are very sensitive to initial
condition. A small and undetectable initial change may generate a drastically altered outcome23
.
This means that even though the dynamic of the system is deterministic, the result is often
unpredictable.

21
In the philosophy of dualism the world is seen to be made out of two substances: matter and mind. In complex
systems these two features are different aspects of the same phenomenon of organization.
(Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references
and collective intelligence, Vrije Universiteit Brussel, p.2)

22
Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared
references and collective intelligence, Vrije Universiteit Brussel, p.2
23
This phenomenon is called “butterfly effect”: after the observation that, because of the non-linearity of the system of
equations governing the weather, the flapping of the wings of a butterfly in Tokyo may cause a hurricane in New York.

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2b) Self-organization
Nowadays the notion of self-organization has substantial applications in computer science because
it is seen as a solid reference for designing systems without centralized control. Finally, self-
organization can explain previously mysterious phenomena linked to complex structures generated
by basic interactions between components.
A self-organizing system may be characterized by global, coordinated activity arising
spontaneously from local interactions between the system’s components or “agents”.24
This
phenomenon affects all components of the system without the need of a central supervisor or
director of the global behavior. I am referring to simple interactions at local levels that generate
complex solutions at the global level.25
Organization can be considered as a structure with function, in fact agents of the system work
together in order to reach a common goal building a structure given by their general behavior. To
clarify this concept i need to introduce the notion of coordination (Crowston et al., 2006)26
. The key
point is the collective work and the way in which agents can globally behave as a single element.27
Moreover, agents can find solutions working together, the same solutions that would not be able to
find individually. This phenomenon is called synergy (Corning, 1998; Heylighen, 2007, 2008).
“Coordination can be defined as: the structuring of actions in time and (social) space so as to
minimize friction and maximize synergy between these actions.”28

24
Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of coordination, shared references
and collective intelligence, Vrije Universiteit Brussel, p.3

25
This phenomenon is called emergence

26

27
This is what Heylighen has called teh avoidance of friction.

28

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3b) Basic group behaviors
Coordination can be divided in four basic behaviors: alignment, division of labor, workflow and
aggregation.
Alignment is the simplest group action: it means that agents aim the same goal or point the same
final target. Looking into a group of agents, the more are already aligned, the larger the force in the
direction of their alignment, the more difficult it will be for others to oppose that movement, but
easier it will be for them to join in with that movement. Moreover, if agents start in an extended
region of space, it could happen that the agents of one region start to align on one direction, while
those in another region align on a different direction.29
This phenomenon creates local homogeneity
but global heterogeneity. Since agents align with the neighbors they have the strongest interactions
with, the borders between the regions will be where the initial interactions are weakest.
Figure c: global alignment of directions of action, from random (left) to homogeneous (right).30
Figure d: local alignment of directions of action, from random (left) to locally homogeneous, but grobally
heterogeneous (right).31

29
Heylighen F., Self-organization in Communicating Groups: the emergence of coordination, shared references and
collective intelligence, Vrije Universiteit Brussel, p.6

30

31

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Division of labor, instead, is related to the possibility of agents to perform different actions,
specialized in what they do better. Therefore, since agents have reduced abilities, the whole system
has the ability to compensate the lack of individuals. Referring to self-organizing logic, agents
prefer to do actions they are most skilled at, so they will pick up these tasks, leaving less fitting
ones to the others. Thus, the number of remaining tasks will gradually diminish.32
While division of labor coordinates activities that happen in parallel, Workflow (van der Aalst &
van Hee, 2004) coordinates activities sequentially. The mechanism is related to the idea that an
agent, after finishing the task he is most skilled at, will look around to find another task that may fit
its profile and will perform it or will finish to perform another agent’s task. This kind of behavior
can be found in animal collaboration: social insect, such as ants and termites, perform sequentially
and in parallel complex activities.
Finally, to understand the benefits of synergic action, aggregation (Surowiecky, 2005) has to be
introduced. It is a parallel process: different streams of activity come together simultaneously.33
A
practical example can be found in the organization of ant societies. In fact, army ants leave a trace
of pheromones to remember where to find food. Using this logic, after a while, ants create a
network of pheromone trails connecting their nest to all the surrounding food sources.

32

33

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4// Self-organization for patterns formation
1c) Self-organization
“Technological systems become organized by commands
from outside, as when human intentions lead to the building
of structures or machines. But many natural systems
become structured by their own internal processes: these are
the self-organizing systems, and the emergence of order
within them is a complex phenomenon that intrigues
scientists from all disciplines.”
- F. E. Yates et al., Self-Organizing System:
The Emergence of Order –
Self-organizing systems can be found both in the field of biology and in physics. These systems,
based on pattern-formation processes, have the power to achieve their own order and their structure
through internal interactions and not by the intervention of external directives. Some examples can
be found in the aggregation of sand grains assembled in dunes and fish moving together in schools.
In order to clarify the relationship of these systems with the formation of patterns in biology, i quote
the following definition:“Self-organization is a process in which pattern at the global level of a
system emerges solely from numerous interactions among the lower-level components of the system.
Moreover, the rules specifying interactions among the system’s components are executed using only
local information, without reference to the global pattern.”34
Another important aspect to underline is the meaning of pattern. Related to this research, pattern is
a group of items organized in a given space according to a time. These patterns can be built by
living units or inanimate objects.35
However, in the both cases subjects are able to build pattens
without external orders and influences but using local information. Each element relates his
behavior to his nearest neighbors without knowing absolutely the global behavior.
Moreover, these elements use simple behavioral rules in a local level in order to build complex
patterns in global level. Therefore systems are defined complex because of their global result.
Choosing to focus on biological systems, it is clear that the mechanism is more complex than the
process which involves physical systems. Firstly, the subunits are living elements so the interactions
between them are more complex. Secondly, biological systems have to submit the law of physics
as well as their own genetic properties. This dual aspect drives systems to specific and stronger
interactions.

34
What is Self-Organization?, Part I, S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau,
Self-Organization in Biological System, (Princeton University Press, 2001), p.8.

35
Examples of living units are animals as fish, ants or cells while examples of inanimate objects are bits of dirt or sand
grains.

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(b)
(c)
(b) skin pigmentation on fish (clockwise from top – vermiculated rabbitfish (Siganus vermiculatus),
male boxfish (Ostracion solorensis), and surgeonfish (Acanthurus lineatus));
(c) zebra and giraffe coat patterns;36

36
S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-Organization in Biological
System, (Princeton University Press, 2001), p.10.

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(d) (e)
(f) (g)
(h) (i)
Self-organized pattern formation in physical and chemical systems. (d) Wind-blown ripples on the
surface of a sand dune. (e) Spiral waves produced by the Belousov-Zhabotinski chemical reaction.
(f) Pattern of cracks produced by muda s it dries and shrinks along the shore of a pond. (g)
Hexagonal pattern of Bènard convection cells created when a thin sheet of viscous oil is heated
uniformly from below. A small amount of aluminum powder has been added to the oil to reveal the
pattern of convection. (h) Polygonal pattern of cracks on wooden surface. (i) Wrinkle pattern
formed by a coat of varnish on a wooden surface.37

37
System, (Princeton University Press, 2001), Plate 1.

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(l) (m)
(n) (o)
(p) (q)
Animal coat patterns and insect coloration belived to involve self-organized pattern formation. (l)
Zebra, Equus grevii. (m) Giraffe, Giraffa sp. (n) Tiger, Felis tigris. (o) Gila monster, Heloderma
suspectum. (p) Rice paper or tree nymph butterfly, Idea leuconoe. (q) Locust borer beetle,
Megacyllene robiniae.38

38
System, (Princeton University Press, 2001), Plate 2.

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2c) Reaction-diffusion in animal coat patterns
“… and after long time, what with standing half in the shade
and half out of it, and what with the slipperry-slidy shadows of the trees
falling on them, the Giraffe grew blotchy, and the Zebra grew striply,
and the Eland and the Koodoo grew darker, with little wavy grey lines
on their backs like bark on a tree trunk; and so, though you could hear
them and smell them, you could very seldom see them, and then only
when you knew precisely where to look.”
- Rudyard Kipling
The Just So Stories –
Animal coat patterns are extremely important for two reasons. Firstly, patterns help animals to
blend with the surrounding environment: for this reason prey are less visible by predators and
predators can hide better and have more opportunity to attack their prey. Secondly, animals of the
same species are able to recognize other members easily because they have got the equal patterns.
These two reasons are correct but we need something more. We want to understand how patterns
are formed and why they reach a final configuration over another. The first attempt to answer can
be found in the research of Alan Turing, a British mathematician who published in 1952 a paper
entitled “The chemical basis of morphogenesis”. Specifically, his assay brings out the idea of a
reaction-diffusion system which can generate symmetry breaking, leading to stable spatial patterns,
in an initially uniform mixture of chemical compounds. 39
Moreover, Turing realized that
manipulating the chemical concentrations of the systems, the final patterns can considerably
change. The mechanism is based on the relationship between activation by compound A and
inhibition by compound B. This process can generate patterns of stripes or spots.
One of the most important applications of the reaction-diffusion system is found in animal coat
patterns. The strength of the process is demonstrated by the fact that, using the same mechanism,
the system can reach extremely different results. Different patterns can be generated by changing
the reaction conditions.
Another fundamental constraint in the growth of patterns is represented by the size and the shape of
the region concerned. Looking at animal tails as an example of study, we can easily understand how
the size affects the formation of patterns: in small tails only bands are formed while in bigger ones
more complex systems are produced.
Concluding, we underline the role of the size and shape of the embryo at the time of pre-patterning.
One implication of this is that small animals with short gestation periods should have less complex
pelt patterns than larger animals, because their smaller embryos support fewer modes.40

39
Philip Ball, The self-made tapestry, (Oxford University Press, 1999) p. 79.

40

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18
(r) How an activator-inhibitor scheme works. The activator
generates more of itself by autocatalysis, and also activates the
inhibitor. The inhibitor disrupts the autocatalytic formation of the
activator. Meanwhile, the two substances diffuse through the
system at different rates, with the inhibitor migrating faster.41
(r)
1 2 3
(s) The patterns produced on tapering cylindrical “model tail” by an activator-
inhibitor scheme depends on their size and shape. Small cylinders support only
bands (stripes) (1), whereas spots appear on larger cylinders (2) as they widen.
On a more slowly tapering tail (3), the transition from bands to spots is more
clear.42
(s)
4 5
6 7 8
(t)
(t) The adult zebra Equus grevyi (5) has more and narrower stripes than the adult Equus burchelli
(4). This is thought to be because the striped “ pre-pattern” is laid down on the embryo of the latter
at an earlier stage: after 21 days for Equus burchelli (6), but after 5 week for Equus grevyi (8). The
smaller embryo supports fewer stripes, and so by the time it is of comparable size (7), its stripes are
wider.43

41

42

43

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4// THE RESEARCH
group name //VARIANCE;
tutors: Alisa Andrasek, Jose Manuel Sanchez;
students: Mark Muscat, Nicolò Friedman, Pallavi Sharma, Vincenzo D’Aura.
2D pattern produced by simple flocking of agents – 2D Agency Logic

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The concept
The research topic focuses round the idea of building a three dimensional mutating fabric
formulated of matter that changes its density and porosity in response to specific conditions.
The concept is closely related to a continuum of responsive matter which adopts highly
specialized behavior in localized scales.
After analyzing microscope images of human bones (kindly given to us by students at the UCL
Department of Surgery), we got inspired by their high porosity structure, strength and flexibility.
Moreover, we also investigated bone remodeling; a lifelong process where mature bone tissue is
removed from the skeleton and new one is formed. Hence we are aiming at achieving a building
fabric that has a high response to the external demands of the surrounding environment and an
intrinsic ability to adapt locally.
Bones are also relevant in their ability to work together with other tissues (tendons and muscles) to
form a single proficient system. The human body is a powerful machine composed of several
elements which simultaneously demonstrate independent and collaborative behavior. Our fabric
responds to ecological pressures, created by using an interrelated synthesis of local and global
behaviors.
A new kind of tectonic language could be built using deposition techniques and generative methods
of topological formation. We decided to focus our attention on the constructability of the shapes
generated by establishing a design approach of performative fabrics where form, material and
structure are closely related.
Voxel-based computational agency logic supports the search for a form with clear state conditions
using a discrete logic. After defining a possible material organization, the fabric, within its
determined boundaries, is refined to adapt to various environmental pressures around it.
The final goal is to create an architectural fabric using these generative approaches which will
produce a high-resolution output (refer to original case studies) on a human-scale. Responsive
material technologies give computation a new dimension with a rich array of material affordances
and behaviors, which may ultimately be materialized through Additive Manufacturing techniques.

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Selected images
Starting references:
microscope image of human bones44
Bio-material: Collagen45
Deposition tecniques:
Left: Overview of a 3D structure covered with a thin metal layer.
Right: Detail of the structure’s corner46

44
Image kindly provided by Medical School UCL, Division of Surgery (15 October 2012).
45
“Collagen Fibrils - FEI”, http://www.fei.com/resources/image-gallery/knee-joint-capsule-7329.aspx
(accessed 06 March 2012)
46
cropped image of “New 3D metal deposition technique for metamaterials fabrication – DTU fotonik”,
http://www.fysik-nano.fotonik.dtu.dk/Projekter%20F2011/Fagprojektbeskrivelser/(accessed 06 March 2012)

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Reaction-Diffusion:
Reaction Diffusion 47
Reaction-diffusion 2d catalogues.
Different configuration of patterns related to different values of F, K, dU and dV.

47
manipulated
image
of

“
Variable
resolution
Reaction
Diffusion
-‐

flight
404
”,

http://www.flickriver.com/photos/flight404/sets/72157623905785665/ (accessed 06 June 2012)

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3d color-based Reaction-Diffusion model:

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2D Agency Logic:
“ high level of expression through the intricacy of the patterns produced by collective
behavior of agents”

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functionally graded material:
Agents have the ability to create different patterns.The patterns denote by their own composition the
materiallity they are made of. TWISTED and INTRICATE patterns have a natural STABILITY.
FIBROUS patterns have a clear ELASTICITY AND FLEXIBILITY.
Materiality, laser sintered experiments:
A serious of “test” prints were created on the SLS machines at the Bartlett to determine material
limits and printer capabilities. The output fabric is completely dependenton the machine by which it
will be constructed.

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Makerbot replicator:
From a digital word to reality: the power of the machine to reproduce the intricacy of the digital
model.

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3D Agency Logic:

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5// CONCLUSION
“Of course our motivations in computer science are often very different from the motivations of
biologists. Nevertheless, it has long been the goal of evolutionary computationists to evolve
complex solutions to problems without needing to program in most of the solutions first. The dream
of complex technology that can design itself requires rejection of the idea of knowledge-rich
systems where human designers dictate what should and should not possible. In their place we
need systems capable of building up complexity from a set of low-level components. Such systems
need to be able to learn and adapt in order to discover the most effective ways of assembling
components into novel solutions. And this is exactly what development processes in biology do, to
great effect.”48
The aim of the paper is to underline the possibility to look at “Nature” as a source for a new design
approach. The point is to look at these biological processes as “recipes” for generate certain forms
or fabrics with specific characteristics. Although today computational science is able to create quite
accurate models that emulate the growth of the embryo or systems that mimic the pattern formation
of the mantle of some animals, the key point is the ability to use this tools for designing. In fact, the
process of generative design can be divided into two principal steps: while the first part is related to
build up systems with high adaptive capacity using simple initial variables the second one deals
with the ability of the designer to speculate a possible scenario in an architectural territory. This is
the most difficult challenge since the designer should be able to push the system in order to find
aesthetic and functional expression without ending up in a design project that expresses only high
technicality. The research part provides a practical example of how to use these theoretical bases as
the initial ground to build up a coherent design project, without forgetting the imaginative capacity,
essential quality that an architect must possess.
The computational models, if driven in the rigt way, have got the superpower to generate fabrics
with an high adaptability and a self-intelligence in according to the environment. Complex systems
are generated using simple initial rules between the components and this is exactly what happens in
Nature.

48

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6// SELECTED REFERENCES
_Brian Goodwin, How the Leopard Changed its Spots: The Evolution of Complexity, ( a Phoenix
Paperback 1994).
_Heylighen F., Bollen J & Riegler A., The Evolution of Complexity (Kluwer Academic,
Dordrecht, ed. 1999).
_John H. Holland, Emergence from chaos to order, (Oxford University Press 1998).
_Philip Ball, The self-made tapestry, (Oxford University Press, 1999).
_Sanford Kwinter, African Genesis, in Assemblage36, (MIT Press 1998).
_Sanford Kwinter, ‘Soft Systems’, Culture Lab 1, Brian Boigon ed., (Princeton, 1993).
_S. Camazine J. L. Deneubourg N. R. Franks J. Sneyd G. Theraulaz E. Bonabeau, Self-
Organization in Biological System, (Princeton University Press, 2001).
_Bentley, P. J. & Kumar, S. Biologically Inspired Evolutionary Development
_Bentley, P. J. & Kumar, S. (2003), Computational embryology: past, present and future
_Bentley, P. J. & Kumar, S. (1999). Three Ways to Grow Designs: A Comparison of Embryogenies
Conference (GECCO) Orlando, Florida, USA.
_Heylighen F. (2005): "Conceptions of a Global Brain: an historical review", , Technological
Forecasting and Social Change[in press ]
_Heylighen F. (2011) Self-organization of complex, intelligent systems: an action ontology for
transdisciplinary integration, Integral Review (in press)
_Heylighen F., (2011), Self-organization in Communicating Groups: the emergence of
coordination, shared references and collective intelligence, Vrije Universiteit Brussel
_Heylighen F. (2010) The Self-organization of Time and Causality: steps towards understanding the
ultimate origin, Foundations of Science, 15(4), 345-356. (doi:10.1007/s10699-010-9171-1)
_Architectural Design, Versatility and Vicissitude Performance in Morpho-Ecological Design
Guest-edited by Michael Hensel and Achim Menges, March/April 2008.

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“Background and Related Work”, http://www.ventrella.com/Alife/Thesis/background.html,
(accessed 15 April 2012)
“Chicken embryo – Microscopy UK”, http://www.microscopy-
uk.org.uk/mag/artnov04macro/mlchicken.html (accessed 22 April 2012)
“Medical Dictionary”, http://medical-dictionary.thefreedictionary.com, (accessed 2 June 2012)
Front page image_ cropped image from “wildencounters”, http://www.wildencounters.net/weblog/,
(accessed 21 April 2012).
“Lesser Flamingos grouped together on shallow-water mud and silt flats, in the shape of a devil's
tail, aerial shot, Lake Natron, Tanzania (aerial shot)

Nicolò Friedman

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