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The Architecture of Us

The Architecture of Us

Humankind has always looked to mould their surroundings to their liking, the beginnings had us inhabit and shape the natural spaces most accessible, from caves and niches in rocky outcrop to dense forests with sheltered canopies. We have come a long way since and have a long way to go. In our journey we have used the materials lying around us to build the habitats we now reside in, tailored to our liking. We’ve even discovered new materials and manipulated them to be more robust, look good and even sustain generations of human beings.

Human morphology has gone beyond our biological borders and translated into the environment that surrounds us. We have changed, over the generations of our existence and along with us our habitats, we’ve managed to build complex systems and infrastructure that harbour life as we know it. Where does the inherent need to craft our living spaces come from? It may well be from within us, on both a psychological and biological level.

In my thesis, I set out to examine if and how Epigenetics, a science of how the environment affects our genes, when pegged onto the mathematical models of cellular automata could be used as a heuristic and abstract model to uncover how our genes play a role in the morphogenesis of our environment and how that environment in turn shapes us. In parallel I attempt to begin a discourse on the thesis through my design project at the Interactive Architecture Lab, by creating iterations of an experiential installation that strives to embody the complex system that is epigenetics through Conway’s Game of Life, a two-dimensional Cellular Automaton. An investigation into how we may eventually apprehend how genetic functions on a biological level can potentially affect our habitable environment.

This paper does not claim to unearth the complex biological or mathematical frameworks that run the epigenetic engines of human and environmental morphology but rather strives to shine some light on the implications of what might occur if we eventually do understand how all this works. Better insight into epigenetics could potentially cause human physiological and morphological evolution to leap forward and even solve many of the environmental and biological constraints that currently plague our Anthropocene era.

Morphogenesis and Epigenetics

Morphogenesis: more than biology?

As I unravel the topics set forth in my introduction, I wish to explicate that the focus throughout this paper is with the idea of Morphogenesis, defined as the biological process in which an organism develops its shape. Whilst I first tunnel into epigenetics and cellular automata they will eventually merge at the nexus of morphogenesis.

Having established that premise, we see morphogenesis first studied as mathematical models of how physical processes and constraints affect biological development, some of the pioneering scientific investigations were: how natural patterns form, like the spirals in phyllotaxis on plants (Thompson, 1917) and how chemical signals of activation and deactivation set up patterns of physical development. (Turing, 1952)

Image 1: Spiral Phyllotaxis in the Aloe Vera Plant

Thompson undertook a mathematical exploration of the development of biological shape, Turing’s predictions of morphogenesis were rooted in molecular biology and biochemistry even before these fields were fully developed and much before the discovery of the DNA. The common factor between both studies is that these processes affect physical changes that occur on a microscopic cellular level, a complex system of operations that can have implications on physical development of a micro and macro scale.

Epigenetics in a nutshell

I wish to avoid putting readers through a crash course of genetic processes in the assumption that they are aware of the basics. I would rather unravel my understanding and research of Epigenetics through outlining some of its basic principles and examples, “It will come as a surprise to many that our external environment affects us through our genes, by modulating their activity. Our environment does not affect our genes directly. Rather environmental influences on our genes are mediated by changes in the cells in which our genes reside. Different kinds of cells respond differently to the same environmental factor, whether it is social stress or food deprivation in the womb. As such, and despite the fact that all of the cells in our body have the same genes, any environmental effect in you is cell type specific.” (Francis, 2011)

In his book ‘Epigenetics how environment shapes our genes’ Richard C. Francis undertakes the enormous task of simplifying this vast field for the general reader through real life narratives and historical facts. Thoroughly understanding epigenetics is an ongoing pursuit of the scientific research community and has yielded many insights into human genetic evolution, however, as is with most evolutionary sciences, time plays a major role as evidence of epigenetic effects can only be inferred over several generations.

The key component in the epigenetic process is the methylation of DNA. It involves the addition of methyl groups to the DNA molecule; methylation occurs from factors external to the DNA and can alter the activity of DNA but does not affect its sequence. Methylation is akin to a switch that turns on or off, say if a gene is methylated its expression is turned off and if de-methylated the gene’s expression is turned on. In that regard, Methylation plays a major role in genetic transcription which on a larger scale affects heritable changes in physical and mental development passed on over generations.

The best example to understand the transgenerational epigenetic process would be its effects on a developing mammalian embryo as illustrated in Figure 2. After the egg is fertilized and forms a zygote, it is scrubbed of any methylation from the sperm or the egg. Fresh methylation occurs in nascent embryonic stages but gets scrubbed on further physical development into later stages. When the born embryo can produce its own sperm or egg, it undergoes fresh methylation from its environment and the cycle of life continues. The methylation process highlights the other unique property of epigenetic effects; they are reversible.

Figure 1: Without a Trace, Epigenetic scrubbing & reprogramming in sperm cell & embryonic development.

Understanding how epigenetic effects pass on through generations are looked into through the example of a natural experiment that was the Dutch Famine (Francis, 2011) which was onset at the end of World War Two, When the retreating Nazi forces disrupted food supplies to cities in western Holland in November 1944, daily diet was reduced to 1000 calories a day and further dropped down to 580 calories in 1945. An investigative & ongoing study1 of malnutrition via the Dutch Famine Birth Cohort Study found that babies born during the famine weighed considerably less than those born prior to the famine. Studies go to further show long term implications in the physical growth of those under the study group looking into how effects during the second & third trimesters of neonatal development eventually affected their physical development in adolescence and teenage years.

Epigenetic factors can turn genetic expressions on or off and most of these factors are triggered by the environment, however changes from the environment can have varied changes on different parts of the body “Your liver cells will react one way to poor nutrition, your neurons will react in a different way, and many cell types wont react at all. Therefore, in determining any environmental influence on gene action, scientists look at specific cell populations, such as neurons in a particular part of the brain, liver cells, pancreatic cells, and such.” (Francis, 2011).

Years of research and studies now confirm the effects of epigenetics on human physiology, but could it be that these effects can extend out further than just our body? While we discovered that the environment affects our biological process could we, through our actions affect the larger environment?

An Extension of Us

A case for the phenotype

In their work on evolutionary psychology John Tooby and Leda Cosmides extrapolate how human psychological adaptation require many genes to regulate development (1990)

Enter: the ‘phenotype’, the manifestation of a gene’s interaction with its environment. Our morphological development is a phenotype of our genetic regulatory functions and various environmental factors.

Figure 2: Genotype + Environment + Gene-Environment Interactions = Phenotype

“Developmental programs (i.e., the regulatory processes that control development) are directed by the genes, but they require and depend upon an entire range of properties of the environment being reliably and stably present in order to successfully produce a healthy individual If either the genes or the environment are sufficiently changed, the result will change Thus, as with all interactions, the product cannot be analysed into separate genetically determined, as opposed to environmentally determined, components.” (Tooby, Cosmides, 1990)

The above suggests that a deep symbiotic system exists between the gene and the environment. While the authors speak about gene expression on a psychological level we can draw parallels to expression on a physical level, based on this gene-environment interaction, our interference in choosing to form and manipulate our external environment will impact the way the environment chooses to talk back to our physiology.

While human morphology has steadily evolved so have the shapes and forms that we’ve built. All the while maintaining a constant feedback loop between the two. Healthy environments promote healthy human development and vice versa. ‘Evolution acts through genes, but it acts on the relationship between the genes and the environment. The “environment” is as much a part of the process of evolutionary inheritance as are the “genes,” and equally as “biological” and evolved.’ (Tooby & Cosmides, 1990)

We bring the focus back to epigenetics and how in better understanding the systems that power these processes could we possibly unlock the algorithms to harness control over both our biological and environmental morphology.

Francis highlights how most epigenetic changes occur outside of the gene itself, most of these changes being of a biochemical nature. Besides having a wide reach within our biology, epigenetic factors play a universal role in the biochemical scheme. “In fact, epigenetic attachments can affect genes quite distant from the point of attachment. So, it is best to think of epigenetic processes as modifications of DNA, not just individual genes.” (Francis, 2011)

“We can think of epigenetics as a new way of looking at DNA that goes beyond the base sequence. The linear base sequence comprises one, albeit the primary dimension of the physical gene, but DNA is a three-dimensional molecule. Epigenetics is a science that extends the study of genes from one to three dimensions. These extra dimensions are particularly important for understanding gene regulation, which is where the epigenetic action is.” (Francis, 2011)

By that logic our genetic expression extends out beyond our organic frame and into the environment that surrounds us characterizing itself in many forms through various phenotypes, one of those phenotypes is architecture and the built environment.

Architecture as a Phenotype

Claiming that the built environment is a phenotype and has anything to do with our biological processes might come as a surprise to many even today. If we can agree that epigenetic effects have an extended reach within our bodies through long causal connections, it should be no huge revelation that these effects might extend out into the environment.

The Extended Phenotype by Richard Dawkins is a thorough inquiry into the extended reach of the gene. He highlights Mike Hansell’s observations on animal artefacts as case studies for ethological principles (Hansell, 1984) and uses artefacts in the animal kingdom to better explain the extension of the phenotype.

Human behaviour has a lot to do in the relationship between gene and environment. Behaviour can be deterministic to inputs received from the environment and the corresponding response to these inputs as well. The artefacts we choose to use and create our built environment are now part of the evolutionary ladder that we are climbing. “Thus, the evolutionary process can be said to store information necessary for development in both the environment and the genes, in that it shapes the relationship of the two so that both are necessary participants in the ontogenetic construction of adaptations. Both are “biologically determined,” if such a phrase has any meaning” (Tooby & Cosmides, 1990)

Image 2: Brachycentrus Caddis Larvae in their chimney shaped homes

Dawkins takes a scrutinizing look into animal artefacts as phenotypic extensions, one example being that of the Caddis fly larvae (Dawkins 1982), of how they choose coloured stones from stream beds to make their shelters before their metamorphism into pupae. Many larvae, like the brachycentrus genus have been observed to choose black coloured stones and build chimney-shaped homes.

The genes that set apart different home types of the larvae could be through the behaviour mechanism of their eyes (Dawkins, 1982). Larvae shelters are functional in that it helps them move in and out while searching for sustenance without the risk of being swept away by the current. We can insist on there being many behavioural and environmental factors that determine how and where caddis larvae make their homes which could be several different genes working together to propagate that behaviour.

“Once we have accepted that there are genes for building behaviour, the rules of existing terminology imply that the artefact itself should be treated as part of the phenotypic expression of genes in the animal. The stones are outside of the body of the organism, yet logically such a gene is a gene ‘for’ house colour” (Dawkins, 1982)

The extension of phenotype is not only restricted to individual organisms but can even be collective. Dawkins questions the evidence of artefacts like termite mounds and bee swarming and how we could push the conceptual range of a gene’s phenotype to a wider purview. He outlines other examples of artefacts identified as phenotypes like the spider’s web and the beaver’s dam and how both these artefacts act as extensions of the animal that aid in their existence. (Dawkins, 1982).

Following Dawkins’ argument of artefacts as extensions of life in the animal kingdom we could liken architecture as a phenotype of the human gene hood. Our world is witness to a vast expanse of human phenotypic effects on the environment, we take parts of our environment, temper it for optimization and construct functional and beautiful artefacts that mitigate the way we interact with our spaces and with each other.

While most architecture is a testament to our genetic evolution, I’d like to highlight a few instances of art, cybernetics and architecture that all have the underpinnings of man’s phenotypic grasp on the built environment.

Merzbau was a spatial intervention by 20th century Dada artist Kurt Schwitters. His original work, which was his very own home and studio was lost in allied bomb raids in Hannover, but photographs and accounts describe it as an “abstract walk-in collage composed of grottoes and columns and found objects, ever-shifting and ever-expanding. It was more than just a studio, it was itself a work of art.” (Thomas, 2012)

Image 3: The Hannover Merzbau by Kurt Schwitters.

Schwitters’ abstract take on how we constantly modify and change our surroundings has similarities to the morphogenesis of artefacts in nature by other organisims. Human innovation and foresight has always been a pioneering force in visualising and building architectural wonders, researchers in the field are currently working on enabling fabrication of structures that employ the properties of natural biological development through biomimetics and even using genetic evolutionary algorithims to discover novel ways of not just building but ‘growing’ our spaces.

There exists a plethora of contemporary research work about architectural development through interaction with its inhabitants. It is only merited that I mention some of the earliest instances of proposals of spaces that could be real-time phenotype extensions. Price’s Fun Palace and Mauricio’s Lyrical Theatre are two early design schemes that feature a spatial design that could morph based on the needs and usage of those that occupied the spaces.

Cybernetician Cedric Price’s fun palace is a widely referenced and published example of a space that could be moulded to accommodate the changing usage requirements of people. More than just a functional space it was more of a socially interactive machine (Mathews, 2005).

Working primarily with avant-garde theatre producer Joan Littlewood and Gordon Pask, Price envisioned the fun palace to challenge the very notions of architecture. Far from being a regular building, it was more of a scaffold or framework (Mathews, 2005) with pivoting stairs, escalators, modules on cranes and movable walls. “It was not a museum, nor a school, theatre or a funfair, and yet it could be all of these things simultaneously or at different times. The Fun Palace is an environment continually interacting and responding to people.” (Mathews, 2005)

Figure 3: A section of Price’s Fun Palace

The schematics echoed Le Corbusier’s assertions of architecture that was informed by technology and a ‘machine for living’ (Mathews, 2005). The draft of the Fun Palace pamphlet spoke of the coming of age of automation and how machines would begin to work for us. We extend our phenotypes into machines that further extend themselves into the environment they create for us.

Another attempt at visualising an architectural space that drew inspiration from the Fun palace was second prize winning entry by Maurizio Sacripanti’s for the Lyrical Theatre in Cagliari.

Image 4: Photograph of competition model for the Cagliari theatre

Maurizio drew up the schematics for a theatre that is an integrated system of movable platforms and fake roof elements, allowing for almost infinite possibilities of stage configurations, seating layout and acoustic performance of the scenic space. (Lucarelli, 2012)
Price and Maurizio’s are a few among many a proposal that foresee an eventual architectural future for humanity where our spaces would be in a state of constant flux, forming and reforming itself by interpreting our usage and behaviour patterns.

In that regard, “one could get used to the idea that a behavioural pattern which is a phenotypic entity gets influenced by a combination of genes whose effects interact in complex and additive ways” (Dawkins, 1982)

The Complex Systems of Life

While our predecessors began with chipping and hacking away at their environment, we have now reached a point where we build systems that build for us both virtually and physically.

Stemming from the logic that we extend our phenotype into the environment wouldn’t that warrant the theory that the machines and systems we build to aid us are also phenotypic artefacts that extend from our genotype? Much like the chewed wood logs used by beavers and stones chosen by the caddis larvae.

Which brings us to the point where we investigate the operational characteristics of the hidden networks and mechanisms that run processes like Morphogenesis and Epigenetics. “Viewed from a biological perspective, organisms are complexly designed systems in fact, there is no non-living system, natural or artificial, that rivals the complexity of organic design” (Dawkins, 1986)

Scientists have long since been applying mathematical models to compute and break down these processes. We must look back into history to see how it began with Alan Turing and John Von Neumann and their efforts in creating universal computation engines, ‘a single machine which can be used to compute any computable sequence’ (Neumann, 1966)

Both Turing and Neumann’s work set the stage for the advent of modern-day computers, after their machines were declassified from the war effort. Some of their work would later pave the way for many researchers looking into their mathematical models to better understand the workings of complex biological processes.

Figure 4: Representation of a basic Turing machine with its various components

Neumann particularly had a keen interest in biology, which led to his proposition of the Universal Constructor (UC). “The concept of a UC is easy to understand. Imagine a machine that can be programmed to build objects by selecting components from a pool of materials and assembling them into a functional product.” (Davies, 2019)
One of the principles of a universal constructor was that it should have the capability to build anything, even itself. We know that living organisms are self-reproducing machines, the possibility of being able to comprehend the same process through a mathematical model was surely a breakthrough. But as we know it to this day, the intricate functioning of living organisms is way too complex to just lay out with a simple set of algorithms.

‘Moreover, biological complexity is not a random collection of unconnected properties, but rather an intricate and articulated set of interdependently organized parts that function together in an adaptive mesh to promote fitness’ (Tooby & Cosmides, 1990)

We earlier established that human behaviour has genetic implications on our own biology and the environment, one pattern of human behaviour through generations has been the ability of adaptation. Through changing times, humanity has adapted to changing geomorphic situations, climates and unstable environments.

Through many causal connections the human behaviour of adaptation can also be called an extended phenotype. “Adaptations are mechanisms or systems of properties “designed” by natural selection to solve the specific biological problems posed by the physical, ecological, and social environments encountered by the ancestors of a species during its evolution” (Tooby & Cosmides,1990)

It is this very same adaptive phenotype that plays a major role in organic Morphogenesis.

“Obviously, there is a natural tension between complex functional interdependence in a system and the existence of a large amount of variability in its components. For living systems, design is controlled by the genetic programs that regulate development.” (Tooby & Cosmides, 1990)

Mathematicians and biologists have been constantly building abstract models that mimic living system and even models that could predict how life itself and the universe works. Mathematician Stephen Wolfram states that the whole point of any of these models is to provide an abstract representation of effects that contribute to determining the behaviour of a system. Below the level of these effects there is no compulsion for the model to operate like the system itself. (2002)

With this reasoning we could potentially devise a model to understand the mechanism of epigenetics and how it affects morphogenesis. Humanity is one among many species in the animal kingdom that can make copies of itself. While von Neumann did not build an actual self-reproducing machine, he did come up with a clever mathematical model that captures the essentials (Davies, 2019)

The model, called Cellular Automaton (CA) is world renowned and has acquired viral status in fields of scientific research as well as games, 3D visualisation and even pop culture.

Morphogenesis through Cellular Automata

The basic structure of the Cellular Automata model is defined as a regular grid of cells, each cell has a limited number of states, a live state and a dead state, rather ‘on’ or ‘off’. Each cell is surrounded by a neighbourhood of cells whose states define their adjacent cells.

Starting from an initial state the automaton progress through generations of reproduction and destruction based on set rules of how a cell may interact with the cells in its neighbourhood. Since its discovery, there have been many rules formulated and CAs have moved through its manifestation from one, two and up to three Dimensions.

In Wolfram’s A New kind of Science, he takes an exhaustive look at one-dimensional cellular automaton. Through his explorations he unearths how simple rule sets, when applied to a one-dimensional grid can create patterns of high complexity.

A popular example of a one-dimensional Cellular automaton is Wolfram Rule 30 which shows highly complex pattern formation as it progresses through generations of cell life and death.

Figure 5: Wolfram rule 30 after 250 iterations

Besides being popular for its complex pattern that have been used by many artists, Rule 30 sticks out because of its proven occurrence in nature. Striking resemblance to Wolfram’s rule was observed in a type of Mollusc shell.

The ramifications of a mathematical CA model having actual occurrence in nature are that the physicality of biological systems originate from simple rules or functions on a cellular level and evolve into complex morphologies.

Image 5: Conus Textile mollusc shell portraying the Rule 30 pattern

Discoveries of mathematical models occurring in nature like Wolfram’s rule 30 lets us take CA research further and apply it as abstract models to better understand processes like epigenetics and morphogenesis.

The Game of Life

In 1970 was the birth of perhaps the most celebrated Cellular Automata, Mathematician John Horton Conway’s Game of Life.  “There are no direct biological implications in the algorithm of the Game, but it does capture something deep about the logic of life.” (Davies, 2019)

A two-dimensional CA, it works only on an initial rule set and evolves from there. This initial set of rules can work as modifiers which then show varied outcomes of cells reproducing and dying. Also set on a finite grid, one could consider a grid of squares, where a square is said to be alive if it is filled and dead if not. Every square has eight other squares in its neighbourhood and like any CA the given square can change its state, based on the state of the other eight neighbours.

These states are determined by the following rules set by Conway:

Rule 1: Any live cell with fewer than two live neighbours dies, as if by underpopulation.

Rule 2: Any live cell with two or three live neighbours lives on to the next generation.

Rule 3: Any live cell with more than three live neighbours dies, as if by overpopulation.

Rule 4: Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.

These rules are applied simultaneously and repeatedly to every square with each step of application considered as one generation. The outcome of these rules are shapes and patterns that seem to take life and move across the grid colliding with each other, killing off cells and bringing new ones to life.

The effects and the visual appeal of the Game of Life does set it apart from other CAs and has even created a cult following, “however the real curiosity about the game is less for art and amusement and more as a tool for studying complexity and information flow among the shapes.” (Davies, 2019)

We bring the conversation back again to Epigenetics and Morphogenesis, I do not claim that the rules of the Game of Life can simply be latched onto epigenetic principles, the logic of the Game of Life does however share many similarities with epigenetics, the state of a cell being on or off can be compared to DNA methylation being on or off and a cell’s neighbourhood can be likened to the environment of an organism. The rules of engagement are like behaviour and adaptation as extended phenotypes that cause changes in patterns and shapes which brings in the resemblance to the concept of morphogenesis.

The validity in using the Game of Life as an abstract model for study lies in its implication of causality and effect. The shapes we see are more than just physical objects, they are manifestations of informational patterns.

Figure 6: Gosper’s glider guns, one of the outcomes of the Game of Life rule set

Although the system seems to be in a state of constant chaos, there is room for emergent order. The formation of shapes or in our case, morphogenesis can be devised with higher level ‘rules of engagement’. “Something like this must be going on in life (and consciousness), where the causal narrative can be applied to informational patterns independently of the physical substrate.” (Davies, 2019)

Manifesting a Model

My design thesis at the Interactive Architecture Lab is an attempt to create an abstract representation of the processes of Epigenetics and Morphogenesis using Conway’s Game of Life through iterations of an experimental spatial installation.

There are many instances of the manifestation of Cellular Automata as built projects, I would like to highlight a few here:

Pentagonal Portal by Jason Bruges Studio

Image 6: View of the Pentagonal Portal at XTX Markets, Kings Cross

Built as a permanent art installation for XTX Markets, JBS uses a modified set of rules of the Game of Life on a panel of LEDs in a corridor. There is no interaction between users of the corridor and the patterns of CA that appear on the panel, the studio states that the corridor is design to ‘challenge the minds of the people who can understand it, and visually captivate the minds of those who don’t.’

Edge of Chaos by The Interactive Architecture Lab & Studio Overvelde

A collaboration between Ruairi Glynn, Vasilija Abramovic and Bas Overvelde, this well-toured installation is also inspired by Conway’s Game of Life and is an interactive spatial artwork that comprise of active and passive kinetic geometries that are triggered by motion sensors. “The narrative installation makes tangible the science of complexity” and through the installation’s changing behaviour of light, sound and robotic motion it encourages a “playful exploration and contemplation on bigger questions of how life could emerge from the disorder of the universe” (Glynn and Abramovic, 2018)

Image 7: Edge of Chaos installed at La Gaite Lyrique

With a rich array of projects using the Conway’s automaton, my design project investigates the use of the Game of Life with light as a medium to communicate the idea of morphogenesis that can occur through an epigenetic process.

This calls for establishing some precedent projects that use lighting to create three-dimensional spatial experiences:

Struttura 895, Omaggio a Fadat by Enzo Mari

Image 8: Mari with his piece at the Galleria dell’Ariete in Milan, 1967

Enzo Mari designed a machine for producing volumes through light with 64 lamps on plexiglass with 64 switches to turn the lights on or off which was showcased at Milan’s Light and Movement exhibition in 1967. The piece takes light as individual cells and creates an impression of volume by the way of arrangement in a grid-like system. User input on the light switches renders the piece with a basic level of interactivity.

Swarm Study by Random International

Interactive design studio Random International built a number of light installations through their ‘swarm studies’ which simulate patterns of group behaviour or ‘swarming’ in nature and embody them in light.

Images 9: Swarm Study VII, Site specific Installation, 2015

Swarm behaviours have resemblances to the behaviour of cellular automata. Enzo Mari and Random International’s use of light to create morphological spatial volumes and gives inspiration to my design project which I will illustrate further on.

Computer vision through a microscope

The need for detecting interaction between users and their environment are today supported by many electronic sensors, from motion sensors that open doors and smoke detectors that set of alarms in case of a fire, we are living in an augmented environment.

Another popular technology used in sensing is the camera.  Computers can now break down and analyse a range of information from cameras. If the eye is an extension of our brain that we rely on for information processing, so is the camera to the computer.

As we discussed earlier, if the artefacts we create are considered an extended phenotype of us, so too can the computer and camera be considered as extensions of us. ‘Computer vision’ is a field of artificial intelligence that trains computers to interpret and understand the visual world.

This technology gives machines the ability to interpret our spaces and even virtually transform it based on behavioural cues from the environment and its users. Artists and researchers have been using computer vision to teach our machines to see and understand spaces and objects.

Machine Learning methodologies like ‘Generative Adversarial Networks’ (GANs) are now pushing the boundaries of how computers learn to see. Artist Memo Akten explores the analytical capability of Neural Networks in his installation ‘Learning to See’ that uses feed from a live camera looking at objects on the table. Users can manipulate the objects and watch as the computer visualises a virtually morphed image based on an inputted database.

Image 10: Akten’s Learning to See at The Barbican AI: More than Human exhibition, 2019.

The potential of our machines being able to comprehend spaces and objects nudged me towards the use of computer vision as the primary sensing technology for my design project.

Human behaviour as interaction

In his book, The Demon in the Machine, Paul Davies illustrates how his colleagues, Alyssa Adams and Sara Walker attempt to make Cellular automata a more realistic representation of biology by including a changing environment (2019). They combined two CAs, one representing the organism and one representing the environment.

Adding human movement and behaviour as affecting agents into the CA system can have interesting and unperceived outcomes that affect morphology.

Figure 7: Three-dimensional cellular automata mapped onto a human body

Using computer vision, Cellular automata and LEDs, Monozygotic, a collaborative design project explores the visual outputs of human movement and behaviour within an environment.


The design project named ‘Monozygotic’ at its current stage has two iterations, the first iteration was visualising Conway’s Game of Life on three LED panels dispersed in a black box while a grid eye sensor communicated with a moving spotlight that tracked people as they moved through the environment.

Imagee 11: Monozygotic at the IAL Project Faire, 2019

The second iteration saw better integration of computer vision with techniques of background subtraction and frame differencing used to track human motion through the Life Rewired Hub at the Barbican.

Image 12: Monozygotic at the Barbican, IAL Prototypes in Public, 2019

Much like Adam and Walker’s exploration of coupling two different CAs, Monozygotic at the Barbican had the computer recognising a human as an Octagonal ‘Oscillator’, a common shape in Conway’s CA that is in a constant shift between states. Human motion is tracked by virtual grids overlaid over six LED panels that trigger oscillator automaton affecting a continuously running Game of Life using a basic rule set.

Image 13: Screen Capture showing Monozygotic at work with Computer Vision tracking Human Movement and affecting Game of Life CA

Next Steps and Conclusion

Both iterations of Monozygotic used two-dimensional representations of Conway’s Game of Life, the final iteration will see these methods taken into a three-dimensional environment, keeping light as the representational medium. This could give us better understanding of morphological effects of human interaction with the environment with the basis of Cellular Automata.

This paper and the design project are the execution of an abstract model for learning and observation of how cellular automaton could be used to study changing shapes and space based on the behaviours and interactions in those spaces.

Studies like this could lead to better understanding of processes like Epigenetics and its role in morphogenesis, through a more in-depth and empirical use of data. It could fuel the advancement or even help us diagnose problems of our ever-changing environment and help us move into a future of smart and adaptive infrastructure.

Image 14: Deployable self-organising drone in action

The age of deployable self-organising systems is upon us, many factories house robots on assembly lines, with research work like the Cyber Physical Macro material at ICD Stuttgart we are closer than ever to a self-organising and changing environment, much like Cedric Price’s dream of the Fun Palace.


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Images and Figures

Image.1: Nelson, S. Photo via <a href=””>Good Free Photos</a> [Accessed September 2019]

Image 2: Fauceglia, T., 2017. Art Case Study: Symbiosis. Noisily. [Accessed September 2019]

Image 3: Redemann, W., 1933 The Hannover Merzbau. In Search of Lost Art: Kurt Schwitter’s Merzbau. [Accessed September 2019]

Image 4: Sacripanti, M., 1965. Lyrical Theatre in Cagliari. [Accessed: September 2019]

Image 5: Ling, R., 2005. A Textile cone snail (Conus textile) [Accessed September 2019]

Image 6: Jason Bruges Studio, 2018. Pentagonal Portal. [Accessed September 2019]

Image 7: Interactive Architecture Lab, UCL, 2018. Edge of Chaos. http://two.wordpress.test/lab-projects/edge-of-chaos#_ftnref1 [Accessed September 2019]

Image 8: Mari, E., 1967. Struttura 895, Omaggio a Fadat. [Accessed September 2019]

Image 9: Random International, 2015. Swarm Study/ VII. [Accessed September 2019]

Image 10: Akten, M., 2019. Learning to See. Barbican “AI: More than Human”[Accessed September 2019]

Images 11: Jacob, R. & Ta, Q.P., 2019. Monozygotic I. Interactive Architecture Lab Project faire

Image 12: Lucy, J., 2019. Monozygotic II. Interactive Architecture Lab Prototypes in Public. Life Rewired Hub, Barbican.

Image 13: Jacob, R & Ta, Q.P., 2019. Monozygotic II, Interaction process screenshot. Interactive Architecture Lab Prototypes in Public. Life Rewired Hub, Barbican.

Image 14: Aflalo, M., Chen, J., Tahanzadeh, B., 2018. ICD/ITKE. Cyber Physical Macro Material. [Accessed September 2019]


Figure 1: Hughes, V., 2014. Epigenetics: The sins of the father. Without a Trace. [Accessed September 2019]

Figure 2: Jacob, R., 2019. Illustration of Genotype and Environment interaction to produce phenotype.

Figure 3: Price, C., 1964. Cedric Price Archives, Fun Palace, Section Canadian Centre for Architecture, Montreal.

Figure 4: Salvado, C.C., 2008, Sketch of a Basic Turing Machine. [Accessed: September 2019]

Figure 5: Wolfram, S. (2002). Rule 30 .A New Kind of Science, [Accessed: September 2019]

Figure 6: Burgers, B., 2008. Diagram from the Game of Life [Accessed: September 2019]

Figure 7: Jacob, R., 2019. Illustration of 3D Octogonal oscillator mapped onto human figure.

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