Unlike classic computers that are based on silicon chips and binary computing operations, biocomputing uses molecules, cells, or even entire tissue materials to perform “computing operations.” Initial research results show that biological systems not only offer enormous storage density and energy efficiency but can also solve problems that are highly complex for classic computers a lot faster.

Biocomputing extends from DNA computers to neural mini brains to bio-inspired software algorithms. It’s a field at the interface between biology, chemistry, computer science, engineering, and physics, and the boundaries between “living” and “technical” can easily become blurred. The objective is to make the principles of nature usable for IT processes, whether through DNA, proteins, or neural networks, and a range of attractive technologies can be derived from those efforts.

“tomorrow” presents four forward-looking fields here: cellular computing, wetware computing, hydrogels, and advanced quantum biocomputing.

Among other things, the cellular computing approach pursues the idea that computing power can no longer be generated only in silicon chips but in living cells as well. For instance, researchers at Stanford University have managed to develop a “biological transistor” they call “transcriptor.” This component consists of DNA/RNA and proteins and operates in living cells like a classic transistor does in electronics. Another example has originated at Arizona State University where scientists have created RNA circuits that can perform calculations such as divisions or root extractions within living cells, practically using cells as “living computers.”

In that way, logical operations such as AND/OR gates can be performed to program biological systems for data processing. That shows how the “computing power from living cells” concept is tangibly taking shape. Synthetic biology, an interdisciplinary research field that blurs the boundaries between computer science, genetic engineering, and engineering provides the basis for that.

Pathways toward animate technology

To enable biocomputers to use biochemical signals to perform logical tasks, researchers install artificial genetic “circuits” into the genotype of bacteria that work in similar ways as hundreds of millions of tiny logic gates that in today’s standard processors make “zero”-“one” decisions. A certain molecule can activate switch “one” and another one switch “zero” which, in combination, may result in the creation of complex logical processes.

Consequently, the outcome is a system that not only processes information but can multiply and repair itself. “Animate computers” keep growing with the process when being fed with data and, above all, need clearly less electric power. This capability makes them one of the most fascinating alternatives to classic computing that even massively saves energy.

Cellular computing in the field

While cellular computing so far has typically been associated with biomedical approaches its real potential begins where industry, manufacturing, and biotechnology meet. The reason is that bacteria and other microorganisms that can be programmed as biological computers open up an all-new dimension of automation, process control, and sustainability. Especially in the field of material research, cellular computing creates completely new prospects.

Researchers are working on so-called “smart materials” that have integrated biological computing units or embedded biocomputers. These materials would be able to capture and process ambient data and, based on that, make decisions. They could, for instance, detect corrosion processes, internally “calculate” the information and then systematically release protective molecules. “Self-healing” materials are conceivable as well. Microbial cells act as biological processors in them, analyze structural damage, and independently initiate repair processes. Such biohybrid systems could both dramatically reduce maintenance cycles and considerably extend the life of industrial components.

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Vision of a bio-intelligent industry

Cellular computing can also be used in industrial plants where parts of the control systems, quality inspections, and product development are no longer performed by machines but by microbiological networks. Researchers are working on concepts for “microbial networks” in which various cell types communicate with each other to solve complex problems in a job-sharing manner.

Moreover, that idea, inspired by neural networks, leads to “biological cloud computing,” in which each cell acts as a node. In that way, cellular computing could provide the basis for an all-new factory floor. In addition, this form of bio-intelligent manufacturing could not only reduce material and energy consumption but finally dissolve the boundaries between biology and technology as well.

Another approach is called neuromorphic biocomputing aka wetware computing. While in cellular computing living cells themselves always assume the role of a computer, in wetware computing, the way in which the brain works is imitated by means of biological or hybrid neural circuits. These circuits, for instance, are grown in artificial nutrient solutions and linked with computers via delicate microelectrode arrays, which means that the technology connects living neural networks or biochemically replicated synapses with electronic systems to create machines that learn, constantly adapt themselves, and remain stable even under unpredictable conditions.

With wetware computing, learning is not based on digital algorithms but on biological processes within living nerve cells. The neurons create synaptic connections that become stronger or weaker, depending on the activity. This principle is known as “synaptic plasticity.” When a certain signal is repeated the system “trains” by stabilizing the neural connections. Similar to the processes in the human brain, that leads to the creation of phenomena such as pattern recognition, memory, and decision-making ability. Wetware computing systematically makes use of this learning capacity by utilizing stimuli or feedback loops to condition the biological network for specific tasks, meaning that the system does not develop from that any programmed responses but “learned” ones within an organic, self-organizing learning process.

The outcome is a so-called hybrid combining biological plasticity with electronic precision and achieving a level of performance and energy efficiency that silicon can never match. To explain: the human brain, for instance, requires around 20 watts of energy to make billions of neurons work in parallel, in other words less than an ordinary incandescent light bulb. A supercomputer performing roughly similar tasks devours several megawatts. This mammoth energy efficiency makes wetware computing so attractive for many uses. Added to that is the great learning ability of biological systems: neurons amplify or weaken their connections in real time. While AI algorithms only simulate these processes, learning in biological networks takes place directly, organically, and continuously.

Cells become circuits

International bekannt wurde das Projekt DishBrain der Monash University in The DishBrain project at Monash University in Melbourne received international attention. The scientists there managed to train a culture of human and animal neurons so that it was able to control the Pong computer game. To do so, cells were provided with visual stimuli and gave back signals. They refined their “gaming technique” with each round, proving that neurons can learn and interact even outside the body. By now, these networks are entrusted with more complex tasks such as pattern recognition or decision making.

However, neurons function not only in processors but also as memory cells. They encode information through changes in synapses. Researchers are working on using these mechanisms in technical applications aimed at creating biological memories with high density, low energy consumption, and self-healing structures. Such “wet memories” could complement classic RAM or flash technologies (i.e., non-volatile memories without power supply) and, above all, provide the bases for all-new architectures.

Use cases for wetware computing

In the automotive sector, bio-based computing units could enable completely new dimensions of sensor technology because wetware modules are extremely energy-efficient and adaptable. Conceivable, for instance, is the in-vehicle integration of biological sensors that on a molecular level detect material fatigue, harmful chemical substances, or microbiological contamination – long before conventional systems would react. These biological “co-processors” could even forward real-time information to central control units, triggering preventive actions such as switching to a safety model or activating nano-coatings to repair micro-cracks.

Wetware systems could also ring in a new era of autonomous driving. While today’s vehicles are based on pre-defined algorithms and massive data processing systems, wetware-based control units could make decisions tending to resemble human intuition. A neural bio-hybrid system in vehicle control would be able to independently recognize patterns from complex surroundings, flexibly interpret unknown situations, while concurrently reacting in energy-efficient ways.

Another field opens up in battery and energy management. Using biochemical control loops, wetware systems could dynamically optimize energy flows – resembling the ways in which organisms regulate their metabolisms. That would make adaptive battery systems conceivable that adjust to varying environmental conditions or independently regenerate cell assemblies. Consequently, a combination like that consisting of synthetic biology and electric mobility paves the way for longer-lasting, sustainable energy storage systems.

Wetware causes all-new process control concepts to emerge in industrial manufacturing as well. Biological computing elements could be used as smart sensors in production chains to detect quality non-conformances on molecular levels. Where classic computers process only digital signals wetware works directly with chemical information and, as a result, can interpret ambient parameters such as pH values, moisture, and “harmful substance concentrations” in real time.

“Whereas traditional computers only process digital signals, wetware works directly with chemical information and can therefore interpret environmental parameters such as pH values, humidity, or pollutant concentrations in real time."

Otto Geißler, Technical journalist for IT

Especially exciting is the prospect of hybrid systems fusing biological and electronic components in Industry 5.0 where machines could use learning-capable wetware modules to adjust to changing manufacturing conditions, like living organisms. A biochemical control loop in the control system, for instance, could detect when raw material qualities change and automatically adjust the process path without a programming requirement.

Additionally, in safety-critical areas such as aviation or autonomous driving, wetware computing promises to become a new form of fault-tolerant intelligence. Biological systems are resistant against partial failures because they’re based on distributed, redundant structures. Consequently, a wetware co-processor could continue to operate even in the event of partial system damage – a crucial advantage over conventional chips that fail in case of minor trouble.

The technology is still in its infancy but the trend is clear: biology is becoming the new hardware. For automotive OEMs, chemical and manufacturing industries, that means no less than the transition into a new era in which technology and life fuse. Wetware computing is not only a new computing technology but marks the beginning of a paradigm shift: from inanimate machines to animate intelligence.

Hydrogels that used to be primarily utilized in wound healing or contact lens research are another important biocomputing approach and describe the realm of innovative human-machine interfaces. Due to their unique ability to store large amounts of water while retaining a soft, flexible, and biocompatible structure, hydrogels in terms of their mechanical properties bear a strong resemble to human tissue. That makes hydrogels particularly well suited for acting as a “soft bridge” between biological and electronic systems.

In combination with conductive nano-materials or ion conductors, hydrogels make it possible to create electrically active platforms enabling gentle signal transmission between nerve cells and technical equipment making it possible, for instance, for paraplegic people or stroke patients to enjoy a more independent life. Especially these compound materials make it possible to produce flexible electrodes clinging to cerebral surfaces, adjusting to neural pathways, or being implantable into soft tissue without provoking chronic scar tissue that often destroys signal quality when using rigid electrodes. This means that hydrogels are now evolving into one of the most exciting materials for industrial uses in the 21st century.

Although the water content of hydrogels amounts to about 90 percent, they remain dimensionally stable, unleashing new potential for smart materials and adaptive systems, with versatility as their special forte. That means they react to temperature, pH value, electric fields, or pressure while actively changing their properties. As a result, they become a functional connecting link between rigid technical systems and the dynamic, animate environment.

Applications of hydrogels

A key field of application is robotics and sensor systems. Soft robot arms developed based on hydrogels can move with similar gentleness and precision as biological muscles. They respond flexibly to pressure, friction, or heat which makes them perfectly suited for tasks in which conventional metal or silicon components reach their limits. This applies, for instance, to food processing, biotechnology, or handling of sensitive materials in electronics manufacturing.

In the area of smart sensors, conductive hydrogels can detect smallest chemical or physical changes and convert them into electric signals. In the chemical industry, hydrogel sensors could work as early warning systems to immediately detect leakage, contamination, or temperature excursions. In the construction industry, hydrogels could be used as moisture sensors for long-term monitoring of the structural integrity of concrete or brickwork.

Especially the automotive industry has long recognized the technology’s potential. Hydrogels can be used as adaptive damping and vibration systems that self-adjust to driving conditions. In automotive interior design, they enable new concepts of haptic interfaces, referring to controls that respond to touch, change their form, or provide tangible feedback. The result is an intuitive, tactile interface between humans and machines.

On the factory floor, hydrogels are gaining importance when it comes to additive manufacturing (3D printing), precision printing, or micro-fabrication. They can be used as temporary supporting structures in 3D printing processes or serve as substrates for functional coatings. Moreover, their ability to combine biological and technical material makes them attractive for biohybrid systems in which living cells are integrated in industrial processes.

This term describes another, particularly visionary approach to research striving to understand nature’s information processing on the level of quantum physics and to derive technological architectures from that understanding. While today’s quantum computers are using supra-conductive circuits or trapped ions to generate quantum bits quantum biocomputing takes this to another intellectual level: it investigates how biological systems maintain and can systematically use quantum coherence – i.e., the simultaneous existence of several states. Because what works in nature at room temperature - while today’s quantum computers must be cooled down to a few mini-kelvins (nearly minus 273 degrees centigrade/-459,4 Fahrenheit) - would mark a giant leap in terms of energy efficiency and miniaturization.

The key question is: How does nature manage to prevent the decomposition of sensitive quantum states? In the photosynthesis of algae and bacteria, for instance, it was demonstrated that the energy of a photon is distributed across various molecules in a kind of quantum-mechanical wave before arriving at the place of the chemical reaction. This mechanism is not only extremely efficient but also shows that biological systems have a kind of quantum coordination network. Scientists are currently still dreaming about developing artificial systems that can transport and process quantum information in similarly loss-free ways.