BIOLOGICAL COMPUTERS

A biological computer is a computing system that uses biological molecules, cells or living tissues rather than silicon to store, process and transmit information.

There are several types of biological computers and they are; DNA, protein-based, cell-based/synthetic biology, organiod/brain on chip (OI-organiod intelligence), neuro-morphic chips (bio inspired), and quantum-biological hybrid biological computers.

DNA computers use strands of DNA to represent and process data through biochemical reactions. DNA bases (A, T, G, C) encode binary like data such that complementary strands binding are used to performs logical operations.

Protein-based computers exploit protein conformational changes such as folding and unfolding as switching mechanisms similar to transistors.

Cell-based/synthetic biological computers use living cells that are genetically engineered to function as logic gates or circuits. Gene expression acts as an ON/OFF switches such that  networks of genes form a cell-based biological computer circuits.

Organoid/brain on chip computers (OI-organoid intelligence) uses clusters of human cells (organoids) grown in labs and interfaced with electronics to perform calculations. The process of organiod computing, Neurons fire electrical signals read by multi-electrode array and learning occurs via synaptic plasticity.

Neuro-morphic chips (bio inspired) computing uses silicon chips to mimic the architecture and signaling of biological neurons.

Quantum-biological hybrid biological computers explores quantum effects in biological molecules such as photosynthesis, bird navigation etc., as a computational substrate.

The advantages of biological computers are; they display massive parallelism so that DNA/cell systems can run billions of operations simultaneously in solutions, they are energy efficient, brain oraganiod uses approximately 1million times less energy than silicon for equivalent task. They are capable of extraordinary storage density such as 1gram of DNA can store approx. 215petabytes of data. They are biocompatible and they can directly interface a living tissue for medical applications. They are self-replicating so biological systems can replicate, repair and evolve. They are scalable and their cells can multiply naturally thereby scaling compute. They are adaptable living systems and can learn and reorganize without programming.

The disadvantages of biological computers are; biochemical reactions are slow compared to electronic switching (nanoseconds versus milliseconds). DNA replication and biochemical reactions introduce noise and mutations. Biological materials degrade thus they require precise temperature, PH, humidity etc. to function optimally. Biological computers are extremely difficult to design, debug and reprogram biological circuits. Growing and maintaining viable organoids at scale is technically demanding. Brain organoid computing raises serious questions about consciousness and consent. Living biological computers can mutate, evolve or escape containment. Translating biological outputs back to digital equivalent signals is complex and difficult.

The application of biological computers are as follows; they find applications in data storage, smart drug delivery, cell based biosensor diagnostics, neuron-based chips, interface with nerve tissue for prosthetic limbs, engineered bacteria to detect metals pollutants or radiation and signal a readable output, wearable biosensors and stealth chemical detectors using live cell computing and DNA-based encryption and steganography such as hiding messages within synthetic DNA.

The future of biological computers is based on the advances and the development of the following technologies; hybrid-silicon biological chips. DNA storage and organiod intelligence platforms. Also the ability to program living cells for implantable diagnosis and therapeutic agents will be feasible. In the future it is expected that neuro-morphic -organoid hybrids would achieve complex reasoning with micro-watt power budget and biological logic gates will be integrated into synthetic organisms for environment cleanups. Finally whole brain emulation and self-healing computers will be norm.

 

SOURCES:

• DNA computing: New computing paradigms by Gherorghe Paun, Grzegorz Rozenberg, and Arto Saloma.
• Biological computation by Ehud Lamm and Ron Unger.
• Computational molecular biology: An algorithmic approach by Pavel Pevzner.
• Neuromorphic computing and engineering by Harish Garg.
• An introduction to systems biology: Design principles of biological circuits by Uri Alon.