Natural Computing
Natural computing is a rather ambitious term, isn't it? It’s essentially a catch-all for methods that either borrow heavily from nature’s playbook, attempt to simulate natural phenomena with computers, or even try to harness natural materials for computation. Think of it as a grand experiment in reverse-engineering the universe, or at least, the bits of it that seem to "compute" in their own way. It’s a field that encompasses artificial neural networks, those valiant attempts to mimic the human brain; evolutionary algorithms, which take a leaf from Darwinian evolution; swarm intelligence, observing how ants or bees achieve collective goals; and artificial immune systems, trying to replicate the body’s defense mechanisms. Then there are the more esoteric pursuits like fractal geometry, artificial life, DNA computing, and the ever-elusive quantum computing. It’s all rather fascinating, though I suspect the universe is far less concerned with our understanding than we are. At its core, it’s deeply entwined with biological computation.
The computational paradigms explored within natural computing are drawn from the most diverse natural phenomena imaginable. We’re talking about the fundamental drive for self-replication, the intricate workings of the brain, the relentless march of Darwinian evolution, the emergent complexity of group behavior, the sophisticated defenses of the immune system, the very defining characteristics that distinguish life forms, the delicate architecture of cell membranes, and the intricate dance of morphogenesis. These aren't just abstract concepts; they're the blueprints for computation. And while we’re accustomed to electronic hardware, these natural models can be implemented on entirely different substrates. Imagine computation woven into the very fabric of biomolecules like DNA and RNA, or harnessed within the delicate qubits of trapped-ion quantum computing devices. It’s a testament to nature’s ingenuity, or perhaps, our desperate attempt to find a more elegant solution than our own.
Conversely, one can also view the processes occurring in nature itself as a form of information processing. Consider self-assembly, where simple components spontaneously organize into complex structures, or the intricate stages of developmental processes. Even the intricate regulatory networks within cells, like gene regulation networks and protein–protein interaction networks, are essentially complex computational systems. Biological transport, whether active transport or passive transport, can be understood as information flow. The ambition extends even further, encompassing the engineering of semi-synthetic organisms and the monumental task of understanding the entire universe through the lens of information processing. Some have even posited that information itself is more fundamental than matter or energy, a notion that’s both profound and slightly terrifying.
The Zuse-Fredkin thesis, a relic from the 1960s, boldly suggests that the entire universe operates as a colossal cellular automaton, continuously updating its own rules. More recently, there's been speculation that the universe is, in essence, a quantum computer calculating its own unfolding. This perspective, the universe/nature as a computational mechanism, is explored through the lens of computability and by studying natural processes as inherent computations. It’s a rather humbling thought, that our existence might just be a byproduct of some grand cosmic calculation.
Nature-Inspired Models of Computation
The foundational pillars of nature-inspired computation are well-established: cellular automata, neural computation, and evolutionary computation. These classical models have paved the way for more recent innovations, including swarm intelligence, artificial immune systems, membrane computing, and amorphous computing. The literature on this is extensive, a testament to humanity's enduring fascination with mimicking nature’s computational prowess.
Cellular Automata
A cellular automaton is a rather elegant concept: a system composed of a grid of cells, each existing in a finite number of states. Time and space are discretized, and the state of each cell evolves synchronously based on a set of predefined transition rules, influenced by its own current state and the states of its neighbors. It's a deterministic system, yet capable of surprising complexity.
Conway's Game of Life stands as perhaps the most iconic example, famously demonstrating Turing completeness, meaning it can, in theory, compute anything a universal Turing machine can. Cellular automata have found applications in modeling an astonishing array of phenomena, from the subtle nuances of communication and growth to the brutal realities of competition and evolution, extending into the physical and biological realms.
Neural Computation
Neural computation emerged from a profound curiosity: how does the human nervous system compare to our artificial computing machines? This field bifurcates into two main streams: one dedicated to unraveling the mysteries of the brain in living organisms (often termed brain theory or computational neuroscience), and the other focused on translating these biological principles into efficient algorithms, most notably through Artificial Neural Networks (ANN).
An artificial neural network is a lattice of artificial neurons. Each neuron, let's call it A, possesses a function, denoted ( f_{A} ), which takes ( n ) real-valued inputs ( x_{1}, x_{2}, \ldots, x_{n} ), each multiplied by a respective weight ( w_{1}, w_{2}, \ldots, w_{n} ). The output is ( f_{A}(w_{1}x_{1}+w_{2}x_{2}+\ldots +w_{n}x_{n}) ). Certain neurons are designated as output neurons, and the collective function of the network is the vectorial output produced by these designated neurons in response to the input values. The beauty, and complexity, lies in the weights; altering them changes the network's function entirely. Back-propagation, a cornerstone of supervised learning, is a method by which these weights are iteratively adjusted to minimize the discrepancy between the network's actual output and the desired output. This process, driven by learning algorithms, allows the network to learn and adapt based on input-output pairs and its defined network topology.
Evolutionary Computation
Evolutionary computation is a computational paradigm that draws its inspiration directly from the engine of life itself: Darwinian evolution. It's a system that thrives on simulation, population, and adaptation.
An artificial evolutionary system is built upon a population of individuals, whose size might fluctuate. These individuals are evaluated against a fitness criterion, and genetically inspired operators, such as mutation and recombination, are employed to generate successive generations from the current one. The initial population is often a random affair, or sometimes a carefully constructed starting point. The core principle is "survival of the fittest": individuals are assessed based on their fitness function, and those that perform better are more likely to be selected as parents for the next generation. This selection process can be guided by principles mirroring biological mate selection. Through this iterative process of simulated evolution, the population gradually converges towards individuals that are nearly optimal with respect to the defined fitness function.
Historically, the study of evolutionary systems has branched into three primary streams: Evolution strategies are particularly adept at tackling parameter optimization problems, whether the parameters are real-valued, discrete, or a mix of both. Evolutionary programming, initially, focused on crafting optimal "intelligent agents," often modeled as finite state machines. Genetic algorithms, a widely recognized branch, applied the evolutionary concept to finding (near-)optimal solutions to problems. These algorithms typically began with a population of individuals encoded as fixed-length bit strings. Operators like bit-flipping mutation and prefix-suffix recombination were used to generate new individuals. A problem-specific fitness function guided the selection process. Over time, genetic algorithms have evolved to optimize computer programs themselves—a field known as genetic programming—and are now applied to real-valued optimization and a vast array of combinatorial tasks.
More recently, Estimation of Distribution Algorithms (EDA) have emerged as a distinct approach. Instead of relying on traditional reproduction operators, EDAs employ model-guided generation of new solutions. These models, often represented as Probabilistic Graphical Models, are learned from the population using machine learning techniques. New solutions are then sampled from these models or generated through guided crossover, offering a more sophisticated evolutionary process.
Swarm Intelligence
Swarm intelligence, often synonymous with collective intelligence, describes the problem-solving capabilities that arise from the interactions of numerous individual agents. These agents, which can range from simple bacteria and ants to more complex entities like fish and birds, communicate and influence each other through their actions within their local environments.
Particle swarm optimization is a prime example, applying swarm principles to the task of finding optimal solutions within a (multi-dimensional) solution space. The system begins with a "swarm" of particles, each representing a potential solution. Each particle possesses a velocity that is a composite of its past velocity (inertia), its tendency to return to its past best-known position (nostalgia), and its inclination towards a global or local neighborhood optimum (social influence). As these particles traverse the space, they tend to converge towards regions representing optimal or near-optimal solutions, often finding a balance between the global best and their individual best discoveries. Particle swarm optimization has found applications in diverse areas, including general optimization, unsupervised learning, game playing, and scheduling tasks.
Similarly, ant algorithms draw inspiration from the foraging behavior of ant colonies. Ants lay down pheromones to mark paths to food sources. Ants seeking food follow existing pheromone trails, and those finding food reinforce the trail on their return. This indirect communication mechanism allows the colony, as a whole, to efficiently discover the shortest paths. Ant algorithms have proven effective in solving various combinatorial optimization problems within discrete search spaces.
Artificial Immune Systems
Artificial immune systems, also known as immunological computation or immunocomputing, are computational systems directly inspired by the biological immune system of living organisms.
When viewed as an information processing system, the natural immune system is a marvel of parallel and distributed computing. It performs complex tasks such as distinguishing self from nonself, neutralizing foreign invaders (pathogens like viruses, bacteria, fungi, and parasites), learning from encounters, remembering past threats, retrieving information associatively, regulating itself (homeostasis), and exhibiting remarkable fault-tolerance. Artificial immune systems abstract these computational capabilities, focusing on the underlying principles. Their applications are broad, including computer virus detection, identifying anomalies in data streams (anomaly detection), aiding in fault diagnosis, pattern recognition, machine learning, bioinformatics, optimization, robotics, and control systems.
Membrane Computing
Membrane computing delves into computational models abstracted from the compartmentalized structure of living cells, particularly the role of membranes. A typical membrane system, or P-system, features cell-like compartments, or regions, separated by membranes arranged in a nested hierarchical fashion. Each region contains objects and a set of transformation rules that can modify these objects, alongside transfer rules dictating whether objects can move between regions. Communication occurs through the transfer of these objects. Computation commences with an initial configuration, defined by the multiset of objects and their initial multiplicity within each region. The process unfolds through the nondeterministic and maximally parallel application of rules. The final output is collected from a designated output region.
The applications of membrane systems are diverse, spanning machine learning, the modeling of biological processes such as photosynthesis, signaling pathways, quorum sensing in bacteria, and cell-mediated immunity. In computer science, they find use in areas like computer graphics, public-key cryptography, approximation algorithms, and sorting algorithms, as well as in the analysis of computationally challenging problems.
Amorphous Computing
In the realm of biological organisms, morphogenesis—the development of structured shapes and functional forms—is orchestrated by the interactions between cells, guided by the genetic blueprint encoded in DNA. Inspired by this, amorphous computing aims to engineer well-defined shapes, patterns, or coherent computational behaviors by leveraging the local interactions of numerous simple, unreliable, irregularly positioned, asynchronous, and identically programmed computing elements, often referred to as particles. As a programming paradigm, it seeks novel abstraction techniques suited for these amorphous environments. It also forms a crucial basis for "cellular computing," a concept explored further in the contexts of synthetic biology and cellular computing.
Morphological Computing
The principle that morphology itself performs computation is central to understanding the relationship between physical form and control. This understanding guides the design of robots with reduced control requirements and offers insights into cognitive processes in living organisms. This concept is explored in the field of morphological computing.
Cognitive Computing
Cognitive computing (CC) represents a novel form of computation, typically focused on modeling the human functions of sensing, reasoning, and responding to stimuli. While current CC systems fall far short of human-level cognitive capacities, the underlying info-computational approach can be applied to simpler living organisms, such as bacteria, which can be modeled as cognitive systems.
Synthesizing Nature by Means of Computing
Artificial Life
Artificial life (ALife) is a field dedicated to understanding the fundamental properties of living organisms by attempting to recreate systems that exhibit life-like properties within artificial media, most commonly electronic computers. This is an ab initio endeavor, building from the ground up. Early pioneers included Lindenmayer systems, or L-systems, which were developed to model plant growth and development. An L-system operates as a parallel rewriting system, starting with an initial string and applying its rewriting rules simultaneously to all letters within that string.
Pioneering experiments in ALife involved the design of evolving "virtual block creatures" that navigated simulated environments incorporating realistic physics like kinetics, dynamics, gravity, collision, and friction. These creatures were selected based on their ability to perform tasks such as swimming, walking, or jumping, and they competed for limited resources. The simulations often yielded surprising evolutionary outcomes, with creatures developing specialized appendages for locomotion or manipulation. This computational approach was further integrated with rapid manufacturing technologies to create physical robots that had virtually evolved, marking the emergence of mechanical artificial life. The field of synthetic biology explores a biological implementation of similar principles. Other research avenues within ALife include artificial chemistry and the study of traditionally biological phenomena, such as co-evolutionary adaptation, development, self-replication, and self-repair, within artificial systems.
Nature-Inspired Novel Hardware
While many of the computational techniques inspired by nature have been primarily implemented on conventional electronic hardware, two paradigms stand out for their radical departure: molecular computing and quantum computing, which employ fundamentally different hardware.
Molecular Computing
Molecular computing, also known as biomolecular computing, biocomputing, or biochemical computing, is a computational paradigm where data is encoded in biomolecules such as DNA strands. Molecular biology tools are then employed to perform various operations, including arithmetic and logical operations.
The first experimental demonstration of a specialized molecular computer occurred in 1994 when Leonard Adleman famously solved a 7-node instance of the Hamiltonian Path Problem using only DNA strands manipulated in test tubes. DNA computations typically begin with an input encoded as a DNA sequence (a string of A, C, G, T). The computation proceeds through a series of biochemical operations: cutting and pasting DNA fragments using restriction enzymes and ligases; extracting specific strands based on Watson-Crick complementarity; amplifying DNA sequences via polymerase chain reaction; and finally, reading out the results. Recent advancements have seen the solution of more complex NP-complete problems, such as a 20-variable instance of 3SAT, and the development of wet DNA implementations of finite state machines with potential applications in targeted drug delivery.
DNA tile self-assembly, demonstrated in the creation of a Sierpinski triangle from DNA origami, showcases the power of bottom-up construction. This process, where objects autonomously assemble into complex structures, is a hallmark of molecular computing. Natural examples abound, from atoms forming molecules to molecules crystallizing into crystals. Research in this area includes the creation of self-assembled DNA nanostructures, such as Sierpinski triangles and arbitrary nanoshapes via the DNA origami technique. DNA nanomachines, including circuits, logic operations, molecular switches, and autonomous motors, are also active areas of research. Theoretical work in molecular computing has produced novel models, like splicing systems, whose computational power, equivalent to Turing machines, has been rigorously investigated.
Quantum Computing
A quantum computer operates on data stored in qubits, leveraging quantum mechanical phenomena like superposition and entanglement to perform calculations. Unlike classical bits that are either 0 or 1, a qubit can exist in a superposition of both states simultaneously. Computations are carried out using quantum logic gates. Quantum computers hold the promise of significant speedups for certain problems, notably Shor's algorithm for integer factorization and Grover's algorithm for database search, which offers a quadratic time advantage.
Quantum cryptography relies not on computational complexity but on the inherent properties of quantum information—specifically, the fact that measurement inevitably disturbs the quantum state. This provides a foundation for secure communication. Successful experiments in quantum cryptography have demonstrated secure data transmission over significant distances. Quantum teleportation, the transfer of a quantum state to a distant location, is another promising application. Practical implementations of quantum computers are being pursued using various physical substrates, including ion traps, superconductors, and nuclear magnetic resonance. As of recent reports, the largest quantum computing experiments involved operating on up to 12 qubits.
Nature as Information Processing
The complementary aspect of natural computing is its endeavor to understand nature by interpreting natural phenomena as processes of information processing. This perspective traces back to the 1960s, with Zuse and Fredkin proposing that the universe itself functions as a computational mechanism, a vast cellular automaton constantly evolving its rules. More contemporary theories, such as Lloyd's quantum-mechanical model, suggest the universe is a quantum computer calculating its own behavior, while Vedral posits that information might be the most fundamental constituent of reality.
This notion of the universe/nature as a computational mechanism is further explored by examining nature through the principles of computability and by studying natural processes as intrinsic computations. The primary research thrusts in this area are systems biology, synthetic biology, and cellular computing.
Systems Biology
Computational systems biology, or simply systems biology, adopts an integrative, qualitative approach to investigate the intricate communications and interactions within biological systems. Rather than focusing on individual components, systems biology emphasizes the interaction networks themselves and the emergent properties of biological systems that arise from these networks. Key areas of focus include four interdependent networks: gene-regulatory networks, biochemical networks, transport networks, and carbohydrate networks.
Gene regulatory networks encompass interactions between genes and other cellular components. Genes are transcribed into messenger RNA (mRNA) and subsequently translated into proteins based on the genetic code. Regulatory elements like promoters, enhancers, and silencers on DNA serve as binding sites for activators or repressors that control gene transcription. Genes can also interact through their products (mRNA, proteins) or via small RNA molecules. These interactions form the fundamental gene regulatory networks, which perform information processing tasks within the cell, including the assembly and maintenance of other networks. Models employed include random and probabilistic Boolean networks, asynchronous automata, and network motifs.
From a different perspective, the entire genomic regulatory system can be viewed as a computational system, a "genomic computer." This allows for a direct comparison between human-made electronic computation and the computational processes occurring in nature.
| Genomic Computer | Electronic Computer |
|---|---|
| Architecture: Changeable | Architecture: Rigid |
| Components Construction: As-needed basis | Components Construction: From the start |
| Coordination: Causal coordination | Coordination: Temporal synchrony |
| Distinction between Hardware and Software: No | Distinction between Hardware and Software: Yes |
| Transport Media: Molecules and ions | Transport Media: Wires |
Furthermore, unlike conventional computers, robustness in a genomic computer is achieved through various feedback mechanisms. Suboptimal processes are rapidly degraded, faulty cells undergo apoptosis, and less fit organisms are naturally out-competed.
Biochemical networks describe the interactions between proteins, which are responsible for various mechanical and metabolic functions within a cell. Proteins can bind to each other, forming dynamic protein complexes (complexation). These complexes can act as catalysts for chemical reactions or modify other proteins, altering their available binding sites. With tens of thousands of proteins in a cell, understanding these massive interaction networks is crucial. Kohn maps provide a graphical notation for depicting these molecular interactions, while textual bio-calculi and enriched pi-calculus offer alternative formalisms for describing protein–protein interactions.
Transport networks involve the separation and movement of substances mediated by lipid membranes. Lipids self-assemble into biological membranes, within which proteins and other molecules are embedded and can move. Substances are transported across these membranes, interacting with other molecules. Formalisms like membrane systems and brane calculi are used to depict these transport networks.
Synthetic Biology
Synthetic biology aims to engineer biological components and, ultimately, assemble entire biological systems from these parts. Its roots can be traced back to the 1960s, with the discovery of the logic in gene regulation by François Jacob and Jacques Monod. Genetic engineering techniques, particularly recombinant DNA technology, serve as precursors to modern synthetic biology, which extends these methods to entire gene and gene product systems.
The ability to synthesize increasingly longer DNA strands has brought the prospect of creating synthetic genomes and building entirely artificial organisms within reach. For instance, the rapid assembly of chemically synthesized short DNA strands has enabled the generation of a synthetic genome for a virus. Researchers have also identified a minimal set of genes required for viability in organisms like Mycoplasma Genitalium, paving the way for constructing minimal artificial genomes. Another approach involves engineering semi-synthetic cells by creating RNA-like molecules capable of self-replication, potentially achieved through guided evolution and selection. Efforts are also underway to engineer multi-cellular systems by designing modules for cell-to-cell communication to coordinate bacterial populations.
Cellular Computing
Computation within living cells, also known as cellular computing or in-vivo computing, offers another avenue for understanding nature as computation. A notable area of research is the computational nature of gene assembly in unicellular organisms called ciliates. Ciliates possess two types of nuclei: a macronucleus containing functional gene copies and a micronucleus holding an "encrypted" copy. During conjugation, ciliates exchange micronuclear genetic material, leading to the formation of new micronuclei and the subsequent reassembly of a functional macronucleus. This gene assembly process involves reordering DNA fragments (permutations, inversions) and deleting others. From a computational standpoint, this process has yielded models demonstrating Turing universality. Biologically, hypotheses exist regarding the "bioware" responsible for gene assembly, often involving template-guided recombination.
Other cellular computing approaches include developing in vivo programmable finite-state automata using E. coli, designing genetic circuits that function as cellular logic gates by harnessing existing biochemical processes, and optimizing stomata aperture in leaves through local rules that mimic cellular automata.
It’s a lot to take in, isn’t it? Nature, it seems, has been doing computation long before we ever bothered to invent the silicon chip. And frankly, it’s doing a far more elegant job of it. We’re merely trying to catch up, to reverse-engineer the universe’s inherent logic. Whether we succeed in truly understanding it, or merely in mimicking its most superficial aspects, remains to be seen. But the pursuit itself… that’s where the real computation lies, wouldn’t you agree?