I probably should've talked about information theory when I went into entropy/order of biological systems.. Information stored in life systems can be measured and the loss of that information = entropy. There's neat universality to open systems having energy flow through them. This makes internal ordering a natural process.

Information theory serves as a framework for understanding the complex processes underlying the storage, processing, and transmission of information in biological systems. By integrating the work of Szilárd, Shannon, Landauer, and others, we can interpret biological phenomena through the principles of entropy, energy, and information flow. This article provides an exploration of how these principles operate across molecular, cellular, and organismal scales, focusing on specific applications such as DNA coding, amino acid encoding, and neural signaling.

Entropy and Information: The Fundamentals

Biological systems are open systems that exchange energy and matter with their environment. They use this energy to maintain order, develop structure, and perform essential functions, but in doing so, they generate entropy, which is expelled as waste heat or material. Entropy can be understood as a measure of disorder in a system, while information is the amount of knowledge required to describe the state of that system.

Szilárd showed that information carries a physical cost and relates directly to entropy. When a biological system lowers its internal entropy it forms ordered molecules such as proteins. By doing this it raises the entropy of its surroundings. Shannon translated this relationship into mathematics. He demonstrated that information content depends on how many states a system can adopt. We can measure how much information is stored or transmitted in biological processes. This lets us understand how efficient organisms are at managing order and complexity.

In biology entropy depends on the organization of molecular structures. A random collection of amino acids has high entropy and requires more information to fully describe. A folded protein or an ordered DNA sequence has lower entropy. It needs fewer bits of information to define its structure and function. This connection helps us see how living systems manage complexity by encoding it in stable molecular forms. It also shows that the drive to reduce internal entropy and maintain order has a quantifiable information cost.

Maxwell’s Demon and Information Processing in Biology

Maxwell’s demon is a classic thought experiment that appears to defy the second law of thermodynamics. It imagines a tiny entity sorting particles by speed without using energy. At first glance this reduces entropy at no cost. In practice, the demon must gather and process information about each particle’s motion. Acquiring this information requires effort and generates entropy. Szilárd and Landauer showed that erasing information also increases entropy by a fixed amount per bit. This link between information and entropy ensures that no system can circumvent basic thermodynamic limits.

Biological systems face the same constraints. DNA replication depends on selecting correct nucleotide bases. This selection relies on information stored in the template strand and requires energy. Neural signaling also involves information processing. Neurons release neurotransmitters into the synapse. They must produce, package, and recycle these molecules. Each step consumes energy and releases waste heat. This work raises entropy as information passes through molecular pathways. Life must follow these rules. It cannot create order without exporting disorder. Even as organisms build complexity and maintain function, they remain bound by the thermodynamics of information.

Information Storage and Coding in DNA

DNA is one of the most efficient storage systems in biology. It encodes information through a four-letter alphabet composed of adenine (A), thymine (T), cytosine (C), and guanine (G). These bases form triplets called codons. Each codon corresponds to an amino acid or a stop signal. In this way codons act as discrete units of biological information. Their sequences form the instructions for protein synthesis, linking the digital language of genes to the material structure of proteins.

In the absence of redundancy each three-base codon would specify one of 64 possible combinations. Four bases raised to the power of three equals 64 distinct codons. This represents about 6 bits of information per codon since 2 raised to the sixth power is 64. However, the genetic code does not use all codons with equal specificity. It is redundant, also described as degenerate, because multiple codons often encode the same amino acid. This degeneracy reduces the effective information content. Instead of a full 6 bits per codon the code delivers about 4.2 bits when accounting for this redundancy.

Further analysis refines this estimate by considering the probabilities of particular amino acids and their distributions in real proteins. Actual protein-coding sequences do not use amino acids equally. Some amino acids appear more often while others are rare. Accounting for these probabilities reduces the average information per amino acid to about 2.5 bits. This value reflects the interplay between the underlying combinatorial possibilities and the biological constraints on amino acid usage. It shows that while DNA is still highly efficient as a storage medium it is not operating at its theoretical maximum capacity. Instead it sacrifices some information density for robustness and adaptability, ensuring that the code can tolerate minor errors and variations while still encoding the complexity of life’s molecular machinery.

Proteins are not just sequences of amino acids. They carry layers of information that govern how they fold and interact with other molecules. The arrangement of amino acids determines the final three-dimensional shape of a protein. This shape influences its function, stability, and the pathways it participates in. Each protein’s amino acid sequence is dictated by the genetic code stored in DNA. Thus, the linear instructions in the genome translate into complex molecular architectures that perform essential tasks in the cell.

The human genome contains over three billion base pairs. From this massive pool of raw data it encodes around 18,000 proteins. Although this number is large, the true informational content is not just a simple count of letters multiplied by their possible variations. Redundancies, correlations, and sequence patterns reduce the effective complexity. Certain combinations of nucleotides recur often, while others are rare. Some amino acids appear more frequently, and structural motifs repeat across many proteins.

This underlying redundancy means the genome’s effective information is lower than one might calculate from a simple combinatorial standpoint. It resembles data compression, where statistical patterns allow for fewer bits of information to represent the same message. The genetic code uses many codons for a single amino acid. Some sequences form predictable structures like alpha helices or beta sheets. These regularities mean the genome is not a random string of letters. It is more like a carefully compressed library of blueprints, refined by evolution to produce functional molecules efficiently.

In computational systems, efficient encoding schemes like Huffman coding reduce the number of bits needed to represent common symbols. In a similar way, the genetic code relies on patterns and common building blocks. This reduces the total amount of unique information required to specify life’s molecular machinery. Natural selection has shaped this code to be both efficient and robust. By taking advantage of recurring motifs and stable folds, living systems minimize the risk of harmful mutations and optimize the resources needed to build and maintain their molecular components.

Altogether, this compression of genetic information allows the cell to generate an enormous range of functional proteins from a finite set of instructions. It ensures that the complexity of life can emerge and persist with a genome that is both manageable and evolvable. Each protein’s architecture reflects not only the raw instructions in DNA but also the intricate patterns and redundancies that shape biology’s remarkable information landscape.

Dynamics of Information Transfer

Consider how information flows within a biological system. At the most fundamental level, it requires a source of energy to drive its processes. A cell uses ATP generated by metabolic pathways, while a neuron relies on sugars in the bloodstream. With energy secured, the system can store information in stable forms. In cells, DNA serves as a durable archive of genetic instructions. In the nervous system, synaptic connections record experiences and learning patterns.

Stored information must be accessed and made useful. A decoder mechanism reads and translates these stored instructions into functional messages. During gene expression, DNA is transcribed into mRNA, which transforms the genetic code into a portable format. Similar decoding processes occur in neurons as they convert stored synaptic weights into meaningful signals.

A transmitter then moves these encoded messages to their next destination. Within a cell, mRNA travels from the nucleus to the cytoplasm. In a neuron, electrical impulses and neurotransmitters carry signals across synapses. The receiver, such as a ribosome, takes in the transmitted message. Ribosomes bind to mRNA and prepare for the next step. In neuronal systems, receiving cells detect incoming neurotransmitters and prepare to interpret them.

Interpreters convert these messages into functional outputs. In protein synthesis, tRNA molecules match codons to specific amino acids, turning a linear genetic code into a growing polypeptide chain. In the nervous system, specialized receptors and ion channels translate chemical signals into electrical changes in the cell, guiding the organism’s responses.

Analyzers then act on this processed information. Enzymes catalyze reactions, altering molecules and directing metabolic pathways. Neural circuits process sensory input, integrate it with stored patterns, and produce meaningful behavioral outputs. The final stage, the actuator, executes a tangible function. A protein emerges from the ribosome and folds into a specific shape, ready to perform tasks like catalysis or structural support. A muscle receives neural commands and contracts, enabling movement.

Throughout this sequence, the system must manage the inevitable rise in entropy. Energy sinks, such as heat dissipation and the removal of waste products, help maintain thermodynamic balance. This continuous export of entropy ensures that internal order can persist despite ongoing work.

Such a framework applies at many scales. Within a cell, the flow from DNA to RNA to protein follows these principles. Across neural networks, sensory inputs convert into electrical signals, travel through synaptic connections, and emerge as coordinated behavior. Whether at the molecular or organismal level, this structured flow of information integrates energy acquisition, code storage, message transmission, and functional output, all while adhering to the fundamental physical limits that govern life’s complexity.

Channel Capacity and Noise in Biological Communication

Shannon’s channel capacity theorem provides a fundamental benchmark for information flow. It defines the highest possible rate at which data can move through a given communication channel without error, assuming optimal encoding and decoding strategies. In biological systems, this concept helps us understand constraints on the transmission of signals within cells, between cells, and across entire organisms.

Consider neural signaling. Neurons communicate through electrical impulses and chemical neurotransmitters. These signals pass along axons and cross synapses to influence other neurons. The channel capacity in this context depends on the maximum firing rates of neurons, the precision of action potentials, and the reliability of synaptic transmission. Just as an electronic channel’s capacity is limited by bandwidth and noise, neural networks face similar constraints. Their signals can be distorted by random ion fluctuations, molecular drift, and variability in transmitter release. The brain’s complex architecture and feedback loops help manage these issues, enabling precise control of motor actions, sensory interpretations, and cognitive processes despite ever-present noise.

DNA replication and gene expression present another example. Genetic information moves from DNA to RNA to protein, and the fidelity of these steps depends on molecular machines that read, copy, and translate sequences. The system’s effective channel capacity reflects how much genetic information can be reliably transferred per unit time. Noise in this scenario may include mispaired nucleotides, transient base shifts, or strand breaks. Although none of these events is desired, the cell can mitigate their effects. Enzymes proofread newly synthesized strands and correct errors, reducing the probability that a single faulty base will propagate into functional proteins. This error correction improves the effective channel capacity of genetic information flow, ensuring that complex organisms can develop and function with remarkable consistency.

Redundancy also plays a key role. Biological systems often repeat critical sequences or distribute vital signals across multiple parallel pathways. This redundancy makes the message more robust against noise. Much like error-correcting codes used in digital communications, cells employ strategies that allow correct interpretation of information even if part of the signal is lost or corrupted. For instance, as we I mentioned earlier some amino acids are encoded by multiple codons, making mutations in one position less likely to disrupt protein function. Additional cellular mechanisms—such as alternative splicing, regulated gene expression, and molecular chaperones—further enhance the reliability of information processing.

Binary encoding serves as an instructive analogy. In digital systems, a single bit can represent two states, 0 or 1. Noise can flip these bits, causing errors. To maintain accuracy, engineers introduce error-correction codes and repeat critical signals multiple times. This approach increases reliability, but at a cost. More energy is spent verifying and correcting data, and more raw material is needed for redundant encoding. In biological terms, the cell pays a similar cost. Producing proofreading enzymes, maintaining quality-control pathways, and synthesizing redundant molecular components all require energy and resources. These expenditures increase the entropy exported into the environment as waste heat or discarded byproducts, satisfying physical laws that govern all energy transformations.

Shannon’s theorem thus offers more than a theoretical limit; it provides a lens through which we can view the balance between efficiency and reliability in biology. The capacity of any communication channel, whether an optical fiber or a nerve bundle, is never infinite. In both engineered and biological systems, information flow is molded by noise, resource availability, and the trade-offs between speed, accuracy, and energy consumption. Understanding these principles helps us appreciate how life maintains its remarkable coherence and complexity despite the ceaseless pressures of randomness and thermodynamic constraints.

Feedback and Regulatory Systems

Feedback loops play a central role in sustaining stability and adaptability within biological systems. They work by sensing changes in internal or external conditions, then adjusting responses to maintain operational integrity. Positive feedback loops amplify certain signals. This amplification can drive rapid transitions or initiate processes like blood clotting or the release of hormones during labor. Negative feedback loops have a stabilizing influence. They detect deviations from a preferred state and initiate corrective actions that bring the system back toward equilibrium. Thermoregulation in mammals offers an example. Sensors detect shifts in body temperature. Actuators, such as sweat glands or muscle contractions, then adjust heat production or release until the body’s core temperature returns to an acceptable range.

These feedback mechanisms do not simply enforce rigid stability. They often produce quasi-stable states that persist while conditions remain stable but shift when conditions change. In this way, they enable dynamic adaptation. Neural circuits rely heavily on feedback. Signals from one neuron can influence the strength of connections in another, reinforcing pathways that prove useful and weakening those that do not. Learning and memory formation depend on these interactions. Changes at synapses integrate past experiences into the neural architecture. The brain’s distributed networks capture useful patterns of activity and recall them later, allowing the organism to respond more effectively to similar situations in the future.

From a physics perspective, the existence of feedback loops requires continuous energy input. Without energy, the system could not correct deviations or store the memory of past adjustments. Each feedback action depends on processes that consume ATP, establish ion gradients, or maintain the chemical stability of signaling molecules. Biophysically, the transmission and reception of feedback signals arise from molecular interactions and conformational changes in proteins. These activities, occurring at the nanoscale, aggregate into functional control systems that guide an organism’s macroscopic behaviors.

Evolution refines these feedback loops over time. Systems that manage energy resources more efficiently tend to persist. Systems that accurately detect deviations and rapidly restore balance remain viable in changing environments. Cells use feedback loops to regulate gene expression, ensuring that proteins are synthesized at appropriate rates. Organs coordinate their activities through hormonal signals and sensory feedback, enabling whole-body homeostasis. Populations and ecosystems also exhibit feedback, as predator and prey numbers influence each other, producing cycles of abundance and scarcity.

The concept of feedback is closely related to cybernetics, a field that studies how systems—both living and engineered—use information, control, and communication to function. By treating biological networks as regulated machines governed by feedback, we’re able to gain some insights into how organisms maintain order, process inputs, and respond to shifting conditions. In engineering, similar principles guide the design of automated control systems, from thermostats to advanced robotics. In both realms, feedback loops serve as fundamental components that ensure stability, adaptivity, and efficiency under constraints imposed by energy, materials, and time.

Applications and Implications

Overall, information theory offers some deep insights into the nature of biological systems, a few of which I’ve mentioned earlier, and guides the engineering of artificial networks. By quantifying how data is stored, transmitted, and processed, this framework helps us understand how life encodes and decodes complexity. At the same time, it informs the design of synthetic systems that mimic or even surpass biological capabilities. Several key areas exemplify these principles:

DNA Coding and Compression:
The genetic code functions like a highly refined data compression scheme. DNA, composed of four nucleotides, must encode instructions for building and maintaining entire organisms. Through redundancy, multiple codons specify the same amino acid, reducing the impact of errors. This setup is analogous to error-correcting codes in digital communication, where redundancy ensures accurate signal interpretation. The degeneracy of the genetic code, combined with patterns that guide gene expression, allows life to store vast amounts of information within finite molecular space. These principles beautifully illustrate how our evolution has optimized molecular data storage for both stability and adaptability, ensuring that we remain resilient in the face of mutational noise.

Neural Networks and Learning:
Brains and artificial neural networks both process information by adjusting connections in response to inputs. In biological neural systems, synaptic plasticity alters the strength of synapses, fine-tuning the flow of signals and enabling learning, memory, and adaptation. In machine learning, algorithms such as backpropagation update connection weights to minimize error functions, improving performance over time. Both domains illustrate how feedback loops, iterative adjustments, and distributed representations support intelligent behavior. Studying brain-inspired learning rules provides engineers with blueprints for more robust, scalable computational architectures. In turn, artificial neural networks offer simplified models that help neuroscientists test hypotheses about how biological brains encode information.

Social Information Systems:
Groups of humans operate as interconnected information-processing networks. Knowledge spreads through language, writing, media, and digital tools, allowing communities to aggregate data, refine ideas, and solve problems collectively. This collective intelligence emerges from feedback loops, trust networks, and cultural norms that guide how information is shared and validated. Such socially constructed “databases” influence how quickly societies adapt to new challenges, discover scientific truths, or craft strategies for environmental stewardship. Understanding these group-level information dynamics can inspire better communication structures and policy-making processes, shaping the future of digital platforms and collaborative technologies.

Across these scales—molecular, neural, and social—biological and bio-inspired systems stand out for their ability to function efficiently despite noise, uncertainty, and limited resources. Cells manage error-prone biochemical reactions while maintaining fidelity in genetic information transfer. Organisms balance sensory overload and metabolic constraints to produce coherent, adaptive responses. Populations merge individual perspectives into shared intelligence, refining solutions over time.

This resilience arises from principles that information theory makes explicit. Redundancy in the genetic code and communication networks provides buffering against errors. Feedback loops help systems self-correct, maintaining stable function or shifting to new equilibria when conditions change. Error correction mechanisms, whether enzymatic proofreading or socially enforced fact-checking, ensure that valuable data persists while noise is filtered out. By applying these concepts in engineering and computing, we can create more robust AI, more effective biotechnology, and better communication infrastructures—systems that honor nature’s lessons on how to encode, transmit, and refine the information that makes complexity possible.

I do want to mention grounding, of course. Grounding establishes a stable reference potential for biological systems. From a physics perspective, this connection allows excess charge to flow between the body and the ground, thereby neutralizing electrical imbalances. By stabilizing electrical potentials, grounding can reduce random voltage fluctuations, improving signal-to-noise ratios in neural and cellular pathways. This lowered background noise may decrease the metabolic energy required for ion pumps and other regulatory mechanisms that maintain membrane potentials. Published studies have documented that grounding modulates inflammatory responses, lowers blood viscosity, and influences indicators of oxidative stress. In part, these effects arise because a stable electrical environment supports electron transfer processes that help control free radical activity.

From a biophysics standpoint, grounding can optimize the flow of bioelectric information. More stable membrane potentials mean that neurons and muscle cells expend less energy correcting for drift or compensating for electrical instabilities. Lower energy costs for maintaining ionic gradients may contribute to enhanced efficiency in neurotransmission and improved resource allocation for repair and maintenance. This stabilization may promote more coherent neural firing patterns and stronger signal fidelity during synaptic transmission, potentially improving cognitive and sensory processing. Grounding has also been associated with shifts in autonomic tone, indicating that it can influence regulatory circuits at multiple hierarchical levels.

At the bioenergetic scale, efficient signal processing and reduced oxidative stress imply a more balanced metabolic environment. With fewer random perturbations to correct and more stable electron distribution, cells may allocate energy more effectively. This could lead to improved performance of critical functions, including tissue repair, immune surveillance, and protein synthesis. The resulting increase in functional integrity can support adaptive processes such as learning and memory, which rely on stable neural architectures and precise molecular signaling. By linking the body’s electrical state to a large reservoir of free electrons, grounding provides a fundamental physical basis for regulating biological information processing.. But, I’m just speculating.. I don’t have a study for this, sorry ;)

Information theory offers a unifying lens through which to understand biological systems. It treats life’s molecular codes, cellular networks, and ecological webs as information channels governed by physical laws. By examining how data flows within and between organisms, we uncover how they transform raw energy into structured order and use that order to shape their futures. Each level of organization—from the precision of DNA encoding to the adaptability of neural circuits and the cultural complexity of human societies—reflects a continuum of information processing refined by selective pressures. Through this perspective, we see that organisms are not static structures but dynamic information integrators. They reconcile the tension between randomness and organization, harnessing just enough disorder to adapt and evolve, yet maintaining enough coherence to persist over time. In this balance lies the fundamental insight that life’s resilience, diversity, and sophistication emerge from universal principles of signal, meaning, and transformation.

Summary

Information theory offers a powerful foundation for analyzing how biological systems store, process, and transmit information. Organisms operate as open systems, continuously channeling energy to maintain order despite ongoing production of entropy. At the molecular scale, DNA uses a four-letter code arranged in triplets to encode amino acids with remarkable efficiency, balancing redundancy and precision. Though theoretical calculations suggest up to six bits of information per codon, evolutionary pressures reduce the practical information content to about 2.5 bits per amino acid. This tradeoff creates robust genetic coding that tolerates errors, preserves functionality, and ensures reliable information flow.

At larger scales, information transfer follows structured pathways. In cells, energy sources drive genetic and metabolic processes. Encoded instructions are transcribed, transmitted, and translated, while cellular components interpret messages, analyze inputs, and actuate responses. In neural networks, electrical and chemical signals fulfill similar roles, enabling perception, learning, and decision-making. Feedback mechanisms, both positive and negative, maintain stable conditions and support adaptation. Synaptic changes form the substrate of memory, reflecting how biological systems store and refine information over time.

Shannon’s channel capacity theorem provides a means to quantify the effects of noise on signal transmission. Biological channels, such as DNA replication and neural signaling, operate under thermodynamic constraints that set ultimate limits on fidelity and speed. They combat noise through error-correction mechanisms, proofreading enzymes, and redundancy in coding schemes, all of which require energy and generate entropy. From a physics perspective, these processes align with the laws of thermodynamics and statistical mechanics, illustrating how information must be acquired, refined, and protected against random perturbations.

Beyond the cellular and neural realms, social groups and ecosystems function as distributed information networks. They aggregate, transform, and disseminate knowledge, forming collective intelligences that guide survival strategies and resource allocation. The parallels between biological information management and engineered data compression, machine learning, and collaborative computing highlight common theoretical principles. By examining these patterns, we can appreciate how biological systems optimize information handling under finite energy budgets.

Information theory thus links molecular coding, cellular dynamics, neural integration, and social communication under a common conceptual umbrella. From a bioenergetic perspective, the cost of maintaining reliable information pathways is always balanced against energetic and material inputs. This balance ensures that organisms function efficiently within physical limits, adapt to changing environments, and persist over evolutionary timescales, all while encoding, transmitting, and interpreting the signals that define life.

Further Reading:

Uda, Shinsuke. "Application of Information Theory in Systems Biology." Biophysical Reviews, vol. 12, no. 3, 2020, pp. 377–384. https://doi.org/10.1007/s12551-020-00665-w.
Cartwright, Julyan H. E., et al. "Information, Coding, and Biological Function: The Dynamics of Life." Artificial Life, vol. 30, no. 1, 2024, pp. 16–27. https://doi.org/10.1162/artl_a_00432.
Reinagel, Pamela. "Information Theory in the Brain." Current Biology, vol. 10, no. 15, 2000, pp. R542–R545. https://doi.org/10.1016/S0960-9822(00)00609-6.
Chanda, Pritam, et al. "Information Theory in Computational Biology: Where We Stand Today." Entropy, vol. 22, no. 6, 2020, article 627. https://doi.org/10.3390/e22060627.
Adami, Christoph. "Information Theory in Molecular Biology." Physics of Life Reviews, vol. 1, no. 1, 2004, pp. 3–22. https://doi.org/10.1016/j.plrev.2004.01.002.
Kotani, Kazuhiko. "Life from the Viewpoint of Information." Open Journal of Philosophy, vol. 9, no. 4, 2019, pp. 503–511. https://doi.org/10.4236/ojpp.2019.94031.
Bartal, Alon, and Kathleen M. Jagodnik. "Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics." Entropy, vol. 24, no. 7, 2022, article 925. https://doi.org/10.3390/e24070925.

Organism Information - Information Theory, The Biophysical Basis