Pub Date : 2025-10-30DOI: 10.1016/j.biosystems.2025.105639
A.V. Melkikh
The issue of rapid adaptation of microorganisms to changing environments is examined. The mechanism of adaptive mutations is analyzed. The possibility that horizontal gene transfer is a random process is discussed. Bacteria, unicellular fungi, and other microorganisms successfully adapt to fast-changing conditions (such as exposure to drugs) because their evolution is not a random process. Adaptation to antibiotics, adaptive mutations, and related phenomena occur because microbial evolution is inherently directed and purposefully oriented toward potential external changes. Rejecting gene-centricity plays a crucial role in understanding the coevolution of humans and pathogens. This means that beyond genes, there exists a higher-level system—an organism with its own unique properties that cannot be reduced to genes. The problem of human adaptation to infectious agents (viruses, bacteria, and protozoa) is also analyzed. Based on general systems theory, it is concluded that humans and pathogens coevolve in a controlled manner.
{"title":"Humans and microbes: A systems theory perspective on coevolution","authors":"A.V. Melkikh","doi":"10.1016/j.biosystems.2025.105639","DOIUrl":"10.1016/j.biosystems.2025.105639","url":null,"abstract":"<div><div>The issue of rapid adaptation of microorganisms to changing environments is examined. The mechanism of adaptive mutations is analyzed. The possibility that horizontal gene transfer is a random process is discussed. Bacteria, unicellular fungi, and other microorganisms successfully adapt to fast-changing conditions (such as exposure to drugs) because their evolution is not a random process. Adaptation to antibiotics, adaptive mutations, and related phenomena occur because microbial evolution is inherently directed and purposefully oriented toward potential external changes. Rejecting gene-centricity plays a crucial role in understanding the coevolution of humans and pathogens. This means that beyond genes, there exists a higher-level system—an organism with its own unique properties that cannot be reduced to genes. The problem of human adaptation to infectious agents (viruses, bacteria, and protozoa) is also analyzed. Based on general systems theory, it is concluded that humans and pathogens coevolve in a controlled manner.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105639"},"PeriodicalIF":1.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-29DOI: 10.1016/j.biosystems.2025.105629
Alexander Spirov
This review explores the deployment of hereditary information during embryogenesis through a semiotic and linguistic perspective, examining how genetic information unfolds from a single fertilized egg into a complex multicellular organism. Building upon foundational work by Howard Pattee, Jesper Hoffmeyer, and Terrence Deacon, we analyze how biomolecules transition from simple chemical entities to information-carrying signs through constraints and scaffolding mechanisms that organize biological processes into hierarchical semiotic systems. The study identifies four main hierarchical "languages" governing genetic information unfolding. Each hierarchical level demonstrates distinct semiotic properties analogous to human language structures, including vocabulary (signaling molecules), and grammar (combinatorial rules), including syntax (temporal and spatial organization). Gene regulatory networks function as complex linguistic systems where the transcription factor binding sites act as "words" organized into "sentences" (regulatory modules) that control gene expression patterns. Intercellular communication resembles sophisticated languages with morphogens, cytokines, and growth factors serving as molecular "lexicon" governed by grammatical rules determining signal integration and interpretation. The geographical metaphor proves particularly illuminating, comparing embryonic morphogenetic fields to linguistic territories with distinct communication systems and simplified "contact languages" at boundaries between adjacent developmental domains. Context-dependent mechanisms, epigenetic modifications, and cellular reprogramming processes add additional layers of meaning, similar to semantic and pragmatic aspects of human language. The striking parallels between biological "languages" and both human linguistic structures and computer programming languages suggest universal principles governing complex information processing systems across diverse domains.
{"title":"Languages of Hereditary Information Unfolding in Development: Linguistic Analogies and Semiotic Aspects.","authors":"Alexander Spirov","doi":"10.1016/j.biosystems.2025.105629","DOIUrl":"https://doi.org/10.1016/j.biosystems.2025.105629","url":null,"abstract":"<p><p>This review explores the deployment of hereditary information during embryogenesis through a semiotic and linguistic perspective, examining how genetic information unfolds from a single fertilized egg into a complex multicellular organism. Building upon foundational work by Howard Pattee, Jesper Hoffmeyer, and Terrence Deacon, we analyze how biomolecules transition from simple chemical entities to information-carrying signs through constraints and scaffolding mechanisms that organize biological processes into hierarchical semiotic systems. The study identifies four main hierarchical \"languages\" governing genetic information unfolding. Each hierarchical level demonstrates distinct semiotic properties analogous to human language structures, including vocabulary (signaling molecules), and grammar (combinatorial rules), including syntax (temporal and spatial organization). Gene regulatory networks function as complex linguistic systems where the transcription factor binding sites act as \"words\" organized into \"sentences\" (regulatory modules) that control gene expression patterns. Intercellular communication resembles sophisticated languages with morphogens, cytokines, and growth factors serving as molecular \"lexicon\" governed by grammatical rules determining signal integration and interpretation. The geographical metaphor proves particularly illuminating, comparing embryonic morphogenetic fields to linguistic territories with distinct communication systems and simplified \"contact languages\" at boundaries between adjacent developmental domains. Context-dependent mechanisms, epigenetic modifications, and cellular reprogramming processes add additional layers of meaning, similar to semantic and pragmatic aspects of human language. The striking parallels between biological \"languages\" and both human linguistic structures and computer programming languages suggest universal principles governing complex information processing systems across diverse domains.</p>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":" ","pages":"105629"},"PeriodicalIF":1.9,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145423274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-25DOI: 10.1016/j.biosystems.2025.105633
Gabi Drochioiu
Bacteriorhodopsin (BR) is involved in the process of light-induced release of ATP molecules from F1F0-ATP synthase in the simplest bacterial cells. We have identified the M412 intermediate of BR to be a key for understanding the phenomena related to ATP production in Halobacterium salinarum cells. In addition, the release of protons and, consequently, the increase in BR acidity upon illumination can be explained by its fluorescence, and the Förster cycle allows the calculation of pKa changes. The light-induced excitation of electrons in unprotonated M412 intermediate can serve as an energy source and not protons. To reinforce our conclusions, we reexamined data from recent literature as well as older findings.
{"title":"Light-induced ATP production and proton translocation, two independent phenomena in Halobacterium salinarum archaea cells","authors":"Gabi Drochioiu","doi":"10.1016/j.biosystems.2025.105633","DOIUrl":"10.1016/j.biosystems.2025.105633","url":null,"abstract":"<div><div>Bacteriorhodopsin (BR) is involved in the process of light-induced release of ATP molecules from F<sub>1</sub>F<sub>0</sub>-ATP synthase in the simplest bacterial cells. We have identified the M<sub>412</sub> intermediate of BR to be a key for understanding the phenomena related to ATP production in <em>Halobacterium salinarum</em> cells. In addition, the release of protons and, consequently, the increase in BR acidity upon illumination can be explained by its fluorescence, and the Förster cycle allows the calculation of pKa changes. The light-induced excitation of electrons in unprotonated M<sub>412</sub> intermediate can serve as an energy source and not protons. To reinforce our conclusions, we reexamined data from recent literature as well as older findings.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105633"},"PeriodicalIF":1.9,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-24DOI: 10.1016/j.biosystems.2025.105631
Ryunosuke Suzuki , Taiji Adachi
In a living system composed of interacting components such as molecules, cells, and tissues, each component often changes its internal states in response to interactions with its surrounding components. For example, individual tissues exhibit component-level responsive behavior, such as growth and remodeling, in response to their mechanical interactions, resulting in the self-organization of functions of a multi-tissue system. Along with the responsive behavior of the components, their interactions exhibit dynamical changes, which strongly influence the self-organization of system functions. To understand how the self-organization of system functions occurs from such dynamical interactions due to component-level responsive behavior, this study proposes a theoretical framework that formulates the dynamics of interactions among components due to the component-level responsive behavior. For modeling the responsive internal state changes, we assign an energy landscape and its associated energy rate landscape for each component, leading to the generalized gradient flow model of responsive behavior. Then, we represent interaction dynamics based on temporal changes in these energy and energy rate landscapes by formulating temporal changes in the environmental states of each component due to the responsive behavior of individual components. Through case studies using simplified models of mechanically interacting tissues under morphological changes, our theoretical framework demonstrates that temporal changes in applied forces due to morphological changes of individual tissues determine the self-organization of system functions. These findings highlight that expressing interaction dynamics based on temporal changes in energy and energy rate landscapes offers a powerful theoretical framework for understanding how component-level responsive behavior organizes system functions.
{"title":"An energy landscape-based theoretical framework for understanding the self-organization of functions in a living system under the dynamical component interaction","authors":"Ryunosuke Suzuki , Taiji Adachi","doi":"10.1016/j.biosystems.2025.105631","DOIUrl":"10.1016/j.biosystems.2025.105631","url":null,"abstract":"<div><div>In a living system composed of interacting components such as molecules, cells, and tissues, each component often changes its internal states in response to interactions with its surrounding components. For example, individual tissues exhibit component-level responsive behavior, such as growth and remodeling, in response to their mechanical interactions, resulting in the self-organization of functions of a multi-tissue system. Along with the responsive behavior of the components, their interactions exhibit dynamical changes, which strongly influence the self-organization of system functions. To understand how the self-organization of system functions occurs from such dynamical interactions due to component-level responsive behavior, this study proposes a theoretical framework that formulates the dynamics of interactions among components due to the component-level responsive behavior. For modeling the responsive internal state changes, we assign an energy landscape and its associated energy rate landscape for each component, leading to the generalized gradient flow model of responsive behavior. Then, we represent interaction dynamics based on temporal changes in these energy and energy rate landscapes by formulating temporal changes in the environmental states of each component due to the responsive behavior of individual components. Through case studies using simplified models of mechanically interacting tissues under morphological changes, our theoretical framework demonstrates that temporal changes in applied forces due to morphological changes of individual tissues determine the self-organization of system functions. These findings highlight that expressing interaction dynamics based on temporal changes in energy and energy rate landscapes offers a powerful theoretical framework for understanding how component-level responsive behavior organizes system functions.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105631"},"PeriodicalIF":1.9,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145370392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1016/j.biosystems.2025.105630
Shouwei Li , Bo Peng , Baochen Li , Yan Shi
This study presents an agent-based model to investigate cooperation dynamics in spatial evolutionary games by integrating memory-based reputation tracking with heterogeneous adaptive learning. Agents interact on a lattice network and update their strategies based on both neighbors’ historical cooperation rates and payoff differences, governed by a modified Fermi rule with individual sensitivity parameters. Simulation results demonstrate that this dual-layered mechanism sustains cooperation even under strong defection incentives and limited interaction ranges. The model also reveals how memory length and learning heterogeneity jointly influence spatial cooperation patterns and strategy diversity. These findings offer new insights into decentralized mechanisms that promote cooperation in structured populations, with implications for evolutionary biology, distributed systems, and behavioral economics.
{"title":"Cooperation in structured populations via coupled reputation and learning: A spatial evolutionary game approach","authors":"Shouwei Li , Bo Peng , Baochen Li , Yan Shi","doi":"10.1016/j.biosystems.2025.105630","DOIUrl":"10.1016/j.biosystems.2025.105630","url":null,"abstract":"<div><div>This study presents an agent-based model to investigate cooperation dynamics in spatial evolutionary games by integrating memory-based reputation tracking with heterogeneous adaptive learning. Agents interact on a lattice network and update their strategies based on both neighbors’ historical cooperation rates and payoff differences, governed by a modified Fermi rule with individual sensitivity parameters. Simulation results demonstrate that this dual-layered mechanism sustains cooperation even under strong defection incentives and limited interaction ranges. The model also reveals how memory length and learning heterogeneity jointly influence spatial cooperation patterns and strategy diversity. These findings offer new insights into decentralized mechanisms that promote cooperation in structured populations, with implications for evolutionary biology, distributed systems, and behavioral economics.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105630"},"PeriodicalIF":1.9,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1016/j.biosystems.2025.105620
Nalini Devi K. , Srinivasa G.
This paper applies the recently introduced Sombor index and its spectral extension, the Sombor energy, to model and analyze the structural complexity of neurotransmitter molecular graphs. Let denote a molecular graph whose vertices and edges correspond to atoms and covalent bonds, respectively. For each , we compute and , and derive degree-based, spectral-radius, and Frobenius-norm bounds to quantify molecular irregularity. Unlike traditional indices such as Zagreb or Wiener, Sombor descriptors incorporate both degree heterogeneity and geometric weighting, offering refined sensitivity to branching and aromaticity. Comparative analysis across inhibitory (glycine, GABA) and excitatory or modulatory (dopamine, serotonin, norepinephrine) neurotransmitters reveals that higher Sombor measures correspond to greater structural and functional complexity. These results confirm that Sombor-based descriptors capture biologically interpretable differences in molecular organization. The study thereby extends spectral graph theory to neurochemical systems, providing a quantitative framework for cheminformatics, drug design, and functional classification of neurotransmitters.
{"title":"Spectral bounds for Sombor and Sombor energy indices: A graph-theoretic study of neurotransmitter molecular networks","authors":"Nalini Devi K. , Srinivasa G.","doi":"10.1016/j.biosystems.2025.105620","DOIUrl":"10.1016/j.biosystems.2025.105620","url":null,"abstract":"<div><div>This paper applies the recently introduced Sombor index and its spectral extension, the Sombor energy, to model and analyze the structural complexity of neurotransmitter molecular graphs. Let <span><math><mi>G</mi></math></span> denote a molecular graph whose vertices and edges correspond to atoms and covalent bonds, respectively. For each <span><math><mi>G</mi></math></span>, we compute <span><math><mrow><mi>S</mi><mi>O</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow></mrow></math></span> and <span><math><mrow><mi>S</mi><mi>O</mi><mi>E</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow></mrow></math></span>, and derive degree-based, spectral-radius, and Frobenius-norm bounds to quantify molecular irregularity. Unlike traditional indices such as Zagreb or Wiener, Sombor descriptors incorporate both degree heterogeneity and geometric weighting, offering refined sensitivity to branching and aromaticity. Comparative analysis across inhibitory (glycine, GABA) and excitatory or modulatory (dopamine, serotonin, norepinephrine) neurotransmitters reveals that higher Sombor measures correspond to greater structural and functional complexity. These results confirm that Sombor-based descriptors capture biologically interpretable differences in molecular organization. The study thereby extends spectral graph theory to neurochemical systems, providing a quantitative framework for cheminformatics, drug design, and functional classification of neurotransmitters.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105620"},"PeriodicalIF":1.9,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-14DOI: 10.1016/j.biosystems.2025.105618
P.G. Tello , S. Kauffman
This work revisits the Maxwell Demon paradigm to explore its implications for evolutionary dynamics from an information-theoretic perspective. By removing the Demon as an intentional agent, we reinterpret the emergence of order as a natural outcome of physical laws combined with stochastic processes. Using models inspired by information theory, such as binary and Z-channels, we show how random fluctuations (e.g., stochastic resonance) can decrease entropy, generate mutual information, and induce non-ergodicity. These dynamics highlight the role of memory and correlation as emergent features of purely physical interactions without recourse to purposeful agency. In this framework, evolutionary exaptations, rather than sole adaptations, emerge as key drivers of biological evolution. Finally, we connect our analysis with recent contributions on agency and memory, underscoring the relevance of informational concepts for understanding the purposeless yet structured dynamics of evolutionary processes.
{"title":"When Maxwell’s Demon leaves the room","authors":"P.G. Tello , S. Kauffman","doi":"10.1016/j.biosystems.2025.105618","DOIUrl":"10.1016/j.biosystems.2025.105618","url":null,"abstract":"<div><div>This work revisits the Maxwell Demon paradigm to explore its implications for evolutionary dynamics from an information-theoretic perspective. By removing the Demon as an intentional agent, we reinterpret the emergence of order as a natural outcome of physical laws combined with stochastic processes. Using models inspired by information theory, such as binary and Z-channels, we show how random fluctuations (e.g., stochastic resonance) can decrease entropy, generate mutual information, and induce non-ergodicity. These dynamics highlight the role of memory and correlation as emergent features of purely physical interactions without recourse to purposeful agency. In this framework, evolutionary exaptations, rather than sole adaptations, emerge as key drivers of biological evolution. Finally, we connect our analysis with recent contributions on agency and memory, underscoring the relevance of informational concepts for understanding the purposeless yet structured dynamics of evolutionary processes.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105618"},"PeriodicalIF":1.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1016/j.biosystems.2025.105615
Luca Zamboni
I present a model of infectious disease transmission with asymptomatic carriers, social distancing, and diagnostic testing. First, I study the impact of asymptomatic carriers on the spread of an infectious disease in the absence of testing, to determine when their presence increases the overall prevalence of symptomatic infection and hence unhealthy agents. Then, I consider mass testing and isolation policies to identify and isolate asymptomatic carriers, and incorporate them into my model. I establish that diagnostic testing successfully reduces steady state disease prevalence. I then explore the implications of testing accuracy, explicitly studying the impact of false positive and false negative test results. I find that reducing the rate of false negatives is unambiguously beneficial, since it improves the identification and isolation of asymptomatic carriers. In contrast, reducing the rate of false positives can be detrimental: by limiting the unintended isolation of susceptible individuals, lower rates of false positives reduce the overall level of social distancing in the population and increase disease spread. Hence, I demonstrate how, under certain conditions, false positive results can improve social welfare.
{"title":"The threat of asymptomatic carriers and the benefits of testing","authors":"Luca Zamboni","doi":"10.1016/j.biosystems.2025.105615","DOIUrl":"10.1016/j.biosystems.2025.105615","url":null,"abstract":"<div><div>I present a model of infectious disease transmission with asymptomatic carriers, social distancing, and diagnostic testing. First, I study the impact of asymptomatic carriers on the spread of an infectious disease in the absence of testing, to determine when their presence increases the overall prevalence of symptomatic infection and hence unhealthy agents. Then, I consider mass testing and isolation policies to identify and isolate asymptomatic carriers, and incorporate them into my model. I establish that diagnostic testing successfully reduces steady state disease prevalence. I then explore the implications of testing accuracy, explicitly studying the impact of false positive and false negative test results. I find that reducing the rate of false negatives is unambiguously beneficial, since it improves the identification and isolation of asymptomatic carriers. In contrast, reducing the rate of false positives can be detrimental: by limiting the unintended isolation of susceptible individuals, lower rates of false positives reduce the overall level of social distancing in the population and increase disease spread. Hence, I demonstrate how, under certain conditions, false positive results can improve social welfare.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105615"},"PeriodicalIF":1.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145304079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-11DOI: 10.1016/j.biosystems.2025.105610
Kevin Hudnall
We extend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall and D’Souza, 2025). That work showed that time itself is multifractal – not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall and D’Souza, 2025). That work showed that time itself is multifractal – not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permits measurement, but grounds it more rigorously in the structure of biology. The tree of life is modeled as the outcome of stochastic, convex branching, and we show how information-theoretic and fractal measures render its multifractal geometry into measurable, observer-relative time intervals. At the core is a dilation equation that expresses relative time elapse between entities as dimensionless ratios. Operational standards such as the SI second remain valid, but our framework makes explicit their lineage-dependence. This framework unifies measurement theory with biological form, preserves full compatibility with established science, and provides a biologically grounded theory of observation. It enables comparative analyses of duration and kinematics across lineages, with predictions that are directly open to experimental validation.
{"title":"Information and the living tree of life: A theory of measurement grounded in biology","authors":"Kevin Hudnall","doi":"10.1016/j.biosystems.2025.105610","DOIUrl":"10.1016/j.biosystems.2025.105610","url":null,"abstract":"<div><div>We extend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall and D’Souza, 2025). That work showed that time itself is multifractal – not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permend a formal framework that previously derived time from the multifractal structure of biological lineages (Hudnall and D’Souza, 2025). That work showed that time itself is multifractal – not a universal background dimension, but an observer-dependent geometry. Here we develop the corresponding theory of measurement: showing that a multifractal conception of time not only permits measurement, but grounds it more rigorously in the structure of biology. The tree of life is modeled as the outcome of stochastic, convex branching, and we show how information-theoretic and fractal measures render its multifractal geometry into measurable, observer-relative time intervals. At the core is a dilation equation that expresses relative time elapse between entities as dimensionless ratios. Operational standards such as the SI second remain valid, but our framework makes explicit their lineage-dependence. This framework unifies measurement theory with biological form, preserves full compatibility with established science, and provides a biologically grounded theory of observation. It enables comparative analyses of duration and kinematics across lineages, with predictions that are directly open to experimental validation.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105610"},"PeriodicalIF":1.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-11DOI: 10.1016/j.biosystems.2025.105608
Ian Todd
Karl Popper’s falsifiability criterion assumes that scientific hypotheses can be reduced to binary tests. We show this assumption is scale-dependent and can saturate in high-dimensional biological systems operating near physical measurement limits, especially near criticality. In neural networks, much relevant information exists as patterns below the Landauer threshold for irreversible bit recording—signals too weak for individual neurons to detect but detectable when pooled across populations. These sub-threshold patterns cannot be projected into binary outcomes without destroying their causal structure. We develop a framework connecting dimensionality, thermodynamic measurement limits, and biological epistemology, showing that Popperian logic represents a special case applicable only to low-dimensional systems with strong signals. Our analysis has implications for neuroscience, where aspects of conscious processing may in part depend on sub-threshold coherence patterns that resist binary measurement, motivating a shift from single-case hypothesis tests to multi-scale, ensemble-based inference. The framework extends to other complex biological systems including ecological networks, protein folding dynamics, and evolutionary processes where causal relationships exist as irreducible multi-dimensional structures operating below classical measurement thresholds.
{"title":"The limits of falsifiability: Dimensionality, measurement thresholds, and the sub-Landauer domain in biological systems","authors":"Ian Todd","doi":"10.1016/j.biosystems.2025.105608","DOIUrl":"10.1016/j.biosystems.2025.105608","url":null,"abstract":"<div><div>Karl Popper’s falsifiability criterion assumes that scientific hypotheses can be reduced to binary tests. We show this assumption is <em>scale-dependent</em> and can <em>saturate</em> in high-dimensional biological systems operating near physical measurement limits, especially near criticality. In neural networks, much relevant information exists as patterns below the Landauer threshold for irreversible bit recording—signals too weak for individual neurons to detect but detectable when pooled across populations. These sub-threshold patterns cannot be projected into binary outcomes without destroying their causal structure. We develop a framework connecting dimensionality, thermodynamic measurement limits, and biological epistemology, showing that Popperian logic represents a special case applicable only to low-dimensional systems with strong signals. Our analysis has implications for neuroscience, where aspects of conscious processing may in part depend on sub-threshold coherence patterns that resist binary measurement, motivating a shift from single-case hypothesis tests to multi-scale, ensemble-based inference. The framework extends to other complex biological systems including ecological networks, protein folding dynamics, and evolutionary processes where causal relationships exist as irreducible multi-dimensional structures operating below classical measurement thresholds.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"258 ","pages":"Article 105608"},"PeriodicalIF":1.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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