arXiv subjects

Stuart A. Kauffman

Publications and source records attributed to Stuart A. Kauffman.

At least 19 recordsLinked to original sources

arXiv2026

Turning Galaxy Rotation Curves into Radial Cosmic Chronometers: A Nexus Paradigm Approach

We present a method for transforming galaxy rotation curves into radially resolved dynamical chronometers, enabling reconstruction of galaxy assembly histories directly from kinematic data. Within the Nexus Paradigm, the baryonic Tully-Fisher relation provides an estimate of the dynamical mass profile $ M_{dyn}(r)=v^4/Ga_0$, where $a_0=H_0/2\pi $.By Comparing this with independently derived intrinsic baryonic mass profiles, $M_{int}(r)$, obtained from stellar S\'ersic fits and gas surface density measurements, we construct the ratio $ M_{dyn}(r)/M_{int}(r)$, which maps directly to a formation redshift via $ 1+z_{form}(r)=(M_{dyn}/M_{int})^{1/4}$. Inverting this relation with$\Lambda CDM$ cosmology yields a radial lookback-time profile, $t_{lb}(r)$, representing the time since the last dynamical reconfiguration at each radius. Applying this framework to a pilot sample of SPARC galaxies spanning high-and low -surface-brightness systems, together with the Milky Way, we recover diverse radial age structures, including flat profiles consistent with coherent disk assembly and stratified profiles indicative of inside-out growth. The method operates without dark-matter halo fitting and provides a kinematic chronometer complementary to stellar-population and chemical-evolution approaches. While the inferred ages depend on the accuracy of baryonic mass reconstruction and local applicability of the evolving baryonic Tully-Fisher relation, the results demonstrate that galaxy rotation curves encode time-resolved dynamical information. This establishes the radial dynamical chronometer as a new observable for probing galaxy evolution and testing gravitational frameworks.

astro-ph.GA↗

arXiv2025

Life Finds A Way: Emergence of Cooperative Structures in Adaptive Threshold Networks

There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution of life. Using a simple model, we investigate how varying bias toward cooperation versus antagonism shapes network dynamics, revealing that higher-order organization emerges even amid pervasive antagonistic interactions. In general, we observe that a quantitative increase in the number of elements in a system leads to a qualitative transition. We present a random threshold-directed network model that integrates node-specific traits with dynamic edge formation and node removal, simulating arbitrary levels of cooperation and competition. In our framework, intrinsic node values determine directed links through various threshold rules. Our model generates a multi-digraph with signed edges (reflecting support/antagonism, labeled ``help''/``harm''), which ultimately yields two parallel yet interdependent threshold graphs. Incorporating temporal growth and node turnover in our approach allows exploration of the evolution, adaptation, and potential collapse of communities and reveals regime changes in both connectivity and resilience. Our findings extend classical random threshold and Erd\H{o}s-R\'enyi models, offering new insights into adaptive systems in biological and economic contexts, with emphasis on the application to Collective Affordance Sets. This framework should also be useful for making predictions that will be tested by ongoing experiments of microbial communities in soil.

q-bio.PE↗

arXiv2024

Biocosmology: Towards the birth of a new science

Cosmologists wish to explain how our Universe, in all its complexity, could ever have come about. For that, we assess the number of states in our Universe now. This plays the role of entropy in thermodynamics of the Universe, and reveals the magnitude of the problem of initial conditions to be solved. The usual budgeting accounts for gravity, thermal motions, and finally the vacuum energy whose entropy, given by the Bekenstein bound, dominates the entropy budget today. There is however one number which we have not accounted for: the number of states in our complex biosphere. What is the entropy of life and is it sizeable enough to need to be accounted for at the Big Bang? Building on emerging ideas within theoretical biology, we show that the configuration space of living systems, unlike that of their fundamental physics counterparts, can grow rapidly in response to emerging biological complexity. A model for this expansion is provided through combinatorial innovation by the Theory of the Adjacent Possible (TAP) and its corresponding TAP equation, whose solutions we investigate, confirming the possibility of rapid state-pace growth. While the results of this work remain far from being firmly established, the evidence we provide is many-fold and strong. The implications are far-reaching, and open a variety of lines for future investigation, a new scientific field we term biocosmology. In particular the relationship between the information content in life and the information content in the Universe may need to be rebuilt from scratch.

astro-ph.CO↗

arXiv2022

Beyond the Newtonian Paradigm: A Statistical Mechanics of Emergence

Since Newton, all classical and quantum physics depends upon the "Newtonian Paradigm". Here the relevant variables of the system are identified. The boundary conditions creating the phase space of all possible values of the variables are defined. Then, given any initial condition, the differential equations of motion are integrated to yield an entailed trajectory in the phase space. It is fundamental to the Newtonian Paradigm that the set of possibilities that constitute the phase space is always definable and fixed ahead of time. All of this fails for the diachronic evolution of ever new adaptations in any biosphere. The central reason is that living cells achieve Constraint Closure and construct themselves. Living cells, evolving via heritable variation and Natural selection, adaptively construct new in the universe possibilities. The new possibilities are opportunities for new adaptations thereafter seized by heritable variation and Natural Selection. Surprisingly, we can neither define nor deduce the evolving phase spaces ahead of time. We can use no mathematics based on Set Theory to do so. These ever-new adaptations with ever-new relevant variables constitute the ever-changing phase space of evolving biospheres. Because of this, evolving biospheres are entirely outside the Newtonian Paradigm. One consequence is that for any universe such as ours there can be no Final Theory that entails all that comes to exist. The implications are large. We face a third major transition in science beyond the Pythagorean dream that "All is Number". We must give up deducing the diachronic evolution of the biosphere. All of physics, classical and quantum, however, apply to the analysis of existing life, a synchronic analysis. We begin to better understand the emergent creativity of an evolving biosphere. Thus, we are on the edge of inventing a physics-like new statistical mechanics of emergence.

physics.soc-ph↗

arXiv2022

What Is Consciousness? Artificial Intelligence, Real Intelligence, Quantum Mind, And Qualia

We approach the question "What is Consciousness?" in a new way, not as Descartes' "systematic doubt", but as how organisms find their way in their world. Finding one's way involves finding possible uses of features of the world that might be beneficial or avoiding those that might be harmful. "Possible uses of X to accomplish Y" are "Affordances". The number of uses of X is indefinite (or unknown), the different uses are unordered, are not listable, and are not deducible from one another. All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by the organism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: (1) Artificial general intelligence based on universal Turing machines (UTMs) is not possible, since UTMs cannot "find" novel affordances. (2) Brain-mind is not purely classical physics for no classical physics system can be an analogue computer whose dynamical behaviour can be isomorphic to "possible uses". (3) Brain mind must be partly quantum-supported by increasing evidence at 6.0 sigma to 7.3 sigma. (4) Based on Heisenberg's interpretation of the quantum state as "potentia" converted to "actuals" by measurement, where this interpretation is not a substance dualism, a natural hypothesis is that mind actualizes potentia. This is supported at 5.2 sigma. Then mind's actualizations of entangled brain-mind-world states are experienced as qualia and allow "seeing" or "perceiving" of uses of X to accomplish Y. We can and do jury-rig. Computers cannot. (5) Beyond familiar quantum computers, we discuss the potentialities of trans-Turing-systems.

physics.soc-ph↗

arXiv2022

The Hiatus Between Organism and Machine Evolution: Contrasting Mixed Microbial Communities with Robots

Mixed microbial communities, usually composed of various bacterial and fungal species, are fundamental in a plethora of environments, from soil to human gut and skin. Their evolution is a paradigmatic example of intertwined dynamics, where not just the relations among species plays a role, but also the opportunities -- and possible harms -- that each species presents to the others. These opportunities are in fact \textit{affordances}, which can be seized by heritable variation and selection. In this paper, starting from a systemic viewpoint of mixed microbial communities, we focus on the pivotal role of affordances in evolution and we contrast it to the artificial evolution of programs and robots. We maintain that the two realms are neatly separated, in that natural evolution proceeds by extending the space of its possibilities in a completely open way, while the latter is inherently limited by the algorithmic framework it is defined. This discrepancy characterises also an envisioned setting in which robots evolve in the physical world. We present arguments supporting our claim and we propose an experimental setting for assessing our statements. Rather than just discussing the limitations of the artificial evolution of machines, the aim of this contribution is to emphasize the tremendous potential of the evolution of the biosphere, beautifully represented by the evolution of communities of microbes.

q-bio.PE↗

arXiv2022

Biocosmology: Biology from a cosmological perspective

The Universe contains everything that exists, including life. And all that exists, including life, obeys universal physical laws. Do those laws then give adequate foundations for a complete explanation of biological phenomena? We discuss whether and how cosmology and physics must be modified to be able to address certain questions which arise at their intersection with biology. We show that a universe that contains life, in the form it has on Earth, is in a certain sense radically non-ergodic, in that the vast majority of possible organisms will never be realized. We argue from this that complete explanations in cosmology require a mixture of reductionist and functional explanations.

physics.hist-ph↗

arXiv2022

The TAP equation: evaluating combinatorial innovation in Biocosmology

We investigate solutions to the TAP equation, a phenomenological implementation of the Theory of the Adjacent Possible. Several implementations of TAP are studied, with potential applications in a range of topics including economics, social sciences, environmental change, evolutionary biological systems, and the nature of physical laws. The generic behaviour is an extended plateau followed by a sharp explosive divergence. We find accurate analytic approximations for the blow-up time that we validate against numerical simulations, and explore the properties of the equation in the vicinity of equilibrium between innovation and extinction. A particular variant, the two-scale TAP model, replaces the initial plateau with a phase of exponential growth, a widening of the TAP equation phenomenology that may enable it to be applied in a wider range of contexts.

q-bio.PE↗

arXiv2021

The world is not a theorem

The evolution of the biosphere unfolds as a luxuriant generative process of new living forms and functions. Organisms adapt to their environment, exploit novel opportunities that are created in this continuous blooming dynamics. Affordances play a fundamental role in the evolution of the biosphere, for organisms can exploit them for new morphological and behavioral adaptations achieved by heritable variations and selection. This way, the opportunities offered by affordances are then actualized as ever novel adaptations. In this paper we maintain that affordances elude a formalization that relies on set theory: we argue that it is not possible to apply set theory to affordances, therefore we cannot devise a set-based mathematical theory of the diachronic evolution of the biosphere.

physics.bio-ph↗

arXiv2020

Online adaptation in robots as biological development provides phenotypic plasticity

The ability of responding to environmental stimuli with appropriate actions is a property shared by all living organisms, and it is also sought in the design of robotic systems. Phenotypic plasticity provides a way for achieving this property as it characterises those organisms that, from one genotype, can express different phenotypes in response to different environments, without involving genetic modifications. In this work we study phenotypic plasticity in robots that are equipped with online sensor adaptation. We show that Boolean network controlled robots can attain navigation with collision avoidance by adapting the coupling between proximity sensors and their controlling network without changing its structure. In other terms, these robots, while being characterised by one genotype (i.e. the network) can express a phenotype among many that is suited for the specific environment. We also show that the dynamical regime that makes it possible to attain the best overall performance is the critical one, bringing further evidence to the hypothesis that natural and artificial systems capable of optimally balancing robustness and adaptivity are critical.

cs.RO↗

arXiv2019

Theory of chemical evolution of molecule compositions in the universe, in the Miller-Urey experiment and the mass distribution of interstellar and intergalactic molecules

Chemical evolution is essential in understanding the origins of life. We present a theory for the evolution of molecule masses and show that small molecules grow by random diffusion and large molecules by a preferential attachment process leading eventually to life's molecules. It reproduces correctly the distribution of molecules found via mass spectroscopy for the Murchison meteorite and estimates the start of chemical evolution back to 12.8 billion years following the birth of stars and supernovae. From the Frontier mass between the random and preferential attachment dynamics the birth time of molecule families can be estimated. Amino acids emerge about 165 million years after chemical elements emerge in stars. Using the scaling of reaction rates with the distance of the molecules in space we recover correctly the few days emergence time of amino acids in the Miller-Urey experiment. The distribution of interstellar and extragalactic molecules are both consistent with the evolutionary mass distribution, and their age is estimated to 108 and 65 million years after the start of evolution. From the model, we can determine the number of different molecule compositions at the time of the emergence of Earth to be 1.6 million and the number of molecule compositions in interstellar space to a mere 719 species.

q-bio.PE↗

arXiv2019

Dynamics of a birth-death process based on combinatorial innovation

A feature of human creativity is the ability to take a subset of existing items (e.g. objects, ideas, or techniques) and combine them in various ways to give rise to new items, which, in turn, fuel further growth. Occasionally, some of these items may also disappear (extinction). We model this process by a simple stochastic birth--death model, with non-linear combinatorial terms in the growth coefficients to capture the propensity of subsets of items to give rise to new items. In its simplest form, this model involves just two parameters $(P, α)$. This process exhibits a characteristic 'hockey-stick' behaviour: a long period of relatively little growth followed by a relatively sudden 'explosive' increase. We provide exact expressions for the mean and variance of this time to explosion and compare the results with simulations. We then generalise our results to allow for more general parameter assignments, and consider possible applications to data involving human productivity and creativity.

q-bio.PE↗

arXiv2018

Logic and connectivity jointly determine criticality in biological gene regulatory networks

The complex dynamics of gene expression in living cells can be well-approximated using Boolean networks. The average sensitivity is a natural measure of stability in these systems: values below one indicate typically stable dynamics associated with an ordered phase, whereas values above one indicate chaotic dynamics. This yields a theoretically motivated adaptive advantage to being near the critical value of one, at the boundary between order and chaos. Here, we measure average sensitivity for 66 publicly available Boolean network models describing the function of gene regulatory circuits across diverse living processes. We find the average sensitivity values for these networks are clustered around unity, indicating they are near critical. In many types of random networks, mean connectivity and the average activity bias of the logic functions have been found to be the most important network properties in determining average sensitivity, and by extension a network's criticality. Surprisingly, many of these gene regulatory networks achieve the near-critical state with and far from that predicted for critical systems: randomized networks sharing the local causal structure and local logic of biological networks better reproduce their critical behavior than controlling for macroscale properties such as and alone. This suggests the local properties of genes interacting within regulatory networks are selected to collectively be near-critical, and this non-local property of gene regulatory network dynamics cannot be predicted using the density of interactions alone.

q-bio.MN↗

arXiv2013

Evolutionary Design in Biological Quantum Computing

The unique capability of quantum mechanics to evolve alternative possibilities in parallel is appealing and over the years a number of quantum algorithms have been developed offering great computational benefits. Systems coupled to the environment lose quantum coherence quickly and realization of schemes based on unitarity might be impossible. Recent discovery of room temperature quantum coherence in light harvesting complexes opens up new possibilities to borrow concepts from biology to use quantum effects for computational purposes. While it has been conjectured that light harvesting complexes such as the Fenna-Matthews-Olson (FMO) complex in the green sulfur bacteria performs an efficient quantum search similar to the quantum Grover's algorithm the analogy has yet to be established. In this work we show that quantum dissipation plays an essential role in the quantum search performed in the FMO complex and it is fundamentally different from known algorithms. In the FMO complex not just the optimal level of phase breaking is present to avoid both quantum localization and Zeno trapping but it can harness quantum dissipation as well to speed the process even further up. With detailed quantum calculations taking into account both phase breaking and quantum dissipation we show that the design of the FMO complex has been evolutionarily optimized and works faster than pure quantum or classical-stochastic algorithms. Inspired by the findings we introduce a new computational concept based on decoherent quantum evolution. While it is inspired by light harvesting systems, the new computational devices can also be realized on different material basis opening new magnitude scales for miniaturization and speed.

cond-mat.dis-nn↗

arXiv2012

Growth efficiency as a cellular objective in Eschericia coli

The identification of cellular objectives is one of the central topics in the research of microbial metabolic networks. In particular, the information about a cellular objective is needed in flux balance analysis which is a commonly used constrained-based metabolic network analysis method for the prediction of cellular phenotypes. The cellular objective may vary depending on the organism and its growth conditions. It is probable that nutritionally scarce conditions are very common in the nature and, in order to survive in those conditions, cells exhibit various highly efficient nutrient processing systems like enzymes. In this study, we explore the efficiency of a metabolic network in transformation of substrates to new biomass, and we introduce a new objective function simulating growth efficiency. We examined the properties of growth efficiency using a metabolic model for Eschericia coli. We found that the maximal growth efficiency is obtained at a finite nutrient uptake rate. The rate is substrate-dependent and it typically does not exceed 20 mmol/h/gDW. We further examined whether the maximal growth efficiency could serve as a cellular objective function in metabolic network analysis, and found that cellular growth in batch cultivation can be predicted reasonably well under this assumption. The fit to experimental data was found slightly better than with the commonly used objective function of maximal growth rate. Based on our results, we suggest that the maximal growth efficiency can be considered as a plausible optimization criterion in metabolic modeling for E. coli. In the future, it would be interesting to study growth efficiency as a cellular objective also in other cellular systems and under different cultivation conditions.

q-bio.MN↗

arXiv2009

A Model of Sequential Branching in Hierarchical Cell Fate Determination

Multipotent stem or progenitor cells undergo a sequential series of binary fate decisions, which ultimately generate the diversity of differentiated cells. Efforts to understand cell fate control have focused on simple gene regulatory circuits that predict the presence of multiple stable states, bifurcations and switch-like transitions. However, existing gene network models do not explain more complex properties of cell fate dynamics such as the hierarchical branching of developmental paths. Here, we construct a generic minimal model of the genetic regulatory network controlling cell fate determination, which exhibits five elementary characteristics of cell differentiation: stability, directionality, branching, exclusivity, and promiscuous expression. We argue that a modular architecture comprising repeated network elements reproduces these features of differentiation by sequentially repressing selected modules and hence restricting the dynamics to lower dimensional subspaces of the high-dimensional state space. We implement our model both with ordinary differential equations (ODEs), to explore the role of bifurcations in producing the one-way character of differentiation, and with stochastic differential equations (SDEs), to demonstrate the effect of noise on the system. We further argue that binary cell fate decisions are prevalent in cell differentiation due to general features of the underlying dynamical system. This minimal model makes testable predictions about the structural basis for directional, discrete and diversifying cell phenotype development and thus can guide the evaluation of real gene regulatory networks that govern differentiation.

q-bio.MN↗

arXiv2008

Maximum Power Efficiency and Criticality in Random Boolean Networks

Random Boolean networks are models of disordered causal systems that can occur in cells and the biosphere. These are open thermodynamic systems exhibiting a flow of energy that is dissipated at a finite rate. Life does work to acquire more energy, then uses the available energy it has gained to perform more work. It is plausible that natural selection has optimized many biological systems for power efficiency: useful power generated per unit fuel. In this letter we begin to investigate these questions for random Boolean networks using Landauer's erasure principle, which defines a minimum entropy cost for bit erasure. We show that critical Boolean networks maximize available power efficiency, which requires that the system have a finite displacement from equilibrium. Our initial results may extend to more realistic models for cells and ecosystems.

q-bio.OT↗

arXiv2008

Critical Networks Exhibit Maximal Information Diversity in Structure-Dynamics Relationships

Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and how that these relationships are most diverse in the critical regime.

cond-mat.dis-nn↗