Pith. sign in

REVIEW 3 major objections 1 minor 106 references

Heatomics

T0 review · 3 major / 1 minor · reviewed 2026-06-29 · grok-4.3

Pith's one-line read The conserved heat dissipation rate of one watt per kilogram in living matter sets the scale for negentropy generation that sustains biological organization.

desk verdict Speculative note on conserved cellular power density and a cosmic coincidence, but the central hypothesis has no derivation or supporting framework. read the letter →

arxiv 2605.28720 v1 pith:7GH5QCJM submitted 2026-05-27 physics.bio-ph cond-mat.soft

classification physics.bio-phcond-mat.soft
keywords heatomicsentropyproductionnegentropypowerdensitynonequilibriumsteadystatevariancesumrulebiologicalorganizationlivingcells
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Living cells maintain nonequilibrium steady states by continuously dissipating heat, with the entropy production rate serving as a universal signal of life. Across scales, this dissipation occurs at a conserved power density of approximately one watt per kilogram. The paper hypothesizes that this value determines the amount of negentropy that can be generated, enabling the organization that separates living systems from inanimate matter. It proposes heatomics as the field to study these processes at cellular and molecular levels and introduces the Variance Sum Rule to measure entropy production from fluctuations.

What carries the argument

The Variance Sum Rule, an experimental-theoretical framework that extracts the entropy production rate from fluctuations combined with the nonequilibrium equation of state.

What would settle it

A survey of entropy production rates across diverse living organisms revealing values that deviate substantially from one watt per kilogram would challenge the scale-setting hypothesis.

Watch

Extended reading notes

Core claim

Living matter dissipates energy at P_life approximately one watt per kilogram, a value ten thousand times larger than the Sun and equal to the universe's average power density defined by c squared times the Hubble constant. This conserved dissipation is hypothesized to set the scale for generating negentropy, providing the negative contribution to overall positive entropy production that sustains biological organization and distinguishes animate from inanimate matter. The Variance Sum Rule offers a way to extract the entropy production rate from fluctuations of a dynamical probe together with the equation of state for a nonequilibrium steady state.

Load-bearing premise

That the observed conserved power density of one watt per kilogram is what fundamentally sets the scale for negentropy generation in living systems.

Editorial extensions

If this is right

  • If the hypothesis holds, the entropy production rate in all living systems should scale with a power density of one watt per kilogram.
  • Negentropy generation in biology would then be directly tied to this universal power density rather than varying freely.
  • Heatomics would provide quantitative tools to optimize energy use and organization in living systems.
  • The coincidence with cosmic power density would suggest a fundamental link between biological and cosmological scales.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If true, synthetic systems could be designed to operate at this power density to test for emergent organization.
  • Measurements in non-living dissipative systems could clarify whether the value is unique to life or arises in other nonequilibrium contexts.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The manuscript hypothesizes that the conserved metabolic power density P_life ~1 W/kg in living systems sets the scale for negentropy generation (the negative contribution to total positive entropy production σ), thereby distinguishing animate from inanimate matter. It notes the numerical coincidence of P_life with the cosmic average P_U = c² H_0 ~1 W/kg and introduces 'heatomics' (the study of σ at cellular/molecular scales) together with a 'Variance Sum Rule' as an experimental-theoretical method to extract σ from fluctuations of a dynamical probe combined with the NESS equation of state.

Significance. If the central hypothesis were independently derived and validated, it would offer a thermodynamic link between biological organization and cosmological scales, extending Dirac's large-number ideas into nonequilibrium thermodynamics of life. The manuscript correctly identifies the striking constancy of P_life across scales and its comparison to P_U as a potentially deep observation, but presents the connection to negentropy purely as an untested hypothesis without derivations, models, or data.

major comments (3)
  1. [Abstract] Abstract: the claim that P_life 'sets the scale for generating negentropy' is presented without any equation, scaling relation, or derivation showing how the observed power density enters the entropy-production budget as the controlling parameter for the negative (negentropy) term rather than emerging as a byproduct of molecular kinetics or stoichiometry.
  2. [Abstract] Abstract: the Variance Sum Rule is introduced as the framework that 'extracts σ from fluctuations... combined with the equation of state for a NESS,' yet no explicit statement of the rule, its derivation, or demonstration that it requires or implies the P_life-negentropy link is supplied.
  3. [Abstract] Abstract: the hypothesis is motivated by the numerical match between the observed P_life and the independently calculated P_U, but no dynamical relation or falsifiable prediction is given that would elevate the match above coincidence or byproduct status.
minor comments (1)
  1. [Abstract] The abstract introduces two new terms ('heatomics' and 'Variance Sum Rule') without indicating whether they are defined later in the manuscript or are entirely novel constructs.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful reading and for highlighting the distinction between hypothesis and derivation. We respond point by point to the major comments. The manuscript is framed as a conceptual introduction to heatomics and the Variance Sum Rule, with the P_life–negentropy link presented explicitly as a hypothesis rather than a derived result.

read point-by-point responses
  1. Referee: [Abstract] Abstract: the claim that P_life 'sets the scale for generating negentropy' is presented without any equation, scaling relation, or derivation showing how the observed power density enters the entropy-production budget as the controlling parameter for the negative (negentropy) term rather than emerging as a byproduct of molecular kinetics or stoichiometry.

    Authors: The manuscript states the connection as a hypothesis motivated by the empirical constancy of P_life across scales and its numerical coincidence with P_U. No derivation is supplied because the work is intended to define the new field of heatomics and to propose the Variance Sum Rule as a future measurement tool that could test the hypothesis. We will revise the abstract to state more explicitly that the link is conjectural and not derived in the present manuscript. revision: partial

  2. Referee: [Abstract] Abstract: the Variance Sum Rule is introduced as the framework that 'extracts σ from fluctuations... combined with the equation of state for a NESS,' yet no explicit statement of the rule, its derivation, or demonstration that it requires or implies the P_life-negentropy link is supplied.

    Authors: The main text outlines the Variance Sum Rule via fluctuation relations applied to a dynamical probe in a NESS, but we agree that an explicit formula and short derivation would improve clarity. Because the manuscript is a perspective introducing the framework rather than a full technical derivation, the rule is described conceptually. We will add a concise mathematical statement of the rule to the revised abstract and main text. revision: yes

  3. Referee: [Abstract] Abstract: the hypothesis is motivated by the numerical match between the observed P_life and the independently calculated P_U, but no dynamical relation or falsifiable prediction is given that would elevate the match above coincidence or byproduct status.

    Authors: We acknowledge that no dynamical model or explicit falsifiable prediction is provided. The hypothesis is offered as an observation-based conjecture in the spirit of Dirac’s large-number ideas, with the Variance Sum Rule positioned as the experimental route to future tests (e.g., comparing measured σ in living versus non-living systems at matched power densities). We will add a short paragraph outlining possible experimental tests in the revised manuscript. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; hypothesis stated without claimed derivation or self-referential reduction

full rationale

The paper reports the empirical observation that living systems dissipate P_life ~1 W/kg (a conserved value) and notes its numerical coincidence with the independent cosmological quantity P_U = c² H_0. It then states a hypothesis that this P_life 'sets the scale for generating negentropy' without supplying any equation, scaling relation, or model that derives the negentropy term from P_life or reduces the claimed connection to the inputs by construction. The Variance Sum Rule is introduced as a new extraction method for σ but is not shown to be fitted to or defined in terms of the target negentropy scale. No self-citation chain or ansatz smuggling is present in the load-bearing steps. The central claim is therefore an open hypothesis rather than a derivation that collapses to its own premises.

Assumptions & free parameters 1 free parameters · 2 assumptions · 2 invented entities

The central hypothesis depends on the observed P_life value as a scale-setting parameter and introduces new named entities without independent evidence or derivation.

free parameters (1)
  • P_life ~1 W/kg = ~1 W/kg
    Observed conserved power density used as the fundamental scale for the negentropy hypothesis.
assumptions (2)
  • standard math Second law requires positive entropy production σ in nonequilibrium steady states
    Basis for identifying σ as the universal primal life signal.
  • domain assumption NESS admits an equation of state relating fluctuations to thermodynamic quantities
    Required for the Variance Sum Rule to extract σ.
invented entities (2)
  • heatomics
    purpose: New field studying σ at cellular and molecular scales
    Coined term for the proposed research program.
  • Variance Sum Rule
    purpose: Experimental-theoretical framework to extract σ from probe fluctuations plus NESS equation of state
    Newly proposed rule without derivation or validation shown.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Heatomics." pith.science (2026). https://pith.science/paper/7GH5QCJM

@misc{pith2026260528720,
  author       = {Pith},
  title        = {Pith review of: Heatomics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7GH5QCJM}},
  note         = {Machine review of arXiv:2605.28720}
}
abstract

Living cells are energy- and information-processing systems that sustain a nonequilibrium steady state (NESS) by continuously consuming energy and dissipating heat, as required by the second law of thermodynamics. The rate of heat dissipation, or the entropy production rate $\sigma$, is the universal primal life signal and a unique descriptor of the cellular state. Living matter dissipates $P_{\mathrm{life}} \sim 1$ Watt/kilogram (W/kg), a remarkably conserved value across scales, from molecular reactions to entire organisms. Surprisingly, this high power density is $10^{4}$ times larger than that of the Sun and comparable to the universe's average, $P_U = c^2 H_0 \sim 1$ W/kg, where $c$ is the speed of light and $H_0$ the Hubble constant, a striking coincidence that aligns with Dirac's large number hypothesis. We hypothesize that this large $P_{\mathrm{life}}$ sets the scale for generating negentropy, the negative contribution to the overall positive $\sigma$ that sustains biological organization, distinguishing animate from inanimate matter. Here, I introduce heatomics, the science of studying $\sigma$ at the cellular and molecular scales, and the Variance Sum Rule, an experimental--theoretical framework that extracts $\sigma$ from fluctuations of a dynamical probe combined with the equation of state for a NESS. The emerging field of heatomics aims to elucidate the fundamental principles governing heat power generation, optimization of energy resources, and negentropy in living systems.

Figures

Figures reproduced from arXiv: 2605.28720 by the authors.

Figure 1
Figure 1. Comparison of heat power densities between living matter and the sun. other heat producing carriers such as ATP relatives (UTP, GTP,..), phosphate compounds, thioesters (Acetyl-coA), redox carriers (NADH,..) among others. If measuring heat at the nanoscale is already challenging, it is no less than measuring ATP consumption at the single molecule or sub-cellular level. Albeit related, heat production is the universa… view at source ↗
Figure 2
Figure 2. Enzymes classification and heat power density in living matter. (Left) Heat power is produced through metabolism by different types of enzymes. (Right) Heat power density is pretty conserved across scales and organisms. dilute mixture kept at temperature T and pressure p the law of mass action holds and ∆G can be written as ∆G = kBT log(Q(T, p)/Keq(T, p)) with Q the so-called reaction quotient (not to be confused wi… view at source ↗
Figure 3
Figure 3. Heat determination in irreversible processes Rather than using the Clausius inequality Q ≥ T ∆S, which only gives a bound for the heat exchanged with the bath, thermodynamics employs energy conservation and a equation of state to determine the final state and the heat exchanged Q. bound, it is preferable to use the first law of thermodynamics of energy conservation, ∆U = W − Q, plus an equation of state of the syste… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: The Variance Sum Rule (VSR)(Left panel) A mechanical probe captured in an optical trap in contact with a NESS, e.g. a living cell, monitors the membrane flickering time-dependent position xt , while Ft stands for the net force acting on the bead. (Right) Illustration o…
Figure 5
Figure 5. Figure 5: The stochastic switching trap (a) A bead captured in an optical trap jumping between two positions separated by ∆λ = λ+ − λ−. (b) Time-dependent net force Ft for different values of ∆λ (different colors). The spikes represent the instantaneous jumps in trap position th…
Figure 6
Figure 6. Figure 6: The reduced-VSR. (a) The net force Ft acting on the bead is the sum of two opposite contributions, the force exerted by the optical trap and pointing upwards, ftrap(xt) (red) and the active force exerted by the membrane of the living cell fa(t) (blue) pointing downward…
Figure 7
Figure 7. Figure 7: Rigidity dependent probe sensitivity. (Left and middle) Video image of a stretched RBC with the zoom showing the area of contact membrane-bead, ∼ 1µm2 , and flickering traces with low trap stiffness (red and orange) and high trap stiffness (blue). (Right) Dependence of…
Figure 8
Figure 8. Figure 8: Heat power map of a single RBC. (Left) Heat power map over lateral regions of pixel size 50nmx50nm along the equatorial rim of a RBC. (Right) Comparison of heat power measured with bulk calorimetry (upper red bar) [58] single-RBC measurements optical tweezers (OT) by s…
Figure 9
Figure 9. Figure 9: Heatomics. A few illustrative layers of the omics disciplines across biology. We posit that heatomics is a fundamental layer encompassing life processes. nanodiamonds, etc. [67, 68]. In the case of mitochondria, it has been reported that its temper￾ature can even reach…
Figure 10
Figure 10. Figure 10: σ-map of single cells. (Left) Illustration of flickering lifetimes expected for FLIM (fluorescence lifetime imaging microscopy) measurements. Video image of a flibroblast illustrates how such measurement could be done with fluorescent proteins internalized at specific…
Figure 11
Figure 11. Figure 11: The role of hidden Dofs. Illustration of a minimal theoretical framework incorporating one observed and one hidden Dof. The second law imposes σ ≥ 0 while this does not preclude the existence of endothermic hidden Dofs with negentropy σ− that coexist with the large an…
Figure 12
Figure 12. Figure 12: Polymerization produces negentropy. Illustration of an optical trapping assay where a DNA polymerase copies a parental ssDNA into a newborn strand, each nucleotide added contributes to negentropy with kBT log 2 per bit. At equal concentrations of the four nucleotides …
Figure 13
Figure 13. Figure 13: The linear heat flow example. Positive σ+/σ0 (blue) and negentropy σ−/σ0 (orange) contributions versus σ/σ0, c.f. (9). While σ+/σ0 grows with σ/σ0, the negentropy part σ−/σ0 con￾verges to 1, c.f. (11). This indicates that a finite negentropy σ− ∼ σ0 requires a large σ…
Figure 14
Figure 14. Figure 14: Heat power density: from cells to the universe. Despite the 32 orders of magnitude, the universe heat power falls in the range 1W/kg of living matter (here we show the cosmic microwave background and an RBC for illustrative purposes). comparatively low heat power dens…
Figure 15
Figure 15. Figure 15: Double cone paradox. While the cone appears to move from right to left, rising along the edges of the wedge seemingly against gravity (analogous to negentropy), its center of mass is actually lowered (indicated by the dark line visible below the wedge), consistent wit…
Figure 16
Figure 16. Figure 16: Evolutionary scenarios. Hypothetical negentropy in the universe driven by cosmic acceleration (left) and a living human across its life cycle (right). Biological evolution and growing complexity is characterized by an increasing negentropy over evolutionary timescales…
Figure 17
Figure 17. Figure 17: σ-landscapes in biology. Illustration of an evolutionary-developmental Waddington landscape where every cellular state is characterized by a different heat power σ. mitochondria etc. In contrast, the sun produces a large amount of heat non-uniformly, mainly at its cor…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

106 extracted references · 1 canonical work pages

  1. [1]

    Academic press, 2013

    David G Nicholls.Bioenergetics. Academic press, 2013

  2. [2]

    Physical bioenergetics: Energy fluxes, budgets, and constraints in cells.Proceedings of the National Academy of Sciences, 118(26):e2026786118, 2021

    Xingbo Yang, Matthias Heinemann, Jonathon Howard, Greg Huber, Srividya Iyer-Biswas, Guil- laume Le Treut, Michael Lynch, Kristi L Montooth, Daniel J Needleman, Simone Pigolotti, et al. Physical bioenergetics: Energy fluxes, budgets, and constraints in cells.Proceedings of the National Academy of Sciences, 118(26):e2026786118, 2021

  3. [3]

    The energy–speed– accuracy trade-off in sensory adaptation.Nature physics, 8(5):422–428, 2012

    Ganhui Lan, Pablo Sartori, Silke Neumann, Victor Sourjik, and Yuhai Tu. The energy–speed– accuracy trade-off in sensory adaptation.Nature physics, 8(5):422–428, 2012

  4. [4]

    Thermodynamic dissipation con- strains metabolic versatility of unicellular growth.Nature communications, 16(1):8543, 2025

    Tommaso Cossetto, Jonathan Rodenfels, and Pablo Sartori. Thermodynamic dissipation con- strains metabolic versatility of unicellular growth.Nature communications, 16(1):8543, 2025

  5. [5]

    Makarieva, Victor G

    Anastassia M. Makarieva, Victor G. Gorshkov, and Bai-Lian Li. Mean mass-specific metabolic rates are strikingly similar across life’s major domains: evidence for life’s metabolic optimum. Proceedings of the National Academy of Sciences, 105(44):16994–16999, 2008

  6. [6]

    Ballesteros, Vicente J

    Fernando J. Ballesteros, Vicente J. Mart´ ınez, Bartolo Luque, Lucas Lacasa, and Enric Valor. On the thermodynamic origin of metabolic scaling.Scientific Reports, 8(1):1448, 2018

  7. [7]

    Oxford University Press, 2023

    Peter William Atkins, Julio De Paula, and James Keeler.Atkins’ Physical Chemistry. Oxford University Press, 2023. 24

  8. [8]

    Garland Science, 2010

    Ken Dill and Sarina Bromberg.Molecular Driving Forces: Statistical Thermodynamics in Biol- ogy, Chemistry, Physics, and Nanoscience. Garland Science, 2010

Show all 106 references
  1. [9]

    John Wiley & Sons, 2015

    Dilip Kondepudi and Ilya Prigogine.Modern Thermodynamics: From Heat Engines to Dissipa- tive Structures. John Wiley & Sons, 2015

  2. [10]

    Garland Science, 2015

    Ron Milo and Rob Phillips.Cell biology by the numbers. Garland Science, 2015

  3. [11]

    Brain power.Proceedings of the National Academy of Sciences, 118(32):e2107022118, 2021

    Vijay Balasubramanian. Brain power.Proceedings of the National Academy of Sciences, 118(32):e2107022118, 2021

  4. [12]

    Courier Corpo- ration, 2013

    Sybren Ruurds De Groot and Peter Mazur.Non-equilibrium thermodynamics. Courier Corpo- ration, 2013

  5. [13]

    On the origin and the use of fluctuation relations for the entropy.S´ eminaire Poincar´ e, 2:29–62, 2003

    Christian Maes. On the origin and the use of fluctuation relations for the entropy.S´ eminaire Poincar´ e, 2:29–62, 2003

  6. [14]

    Macroscopic stochastic thermodynamics.Reviews of Modern Physics, 97(1):015002, 2025

    Gianmaria Falasco and Massimiliano Esposito. Macroscopic stochastic thermodynamics.Reviews of Modern Physics, 97(1):015002, 2025

  7. [15]

    Nonequilibrium fluctuations in small systems: From physics to biology.Advances in chemical physics, 137:31, 2008

    Felix Ritort. Nonequilibrium fluctuations in small systems: From physics to biology.Advances in chemical physics, 137:31, 2008

  8. [16]

    Stochastic thermodynamics, fluctuation theorems and molecular machines.Reports on progress in physics, 75(12):126001, 2012

    Udo Seifert. Stochastic thermodynamics, fluctuation theorems and molecular machines.Reports on progress in physics, 75(12):126001, 2012

  9. [17]

    Experiments in stochastic thermodynamics: Short history and perspectives

    Sergio Ciliberto. Experiments in stochastic thermodynamics: Short history and perspectives. Physical Review X, 7(2):021051, 2017

  10. [18]

    Princeton Uni- versity Press, 2021

    Luca Peliti and Simone Pigolotti.Stochastic thermodynamics: an introduction. Princeton Uni- versity Press, 2021

  11. [19]

    Thermodynamic uncertainty relation for biomolecular pro- cesses.Physical review letters, 114(15):158101, 2015

    Andre C Barato and Udo Seifert. Thermodynamic uncertainty relation for biomolecular pro- cesses.Physical review letters, 114(15):158101, 2015

  12. [20]

    Thermodynamic uncertainty relations constrain non-equilibrium fluctuations.Nature Physics, 16(1):15–20, 2020

    Jordan M Horowitz and Todd R Gingrich. Thermodynamic uncertainty relations constrain non-equilibrium fluctuations.Nature Physics, 16(1):15–20, 2020

  13. [21]

    Improving thermodynamic bounds using correlations

    Andreas Dechant and Shin-ichi Sasa. Improving thermodynamic bounds using correlations. Physical Review X, 11(4):041061, 2021

  14. [22]

    Continuous-time random walk model for the diffusive motion of helicases.QRB discovery, 6:e26, 2025

    Victor Rodr´ ıguez-Franco, Michelle Marie Spiering, Piero Bianco, Felix Ritort, and Maria Manosas. Continuous-time random walk model for the diffusive motion of helicases.QRB discovery, 6:e26, 2025

  15. [23]

    Hong Qian and Elliot L Elson. Fluorescence correlation spectroscopy with high-order and dual- color correlation to probe nonequilibrium steady states.Proceedings of the National Academy of Sciences, 101(9):2828–2833, 2004

  16. [24]

    Broken detailed balance at mesoscopic scales in active biological systems.Science, 352(6285):604–607, 2016

    Christopher Battle, Chase P Broedersz, Nikta Fakhri, Veikko F Geyer, Jonathon Howard, Christoph F Schmidt, and Fred C MacKintosh. Broken detailed balance at mesoscopic scales in active biological systems.Science, 352(6285):604–607, 2016

  17. [25]

    Hierarchical bounds on entropy production inferred from partial information.Journal of Statistical Mechanics: Theory and Experiment, 2017(9):093210, 2017

    Gili Bisker, Matteo Polettini, Todd R Gingrich, and Jordan M Horowitz. Hierarchical bounds on entropy production inferred from partial information.Journal of Statistical Mechanics: Theory and Experiment, 2017(9):093210, 2017. 25

  18. [26]

    Exact coarse graining preserves entropy production out of equilibrium.Physical Review Letters, 125(11):110601, 2020

    Gianluca Teza and Attilio L Stella. Exact coarse graining preserves entropy production out of equilibrium.Physical Review Letters, 125(11):110601, 2020

  19. [27]

    Quantifying dissipation using fluctuating currents.Nature communications, 10(1):1666, 2019

    Junang Li, Jordan M Horowitz, Todd R Gingrich, and Nikta Fakhri. Quantifying dissipation using fluctuating currents.Nature communications, 10(1):1666, 2019

  20. [28]

    Inferring entropy pro- duction from short experiments.Physical review letters, 124(12):120603, 2020

    Sreekanth K Manikandan, Deepak Gupta, and Supriya Krishnamurthy. Inferring entropy pro- duction from short experiments.Physical review letters, 124(12):120603, 2020

  21. [29]

    Quan- tifying entropy production in active fluctuations of the hair-cell bundle from time irreversibility and uncertainty relations.New Journal of Physics, 23(8):083013, 2021

    ´Edgar Rold´ an, J´ er´ emie Barral, Pascal Martin, Juan MR Parrondo, and Frank J¨ ulicher. Quan- tifying entropy production in active fluctuations of the hair-cell bundle from time irreversibility and uncertainty relations.New Journal of Physics, 23(8):083013, 2021

  22. [30]

    Mathematical, thermodynamical, and experimental necessity for coarse graining empirical densities and currents in continuous space.Physical Review Letters, 129(14):140601, 2022

    Cai Dieball and Aljaˇ z Godec. Mathematical, thermodynamical, and experimental necessity for coarse graining empirical densities and currents in continuous space.Physical Review Letters, 129(14):140601, 2022

  23. [31]

    Thermodynamic cost for precision of general counting observables.Physical Review E, 109(6):064128, 2024

    Patrick Pietzonka and Francesco Coghi. Thermodynamic cost for precision of general counting observables.Physical Review E, 109(6):064128, 2024

  24. [32]

    Entropy production and the arrow of time.New Journal of Physics, 11(7):073008, 2009

    Juan MR Parrondo, C Van den Broeck, and Ryoichi Kawai. Entropy production and the arrow of time.New Journal of Physics, 11(7):073008, 2009

  25. [33]

    ´Edgar Rold´ an and Juan MR Parrondo. Entropy production and kullback-leibler divergence between stationary trajectories of discrete systems.Physical Review E—Statistical, Nonlinear, and Soft Matter Physics, 85(3):031129, 2012

  26. [34]

    Improved bounds on entropy production in living systems

    Dominic J Skinner and J¨ orn Dunkel. Improved bounds on entropy production in living systems. Proceedings of the National Academy of Sciences, 118(18):e2024300118, 2021

  27. [35]

    Verification of the crooks fluctuation theorem and recovery of rna folding free energies.Nature, 437(7056):231–234, 2005

    Delphine Collin, Felix Ritort, Christopher Jarzynski, Steven B Smith, Ignacio Tinoco Jr, and Carlos Bustamante. Verification of the crooks fluctuation theorem and recovery of rna folding free energies.Nature, 437(7056):231–234, 2005

  28. [36]

    Recovery of free energy branches in single molecule experiments.Physical review letters, 102(7):070602, 2009

    Ivan Junier, Alessandro Mossa, Maria Manosas, and Felix Ritort. Recovery of free energy branches in single molecule experiments.Physical review letters, 102(7):070602, 2009

  29. [37]

    Experimental free-energy measurements of kinetic molecular states using fluctuation theorems.Nature Physics, 8(9):688– 694, 2012

    Anna Alemany, Alessandro Mossa, Ivan Junier, and Felix Ritort. Experimental free-energy measurements of kinetic molecular states using fluctuation theorems.Nature Physics, 8(9):688– 694, 2012

  30. [38]

    Experimental measurement of binding energy, selectivity, and allostery using fluctuation theorems.Science, 355(6323):412–415, 2017

    Joan Camunas-Soler, Anna Alemany, and Felix Ritort. Experimental measurement of binding energy, selectivity, and allostery using fluctuation theorems.Science, 355(6323):412–415, 2017

  31. [39]

    Molten globule–like transition state of protein barnase measured with calorimetric force spectroscopy

    Marc Rico-Pasto, Annamaria Zaltron, Sebastian J Davis, Silvia Frutos, and Felix Ritort. Molten globule–like transition state of protein barnase measured with calorimetric force spectroscopy. Proceedings of the National Academy of Sciences, 119(11):e2112382119, 2022

  32. [40]

    Stem–loop formation drives rna fold- ing in mechanical unzipping experiments.Proceedings of the National Academy of Sciences, 119(3):e2025575119, 2022

    Paolo Rissone, Cristiano V Bizarro, and Felix Ritort. Stem–loop formation drives rna fold- ing in mechanical unzipping experiments.Proceedings of the National Academy of Sciences, 119(3):e2025575119, 2022

  33. [41]

    A two-state kinetic model for the unfolding of single molecules by mechanical force.Proceedings of the National Academy of Sciences, 99(21):13544–13548, 2002

    Felix Ritort, Carlos Bustamante, and Ignacio Tinoco Jr. A two-state kinetic model for the unfolding of single molecules by mechanical force.Proceedings of the National Academy of Sciences, 99(21):13544–13548, 2002. 26

  34. [42]

    Bias and error in estimates of equilibrium free- energy differences from nonequilibrium measurements.Proceedings of the National Academy of Sciences, 100(22):12564–12569, 2003

    Jeff Gore, Felix Ritort, and Carlos Bustamante. Bias and error in estimates of equilibrium free- energy differences from nonequilibrium measurements.Proceedings of the National Academy of Sciences, 100(22):12564–12569, 2003

  35. [43]

    Nano-calorimetry of small-sized biological samples.Thermochimica acta, 477(1- 2):48–53, 2008

    J Lerchner, A Wolf, H-J Schneider, F Mertens, E Kessler, V Baier, A Funfak, M Nietzsch, and M Kr¨ ugel. Nano-calorimetry of small-sized biological samples.Thermochimica acta, 477(1- 2):48–53, 2008

  36. [44]

    A sensitive calorimetric technique to study energy (heat) exchange at the nano-scale.Nanoscale, 10(21):10079–10086, 2018

    Luca Basta, Stefano Veronesi, Yuya Murata, Zo´ e Dubois, Neeraj Mishra, Filippo Fabbri, Camilla Coletti, and Stefan Heun. A sensitive calorimetric technique to study energy (heat) exchange at the nano-scale.Nanoscale, 10(21):10079–10086, 2018

  37. [45]

    Sub- nanowatt resolution direct calorimetry for probing real-time metabolic activity of individual c

    Sunghoon Hur, Rohith Mittapally, Swathi Yadlapalli, Pramod Reddy, and Edgar Meyhofer. Sub- nanowatt resolution direct calorimetry for probing real-time metabolic activity of individual c. elegans worms.Nature communications, 11(1):2983, 2020

  38. [46]

    Sub-nanowatt microfluidic single-cell calorimetry

    Sahngki Hong, Edward Dechaumphai, Courtney R Green, Ratneshwar Lal, Anne N Murphy, Christian M Metallo, and Renkun Chen. Sub-nanowatt microfluidic single-cell calorimetry. Nature communications, 11(1):2982, 2020

  39. [47]

    Variance sum rule for entropy production.Science, 383(6686):971–976, 2024

    Ivan Di Terlizzi, M Gironella, D Herraez-Aguilar, Timo Betz, F Monroy, M Baiesi, and F Ritort. Variance sum rule for entropy production.Science, 383(6686):971–976, 2024

  40. [48]

    Variance sum rule: proofs and solvable models

    Ivan Di Terlizzi, Marco Baiesi, and Felix Ritort. Variance sum rule: proofs and solvable models. New Journal of Physics, 26(6):063013, 2024

  41. [49]

    Thermodynamic probes of life.Science, 383(6686):952–953, 2024

    ´Edgar Rold´ an. Thermodynamic probes of life.Science, 383(6686):952–953, 2024

  42. [50]

    Fluorescence lifetimes: fundamentals and interpretations

    Ulai Noomnarm and Robert M Clegg. Fluorescence lifetimes: fundamentals and interpretations. Photosynthesis research, 101(2):181–194, 2009

  43. [51]

    Fluorescence lifetime imaging microscopy.Nature Reviews Methods Primers, 4(1):80, 2024

    Belen Torrado, Bruno Pannunzio, Leonel Malacrida, and Michelle A Digman. Fluorescence lifetime imaging microscopy.Nature Reviews Methods Primers, 4(1):80, 2024

  44. [52]

    Molec- ular fluorescence lifetime fluctuations: on the possible role of conformational effects.Chemical physics letters, 372(1-2):282–287, 2003

    Renaud AL Vall´ ee, Gyula J Vancso, NF Van Hulst, J-P Calbert, J Cornil, and JL Bredas. Molec- ular fluorescence lifetime fluctuations: on the possible role of conformational effects.Chemical physics letters, 372(1-2):282–287, 2003

  45. [53]

    Design and application of fluorescent probes to detect cellular physical microenvironments.Chemical reviews, 124(4):1738– 1861, 2024

    Junbao Ma, Rui Sun, Kaifu Xia, Qiuxuan Xia, Yu Liu, and Xin Zhang. Design and application of fluorescent probes to detect cellular physical microenvironments.Chemical reviews, 124(4):1738– 1861, 2024

  46. [54]

    Viscoelastic phenotyping of red blood cells.Biophysical Journal, 123(7):770–781, 2024

    Marta Gironella-Torrent, Giulia Bergamaschi, Raya Sorkin, Gijs JL Wuite, and Felix Ritort. Viscoelastic phenotyping of red blood cells.Biophysical Journal, 123(7):770–781, 2024

  47. [55]

    A master relation defines the nonlinear viscoelasticity of single fibroblasts.Biophysical journal, 90(10):3796–3805, 2006

    Pablo Fern´ andez, Pramod A Pullarkat, and Albrecht Ott. A master relation defines the nonlinear viscoelasticity of single fibroblasts.Biophysical journal, 90(10):3796–3805, 2006

  48. [56]

    Viscoelasticity of the human red blood cell.American Journal of Physiology-Cell Physiology, 293(2):C597–C605, 2007

    Marina Puig-de Morales-Marinkovic, Kevin T Turner, James P Butler, Jeffrey J Fredberg, and Subra Suresh. Viscoelasticity of the human red blood cell.American Journal of Physiology-Cell Physiology, 293(2):C597–C605, 2007

  49. [57]

    Viscoelastic mechanics of living cells.Physics of Life Reviews, 53:91–116, 2025

    Hui Zhou, Ruye Liu, Yizhou Xu, Jierui Fan, Xinyue Liu, Longquan Chen, and Qiang Wei. Viscoelastic mechanics of living cells.Physics of Life Reviews, 53:91–116, 2025

  50. [58]

    A microcalorimetric study of human erythrocytes in stirred buffer suspensions

    Per B¨ ackman. A microcalorimetric study of human erythrocytes in stirred buffer suspensions. Thermochimica acta, 205:87–97, 1992. 27

  51. [59]

    A critique of methods for temperature imaging in single cells.Nature methods, 11(9):899–901, 2014

    Guillaume Baffou, Herv´ e Rigneault, Didier Marguet, and Ludovic Jullien. A critique of methods for temperature imaging in single cells.Nature methods, 11(9):899–901, 2014

  52. [60]

    Temperature sensing using fluorescent nanothermometers

    Fiorenzo Vetrone, Rafik Naccache, Alicia Zamarr´ on, Angeles Juarranz de la Fuente, Francisco Sanz-Rodr´ ıguez, Laura Martinez Maestro, Emma Martin Rodriguez, Daniel Jaque, Jose Gar- cia Sole, and John A Capobianco. Temperature sensing using fluorescent nanothermometers. ACS n...

  53. [61]

    Mapping intracellular temperature using green fluorescent protein.Nano letters, 12(4):2107–2111, 2012

    Jon S Donner, Sebastian A Thompson, Mark P Kreuzer, Guillaume Baffou, and Romain Quidant. Mapping intracellular temperature using green fluorescent protein.Nano letters, 12(4):2107–2111, 2012

  54. [62]

    Intracellular temperature mapping with a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy.Nature communications, 3(1):705, 2012

    Kohki Okabe, Noriko Inada, Chie Gota, Yoshie Harada, Takashi Funatsu, and Seiichi Uchiyama. Intracellular temperature mapping with a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy.Nature communications, 3(1):705, 2012

  55. [63]

    Nanometre-scale thermometry in a living cell.Nature, 500(7460):54–58, 2013

    Georg Kucsko, Peter C Maurer, Norman Ying Yao, MICHAEL Kubo, Hyun Jong Noh, Po Kam Lo, Hongkun Park, and Mikhail D Lukin. Nanometre-scale thermometry in a living cell.Nature, 500(7460):54–58, 2013

  56. [64]

    Nucleic acid based fluorescent nanothermometers.Acs Nano, 8(10):10372–10382, 2014

    Sara Ebrahimi, Yousef Akhlaghi, Mohsen Kompany-Zareh, and ˚Asmund Rinnan. Nucleic acid based fluorescent nanothermometers.Acs Nano, 8(10):10372–10382, 2014

  57. [65]

    Harnessing dna for nanothermometry.Journal of biophotonics, 14(2):e202000341, 2021

    Graham Spicer, Sylvia Gutierrez-Erlandsson, Ruth Matesanz, Hugo Bernard, Alejandro P Adam, Alejo Efeyan, and Sebastian Thompson. Harnessing dna for nanothermometry.Journal of biophotonics, 14(2):e202000341, 2021

  58. [66]

    Implication of thermal signaling in neuronal differentiation revealed by manipulation and measurement of intracellular temperature.Nature Communications, 15(1):3473, 2024

    Shunsuke Chuma, Kazuyuki Kiyosue, Taishu Akiyama, Masaki Kinoshita, Yukiho Shimazaki, Seiichi Uchiyama, Shingo Sotoma, Kohki Okabe, and Yoshie Harada. Implication of thermal signaling in neuronal differentiation revealed by manipulation and measurement of intracellular tempera...

  59. [67]

    Adam, Daniel Jaque, Jana B Nieder, and Roberto de la Rica

    Sebastian A Thompson, Ignacio A Martinez, Patricia Haro-Gonzalez, Alejandro P. Adam, Daniel Jaque, Jana B Nieder, and Roberto de la Rica. Plug and play anisotropy-based nanothermome- ters.ACS Photonics, 5(7):2676–2681, 2018

  60. [68]

    Advances and challenges for fluorescence nanothermometry.Nature methods, 17(10):967–980, 2020

    Jiajia Zhou, Blanca Del Rosal, Daniel Jaque, Seiichi Uchiyama, and Dayong Jin. Advances and challenges for fluorescence nanothermometry.Nature methods, 17(10):967–980, 2020

  61. [69]

    Mitochondria are physiologically maintained at close to 50 c.PLoS biology, 16(1):e2003992, 2018

    Dominique Chr´ etien, Paule B´ enit, Hyung-Ho Ha, Susanne Keipert, Riyad El-Khoury, Young-Tae Chang, Martin Jastroch, Howard T Jacobs, Pierre Rustin, and Malgorzata Rak. Mitochondria are physiologically maintained at close to 50 c.PLoS biology, 16(1):e2003992, 2018

  62. [70]

    Rare Treasure Editions, 2025

    Erwin Schr¨ odinger.What is life? The physical aspect of the living cell. Rare Treasure Editions, 2025

  63. [71]

    Quantum tunnelling in a dissipative system.Annals of physics, 149(2):374–456, 1983

    Amir O Caldeira and Anthony J Leggett. Quantum tunnelling in a dissipative system.Annals of physics, 149(2):374–456, 1983

  64. [72]

    Di Terlizzi et al., 2026

    I. Di Terlizzi et al., 2026. In preparation

  65. [73]

    Force spectroscopy with dual-trap optical tweez- ers: Molecular stiffness measurements and coupled fluctuations analysis.Biophysical Journal, 103(9):1919–1928, 2012

    Marco Ribezzi-Crivellari and Felix Ritort. Force spectroscopy with dual-trap optical tweez- ers: Molecular stiffness measurements and coupled fluctuations analysis.Biophysical Journal, 103(9):1919–1928, 2012

  66. [74]

    Universal axial fluctuations in optical tweezers.Optics Letters, 40(5):800–803, 2015

    Marco Ribezzi-Crivellari, Anna Alemany, and Felix Ritort. Universal axial fluctuations in optical tweezers.Optics Letters, 40(5):800–803, 2015. 28

  67. [75]

    The enthalpy change of adenosine triphosphate hydrolysis.Journal of Biological Chemistry, 218(2):945–959, 1956

    Richard J Podolsky and Manuel F Morales. The enthalpy change of adenosine triphosphate hydrolysis.Journal of Biological Chemistry, 218(2):945–959, 1956

  68. [76]

    Enzymes as active matter.Annual Review of Condensed Matter Physics, 12(1):177–200, 2021

    Subhadip Ghosh, Ambika Somasundar, and Ayusman Sen. Enzymes as active matter.Annual Review of Condensed Matter Physics, 12(1):177–200, 2021

  69. [77]

    Irreversibility and heat generation in the computing process.IBM journal of research and development, 5(3):183–191, 1961

    Rolf Landauer. Irreversibility and heat generation in the computing process.IBM journal of research and development, 5(3):183–191, 1961

  70. [78]

    The thermodynamics of computation—a review.International Journal of Theoretical Physics, 21(12):905–940, 1982

    Charles H Bennett. The thermodynamics of computation—a review.International Journal of Theoretical Physics, 21(12):905–940, 1982

  71. [79]

    The cosmological constants.Nature, 139(3512):323–323, 1937

    Paul AM Dirac. The cosmological constants.Nature, 139(3512):323–323, 1937

  72. [80]

    Stochastic gravity: Theory and applications.Living Reviews in Relativity, 11(1):1–112, 2008

    Bei Lok Hu and Enric Verdaguer. Stochastic gravity: Theory and applications.Living Reviews in Relativity, 11(1):1–112, 2008

  73. [81]

    Mechanistic aspects of enzymatic catalysis: lessons from comparison of rna and protein enzymes.Annual review of biochemistry, 66(1):19–59, 1997

    Geeta J Narlikar and Daniel Herschlag. Mechanistic aspects of enzymatic catalysis: lessons from comparison of rna and protein enzymes.Annual review of biochemistry, 66(1):19–59, 1997

  74. [82]

    Catalytic enzymes are active matter.Proceedings of the National Academy of Sciences, 115(46):E10812–E10821, 2018

    Ah-Young Jee, Yoon-Kyoung Cho, Steve Granick, and Tsvi Tlusty. Catalytic enzymes are active matter.Proceedings of the National Academy of Sciences, 115(46):E10812–E10821, 2018

  75. [83]

    Direct single molecule imaging of enhanced enzyme diffusion.Physical review letters, 123(12):128101, 2019

    Mengqi Xu, Jennifer L Ross, Lyanne Valdez, and Aysuman Sen. Direct single molecule imaging of enhanced enzyme diffusion.Physical review letters, 123(12):128101, 2019

  76. [84]

    Royal Society of Chemistry, 2024

    Juliane Simmchen, William Uspal, and Wei Wang.Active Colloids: From Fundamentals to Frontiers. Royal Society of Chemistry, 2024

  77. [85]

    Single molecule–driven nanomotors reveal the dynamic-disordered chemomechanical transduction of active enzymes.Science Advances, 11(5):eads0446, 2025

    Zhuodong Tang, Jingyu Wu, Shaojun Wu, Wenjing Tang, Jian-Rong Zhang, Wenlei Zhu, Jun- Jie Zhu, and Zixuan Chen. Single molecule–driven nanomotors reveal the dynamic-disordered chemomechanical transduction of active enzymes.Science Advances, 11(5):eads0446, 2025

  78. [86]

    Diffusion measurements of swimming enzymes with fluorescence correlation spectroscopy.Accounts of chemical research, 51(9):1911– 1920, 2018

    Jan-Philipp Gunther, Michael Borsch, and Peer Fischer. Diffusion measurements of swimming enzymes with fluorescence correlation spectroscopy.Accounts of chemical research, 51(9):1911– 1920, 2018

  79. [87]

    Zhijie Chen, Alan Shaw, Hugh Wilson, Maxime Woringer, Xavier Darzacq, Susan Marqusee, Quan Wang, and Carlos Bustamante. Single-molecule diffusometry reveals no catalysis-induced diffusion enhancement of alkaline phosphatase as proposed by fcs experiments.Proceedings of the Nat...

  80. [88]

    Following molecular mobility during chemical reactions: no evidence for active propulsion.Journal of the American Chemical Society, 143(49):20884–20890, 2021

    Lucy L Fillbrook, Jan-Philipp Gunther, Gunter Majer, Daniel J O’Leary, William S Price, Hal Van Ryswyk, Peer Fischer, and Jonathon E Beves. Following molecular mobility during chemical reactions: no evidence for active propulsion.Journal of the American Chemical Society, 143(4...

  81. [89]

    Excess and loss of entropy production for different levels of coarse graining.Physical Review E, 104(2):024140, 2021

    Pierpaolo Bilotto, Lorenzo Caprini, and Angelo Vulpiani. Excess and loss of entropy production for different levels of coarse graining.Physical Review E, 104(2):024140, 2021

  82. [90]

    Time-dependent trajectory of a one-dimensional gaussian non-markovian ob- servable does not reveal its nonequilibrium character.Physical Review E, 112(1):014132, 2025

    Roland R Netz. Time-dependent trajectory of a one-dimensional gaussian non-markovian ob- servable does not reveal its nonequilibrium character.Physical Review E, 112(1):014132, 2025

  83. [91]

    Entropy production in field theories without time-reversal symmetry: quanti- fying the non-equilibrium character of active matter.Physical Review X, 7(2):021007, 2017

    Cesare Nardini, ´Etienne Fodor, Elsen Tjhung, Fr´ ed´ eric Van Wijland, Julien Tailleur, and Michael E Cates. Entropy production in field theories without time-reversal symmetry: quanti- fying the non-equilibrium character of active matter.Physical Review X, 7(2):021007, 2017. 29

  84. [92]

    En- tropy production fluctuations encode collective behavior in active matter.Physical Review E, 103(1):012613, 2021

    Trevor GrandPre, Katherine Klymko, Kranthi K Mandadapu, and David T Limmer. En- tropy production fluctuations encode collective behavior in active matter.Physical Review E, 103(1):012613, 2021

  85. [93]

    Thermodynamics of active field theories: Energetic cost of coupling to reservoirs.Physical Review X, 11(2):021057, 2021

    Tomer Markovich, ´Etienne Fodor, Elsen Tjhung, and Michael E Cates. Thermodynamics of active field theories: Energetic cost of coupling to reservoirs.Physical Review X, 11(2):021057, 2021

  86. [94]

    How far from equilibrium is active matter?Physical review letters, 117(3):038103, 2016

    ´Etienne Fodor, Cesare Nardini, Michael E Cates, Julien Tailleur, Paolo Visco, and Fr´ ed´ eric Van Wijland. How far from equilibrium is active matter?Physical review letters, 117(3):038103, 2016

  87. [95]

    The statistical physics of active matter: From self- catalytic colloids to living cells.Physica A: Statistical Mechanics and its Applications, 504:106– 120, 2018

    ´Etienne Fodor and M Cristina Marchetti. The statistical physics of active matter: From self- catalytic colloids to living cells.Physica A: Statistical Mechanics and its Applications, 504:106– 120, 2018

  88. [96]

    Why life is hot.arXiv preprint arXiv:2512.04725, 2025

    Tanja Schilling, Patrick B Warren, and Wilson Poon. Why life is hot.arXiv preprint arXiv:2512.04725, 2025

  89. [97]

    Protein condensates as aging maxwell fluids.Science, 370(6522):1317–1323, 2020

    Louise Jawerth, Elisabeth Fischer-Friedrich, Suropriya Saha, Jie Wang, Titus Franzmann, Xi- aojie Zhang, Jenny Sachweh, Martine Ruer, Mahdiye Ijavi, Shambaditya Saha, et al. Protein condensates as aging maxwell fluids.Science, 370(6522):1317–1323, 2020

  90. [98]

    Artificial brownian motors: Controlling transport on the nanoscale.Reviews of Modern Physics, 81(1):387–442, 2009

    Peter H¨ anggi and Fabio Marchesoni. Artificial brownian motors: Controlling transport on the nanoscale.Reviews of Modern Physics, 81(1):387–442, 2009

  91. [99]

    Chemistry in motion: tiny synthetic motors.Accounts of chemical research, 47(12):3504–3511, 2014

    Peter H Colberg, Shang Yik Reigh, Bryan Robertson, and Raymond Kapral. Chemistry in motion: tiny synthetic motors.Accounts of chemical research, 47(12):3504–3511, 2014

  92. [100]

    Artificial molecular motors.Chemical Society Reviews, 46(9):2592–2621, 2017

    Salma Kassem, Thomas van Leeuwen, Anouk S Lubbe, Miriam R Wilson, Ben L Feringa, and David A Leigh. Artificial molecular motors.Chemical Society Reviews, 46(9):2592–2621, 2017

  93. [101]

    Motility of an autonomous protein-based artificial motor that operates via a burnt-bridge principle.Nature Communications, 15(1):1511, 2024

    Chapin S Korosec, Ivan N Unksov, Pradheebha Surendiran, Roman Lyttleton, Paul MG Curmi, Christopher N Angstmann, Ralf Eichhorn, Heiner Linke, and Nancy R Forde. Motility of an autonomous protein-based artificial motor that operates via a burnt-bridge principle.Nature Communica...

  94. [102]

    Cell state transitions: defi- nitions and challenges.Development, 148(20):dev199950, 2021

    Carla Mulas, Agathe Chaigne, Austin Smith, and Kevin J Chalut. Cell state transitions: defi- nitions and challenges.Development, 148(20):dev199950, 2021

  95. [103]

    Control of cell state transitions.Nature, 609(7929):975–985, 2022

    Oleksii S Rukhlenko, Melinda Halasz, Nora Rauch, Vadim Zhernovkov, Thomas Prince, Kieran Wynne, Stephanie Maher, Eugene Kashdan, Kenneth MacLeod, Neil O Carragher, et al. Control of cell state transitions.Nature, 609(7929):975–985, 2022

  96. [104]

    Establishing a conceptual framework for holistic cell states and state transitions.Cell, 187(11):2633–2651, 2024

    Susanne M Rafelski and Julie A Theriot. Establishing a conceptual framework for holistic cell states and state transitions.Cell, 187(11):2633–2651, 2024

  97. [105]

    Jin Wang, Li Xu, and Erkang Wang. Potential landscape and flux framework of nonequilibrium networks: robustness, dissipation, and coherence of biochemical oscillations.Proceedings of the National Academy of Sciences, 105(34):12271–12276, 2008

  98. [106]

    The molecular and mathematical basis of waddington’s epigenetic landscape: A framework for post-darwinian biology?BioEssays, 34(2):149–157, 2012

    Sui Huang. The molecular and mathematical basis of waddington’s epigenetic landscape: A framework for post-darwinian biology?BioEssays, 34(2):149–157, 2012. 30

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.