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Record W3119298323 · doi:10.1017/qrd.2020.15

Which are the ‘Hilbert Problems’ of Biophysics?

2021· editorial· en· W3119298323 on OpenAlexaff
Bengt Nordén

Bibliographic record

VenueQRB Discovery · 2021
Typeeditorial
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsContent (measure theory)Computer scienceAction (physics)MathematicsPhysicsMathematical analysisQuantum mechanics

Abstract

fetched live from OpenAlex

Whilst considering commissioning articles for QRB Discovery as Editor-in-Chief, I would like to explain what I think could be interesting topics for new articles in addition to unexpected amazing discoveries.Recently, I submitted a somewhat philosophical paper with the title 'The Mole and Albert Einstein' to which I had been invited by the Swedish Physics Society.To prepare myself, I had been reading Einstein's famous papers and PhD Thesis of 1905 (in German to avoid interpreters' pedagogical improvements!) and was struck by his quite narrow approach in his attempt to address and generalize so many different problems.I do not want to diminish the impression of his genius, but I suddenly saw Einstein as a somewhat bewildered young (25 years) man eager to make an impact.When in 2005 the Physics world celebrated the anniversary of Einstein's miracle year, he was claimed to be a Chemist by Nature columnist Philip Ball because of his work and PhD Thesis on molecular diffusion inspired by Brownian motion and his goal to determine Avogadro's numberbut then why not call Einstein a Biophysicist?Following van't Hoff, the first Nobel Laureate in Chemistry (1901), Einstein starts with the osmotic pressure and van't Hoff's formula pV* = nRT, which looks like the ideal gas law, and which he assumes to be a thermodynamic macroscopic equation of state integrating corresponding microscopic equations in which the colliding solvent molecules just balance the osmotic pressure.His analysis (including some strange detours) leads to the famous statistical error function for diffusion.His explanation of the Brownian motion also results in the final acceptance by the science society of the atom and molecule concepts in the shape we know them today.The jumpy random motions that Robert Brown observed pollen grains to perform when suspended in water, Einstein explains, reflect the thermal motions of surrounding water molecules, an unequal number obviously colliding from opposite sides of the grain, telling something about the size of the numbers.The molecular dynamics and molecular-kinetic theory of heat are at the heart of Einstein's thinking in 1905 and many years later his endeavor to develop a universal theory for the fundamental forces can be seen as just an expansion of this early work on liquids and intermolecular forces, based on Newtonian kinetic theory of matter.Where am I heading with this?Well, I might ask: would a modern approach by molecular dynamics computation exactly agree with Einstein's description of Brownian dynamics?Or might there be some deviations that could suggest additional, yet undiscovered mechanisms, such as wave front-like attacks by many coherently coupled solvent molecules by which the momentum transfer between the small molecules and big pollen grains could occur?The biophysical impact of such a mechanism could be earth-shattering, changing the rules for thermal activations of various processes inside the cell, adding large fluctuation forces.Unfortunately (or fortunately?),my checks in recent literature all indicate excellent agreement between modern molecular dynamics simulations and Brownian motion according to the classical Langevin equation as characterized in terms of autocorrelation memory and fluctuating force functions.Nor do any reported disagreement with experiment suggest the existence of any additional effects, neither under thermodynamic equilibrium conditions nor at investigated nonequilibrium conditions (electrophoresis and sedimentation).From this, I conclude that I must probably dismiss fundamentals of molecular diffusion as a problem area worth prioritized attention.Or maybe not since MD today is still limited to rather short times?With this example, I would like to open a discussion asking: can we identify problem areas or paradigmatic models in biophysics and molecular biology that would deserve special attention because they are particularly important and not fully understood?Of course, we may not predict discovery, but as Pasteur says we could help chance for making one by having a prepared mind.With inspiration from 'Hilbert's 23 Problems', I would encourage all our readers, Editors, and Editorial Board Members to come up with suggestions for a list of such problems in contemporary biophysics-related sciences, whether theoretical, experimental, or intrinsic to the dissipative systems of living organisms.But like in my example above, before a certain problem be put forward, I would expect that effort has been made to search for evidence in the literature of any violations or conflicting data that can motivate further digging.I shall list examples here, starting with fundamentals of mechanisms in biology and then theoretical challenges and potential future method developments.I welcome your adding more points to the list or complementing/correcting the current ones.We might also eventually do a QRB Discovery

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0050.007
Open science0.0020.001
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0050.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.245
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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