Tópico: A Origem da vida segundo a Termodinâmica

[Gigaview]

O livro "Origem" de Dan Brown seria mais uma novelinha de 3a. categoria se não apresentasse uma idéia interessante saída da mente de um personagem, que de fato existe com o mesmo nome na vida real e que alguns apontam como alguém do quilate de Darwin: Jeremy England.

Schrödinger já tinha dado a dica: a termodinâmica é a chave para entender a origem da vida.

Obrigado Dan Brown, valeu esperar pela palestra de Edmond Kirsch mesmo após um suspense arrastado com Robert Langdon sem fazer praticamente nada  e ainda sair perdendo de 10 a 0 para a inteligência sintética de Winston.

Uma breve introdução abaixo seguida pela palestra de Jeremy England no MIT aprofundando o tema: No Turning Back: The Nonequilibrium Statistical Thermodynamics of becoming (and remaining) Life-Like

Vale a pena conferir.

[Buckaroo Banzai]

Algum dia quero tentar comparar:

Mathematical and Computer Modelling
Volume 19, Issues 6–8, March–April 1994, Pages 25–48

Life as a manifestation of the second law of thermodynamics ☆
E.D. Schneider∗
Hawkwood Institute P.O. Box 1017, Livingston, MT 59047, USA
J.J. Kay∗
Environment and Resource Studies, University of Waterloo Waterloo, Ontario, Canada, N2L 3G1
  Open Archive
Abstract
We examine the thermodynamic evolution of various evolving systems, from primitive physical systems to complex living systems, and conclude that they involve similar processes which are phenomenological manifestations of the second law of thermodynamics. We take the reformulated second law of thermodynamics of Hatsopoulos and Keenan and Kestin and extend it to nonequilibrium regions, where nonequilibrium is described in terms of gradients maintaining systems at some distance away from equilibrium.

The reformulated second law suggests that as systems are moved away from equilibrium they will take advantage of all available means to resist externally applied gradients. When highly ordered complex systems emerge, they develop and grow at the expense of increasing the disorder at higher levels in the system's hierarchy. We note that this behaviour appears universally in physical and chemical systems. We present a paradigm which provides for a thermodynamically consistent explanation of why there is life, including the origin of life, biological growth, the development of ecosystems, and patterns of biological evolution observed in the fossil record.

We illustrate the use of this paradigm through a discussion of ecosystem development. We argue that as ecosystems grow and develop, they should increase their total dissipation, develop more complex structures with more energy flow, increase their cycling activity, develop greater diversity and generate more hierarchical levels, all to abet energy degradation. Species which survive in ecosystems are those that funnel energy into their own production and reproduction and contribute to autocatalytic processes which increase the total dissipation of the ecosystem. In short, ecosystems develop in ways which systematically increase their ability to degrade the incoming solar energy. We believe that our thermodynamic paradigm makes it possible for the study of ecosystems to be developed from a descriptive science to predictive science founded on the most basic principle of physics.

http://izt.ciens.ucv.ve/ecologia/Archivos/Referencias/stu/schneider_kay-mathcompmod94.pdf

[...] THE ORIGIN OF LIFE  The    origin    of    prebiotic    life    is    thedevelopment    of     another     route    for     thedissipation  of  induced  energy  gradients.    Lifewith  its  requisite  ability  to  reproduce,  insuresthat  these  dissipative  pathways   continue  and  ithas    evolved    strategies    to    maintain     thesedissipative structures in the face of  a  fluctuatingphysical  environment.     We  suggest  that  livingsystems are  dynamic  dissipative  systems  withencoded memories, the gene with its  DNA, thatallow   the   dissipative   processes   to    continuewithout having to restart the  dissipative processvia   stochastic   events.      Living systems   aresophisticated  mini-tornados,  with   a  memory(its  DNA),  whose  Aristotelian  "final   cause"may  be  the  second  law  of  thermodynamics.However  one   should   be  clear  not  to  overstatethe role of thermodynamics in  living processes.The restated second law is a necessary but not  asufficient condition for life. 

[...]

The  origin  of  life  should  not  be  seen  as   anisolated event.      Rather   it    represents    theemergence  of  yet  another  class  of  processeswhose goal is the dissipation of thermodynamicgradients.    Life should  be   viewed   as  the  most sophisticated  (until  now) end  in  the   continuumof development of  natural  dissipative  structuresfrom  physical  to  chemical  to   autocatalytic   toliving systems.    As  we  have discussed  earlierautocatalytic     chemical     reactions     are      thebackbone of chemical dissipative systems.    Thework  of  Eigen (1971), Eigen  and  Schuster(1979) connect     autocatalytic     and     self-reproductive   macromolecular   species   with   a thermodynamic vision  of  the  origin  of  life.

[...]

From the  point  of  view  of  nonequilibriumthermodynamics   of   chemical reactions   in    ahomogeneous  system,  Eigen has  shown  thatthe       Darwinian       selection        of       suchmacromolecular   species   can   be    linked    toGlansdorff   &   Prigogine's   stability   criterion(Nicolis  and  Prigogine,  1977  and  Peacocke,1983).  By examining  the  entropy  changes  andentropy  production  of  such  systems  Ishida  hasformulated  a   nonequilibrium   thermodynamicsof  the  hypercycles.    This   theory   is   used   todiscuss the  stability   of   the  "quasi-species"  (asolution    to    Eigen's    equations    with    non-vanishing mutation terms).    In  principle,  it  ispossible,              using              nonequilibriumthermodynamics   of    open   systems,    to    putforward   scenarios   for   the   development    ofbiochemical  machinery  for the  duplication  andtranslation    of     nucleic     acids    and     othermacromolecules essential to life.

[...]

[EuSouOqueSou]

Postei sobre isso em outro tópico:

Lembrei dessa matéria que li algum tempo atrás:

A New Physics Theory of Life

https://www.scientificamerican.com/article/a-new-physics-theory-of-life/

O autor explica a origem da vida sob a luz de fenômenos puramente físicos, não apenas com argumentação teórico-matemática, mas também apresentando resultados experimentais.

Em resumo, um grupo de átomos sofrendo estímulos externos (recebendo energia) tende a se (re)organizar gradualmente para dissipar essa energia. Parte da energia é mantida mantendo a ordem adquirida e o restante é dissipado em outras formas de energia e, pelo que entendi, maiores medidas de entropia. E segundo seus estudos, a auto-replicação é um dos meios de dissipar grandes quantidades de energia.

... At the heart of England’s idea is the second law of thermodynamics, also known as the law of increasing entropy or the “arrow of time.” ...

... Although entropy must increase over time in an isolated or “closed” system, an “open” system can keep its entropy low — that is, divide energy unevenly among its atoms — by greatly increasing the entropy of its surroundings. In his influential 1944 monograph “What Is Life?” the eminent quantum physicist Erwin Schrödinger argued that this is what living things must do. ...

... Life does not violate the second law of thermodynamics, but until recently, physicists were unable to use thermodynamics to explain why it should arise in the first place. In Schrödinger’s day, they could solve the equations of thermodynamics only for closed systems in equilibrium. In the 1960s, the Belgian physicist Ilya Prigogine made progress on predicting the behavior of open systems weakly driven by external energy sources (for which he won the 1977 Nobel Prize in chemistry). But the behavior of systems that are far from equilibrium, which are connected to the outside environment and strongly driven by external sources of energy, could not be predicted

This situation changed in the late 1990s, due primarily to the work of Chris Jarzynski, now at the University of Maryland, and Gavin Crooks, ...

... Using Jarzynski and Crooks’ formulation, he derived a generalization of the second law of thermodynamics that holds for systems of particles with certain characteristics: ...

... Particles tend to dissipate more energy when they resonate with a driving force, or move in the direction it is pushing them, and they are more likely to move in that direction than any other at any given moment. ...

Self-replication (or reproduction, in biological terms), the process that drives the evolution of life on Earth, is one such mechanism by which a system might dissipate an increasing amount of energy over time. As England put it, “A great way of dissipating more is to make more copies of yourself.” In a September paper in the Journal of Chemical Physics, he reported the theoretical minimum amount of dissipation that can occur during the self-replication of RNA molecules and bacterial cells, and showed that it is very close to the actual amounts these systems dissipate when replicating. He also showed that RNA, the nucleic acid that many scientists believe served as the precursor to DNA-based life, is a particularly cheap building material. Once RNA arose, he argues, its “Darwinian takeover” was perhaps not surprising.

[EuSouOqueSou]

E aqui, outro artigo correlato, para enriquecer a discussão:

Na onda da explicação sobre a origem da vida pela perspectiva da energia disponível, trago outro artigo muito interessante (não é sobre a origem em si, mas correlato) explicando a evolução sob essa mesma ótica de energia disponível.

Basicamente, o autor divide a cronologia da evolução em 5 grandes épocas, classificadas conforme a fonte de energia disponível: energia geotermica, luz do sol, oxigênio, carne e fogo.

Interessante notar que a diversidade biológica cresce conforme crescem as fontes de energia disponível. Isso é perfeitamente coerente com a tese do outro artigo no qual afirma-se que a organização da materia e sua auto-replicação ajuda a dissipar o excesso de energia. Ora, quanto mais energia disponível, mais provável surgirem novas formas de organização.

The history of the life–Earth system can be divided into five ‘energetic’ epochs, each featuring the evolution of life forms that can exploit a new source of energy. These sources are: geochemical energy, sunlight, oxygen, flesh and fire. The first two were present at the start, but oxygen, flesh and fire are all consequences of evolutionary events. Since no category of energy source has disappeared, this has, over time, resulted in an expanding realm of the sources of energy available to living organisms and a concomitant increase in the diversity and complexity of ecosystems. These energy expansions have also mediated the transformation of key aspects of the planetary environment, which have in turn mediated the future course of evolutionary change. Using energy as a lens thus illuminates patterns in the entwined histories of life and Earth, and may also provide a framework for considering the potential trajectories of life–planet systems elsewhere.

Free energy is a universal requirement for life. It drives mechanical motion and chemical reactions—which in biology can change a cell or an organism1,2. Over the course of Earth history, the harnessing of free energy by organisms has had a dramatic impact on the planetary environment3,​4,​5,​6,​7. Yet the variety of free-energy sources available to living organisms has expanded over time. These expansions are consequences of events in the evolution of life, and they have mediated the transformation of the planet from an anoxic world that could support only microbial life, to one that boasts the rich geology and diversity of life present today. Here, I review these energy expansions, discuss how they map onto the biological and geological development of Earth, and consider what this could mean for the trajectories of life–planet systems elsewhere.


(i) Life emerges; epoch of geochemistry begins. (ii) Anoxygenic photosynthesis: start of energy epoch 2, sunlight. (iii) Emergence of cyanobacteria. (iv) Great Oxidation Event: energy epoch 3, oxygen. (v) Probable eukaryotic fossils appear. (vi) Fossils of red algae appear. (vii) Start of energy epoch 4, flesh. (viii) Vascular plants colonize land; fire appears on Earth. Finally, the burning logs indicate the start of energy epoch 5, fire.

https://www.nature.com/articles/s41559-017-0138

[Buckaroo Banzai]

Os "resultados experimentais" praticamente não têm nada a ver com origem da vida em si, mas tem relação com como algum tipo de bactérias é energeticamente eficiente, se bem me lembro.

Se não estiver se chamando de "resultados experimentais" umas coisas até como fazer limalha de ferro se mover "organizadamente" passando um imã por baixo de uma superfície, acho que tinha algo assim, dentre outras coisas que me pareciam apenas infimamente relacionadas, ainda que mais do que essa.

[Tirn Aill]

Será que um dia os cientistas vão conseguir criar vida em laboratório imitando as condições iniciais da terra?

[Buckaroo Banzai]

Talvez... se consegue de vez em quando resultados fantásticos apenas por acidente:

https://www.newscientist.com/article/dn25471-spark-of-life-metabolism-appears-in-lab-without-cells/

[...] Remarkably, the discovery was an accident, stumbled on during routine quality control testing of the medium used to culture cells at Ralser’s laboratory. As a shortcut, one of his students decided to run unused media through a mass spectrometer, which spotted a signal for pyruvate – an end product of a metabolic pathway called glycolysis.

To test whether the same processes could have helped spark life on Earth, they approached colleagues in the Earth sciences department who had been working on reconstructing the chemistry of the Archean Ocean, which covered the planet almost 4 billion years ago. This was an oxygen-free world, predating photosynthesis, when the waters were rich in iron, as well as other metals and phosphate. All these substances could potentially facilitate chemical reactions like the ones seen in modern cells.

Metabolic backbone
Ralser’s team took early ocean solutions and added substances known to be starting points for modern metabolic pathways, before heating the samples to between 50 ˚C and 70 ˚C – the sort of temperatures you might have found near a hydrothermal vent – for 5 hours. Ralser then analysed the solutions to see what molecules were present.

“In the beginning we had hoped to find one reaction or two maybe, but the results were amazing,” says Ralser. “We could reconstruct two metabolic pathways almost entirely.”

The pathways they detected were glycolysis and the pentose phosphate pathway, “reactions that form the core metabolic backbone of every living cell,” Ralser adds. Together these pathways produce some of the most important materials in modern cells, including ATP – the molecule cells use to drive their machinery, the sugars that form DNA and RNA, and the molecules needed to make fats and proteins.

If these metabolic pathways were occurring in the early oceans, then the first cells could have enveloped them as they developed membranes.

In all, 29 metabolism-like chemical reactions were spotted, seemingly catalysed by iron and other metals that would have been found in early ocean sediments. The metabolic pathways aren’t identical to modern ones; some of the chemicals made by intermediate steps weren’t detected. However, “if you compare them side by side it is the same structure and many of the same molecules are formed,” Ralser says. These pathways could have been refined and improved once enzymes evolved within cells. [...]

[Gigaview]

Sei lá...mas me parece que essa perspectiva fornecida pela termodinâmica (segundo J. England) para a origem da vida serve mais como justificativa geral para a existência de subsistemas organizados (dissipadores eficientes de energia) em sistemas orientados para a desordem (espalhadores de energia). Até onde consegui entender, ela não explica muita coisa sem recorrer a outras teorias que se preocupam com a mecânica fundamental daquilo que possivelmente acontece de fato e que já trazem implícitas as condições ditadas pelas leis da termodinâmica. A "evolução química" é um exemplo de teoria que me parece de investigação necessária para a compreensão da origem da vida e cuja objetividade coloca as idéias de England num patamar quase filosófico.

As idéias de England também não são originais. Ele mesmo faz referência a Schrödinger num de seus slides. E até onde pude perceber, não há muita diferença entre o que ele diz e o que foi dito no paper de Schneider de 1994. Acho que compará-lo a um novo Darwin, por isso, me parece um exagero mesmo sendo ele capaz de provar o que ele afirma, o que ainda não aconteceu.

Uma dúvida...uma simulação que apresenta a validação da hipótese de uma teoria tem o status de prova?

[Gigaview]

Será que um dia os cientistas vão conseguir criar vida em laboratório imitando as condições iniciais da terra?

Já tentaram em 1953. O experimento foi repetido com algumas alterações necessárias e de novo não se chegou a resultado algum.

https://www.scientificamerican.com/article/primordial-soup-urey-miller-evolution-experiment-repeated/

[Gorducho]

uma simulação que apresenta a validação da hipótese de uma teoria tem o status de prova?

No caso entendo que o vídeo APRESENTA a teoria (sobre como se dá a formação...).
Genericamente falando: não, pois simulações se baseiam em modelos feitos a partir da teoria a ser provada.
Então elas (as simulações) conferem verossimilhança à teoria, mas sem significar que no mundo real isso se suceda.
Mal comparando: é como o universo metafísico das ideias do Platão vs o universo físico.   

[Gigaview]

uma simulação que apresenta a validação da hipótese de uma teoria tem o status de prova?

No caso entendo que o vídeo APRESENTA a teoria (sobre como se dá a formação...).
Genericamente falando: não, pois simulações se baseiam em modelos feitos a partir da teoria a ser provada.
Então elas (as simulações) conferem verossimilhança à teoria, mas sem significar que no mundo real isso se suceda.
Mal comparando: é como o universo metafísico das ideias do Platão vs o universo físico.   

Ok...mas um experimento também se baseia numa situação (modelo real) montado a partir da teoria a ser provada. A diferença é que a situação do modelo para a simulação é totalmente virtual mas que reproduz com fidelidade estatisticamente aceitável as condições do modelo real. Certos experimentos são de realização impossível, como os de dimensão cosmológica/quântica ou aqueles que exigem muito tempo para observação dos resultados como a evolução, mas podem ser simulados. Nesse caso, a "prova" através da simulação seria uma espécie de conclusão por inferência, que é um procedimento lógico aceito. Não é?   

[Gorducho]

Mas nunca vai ser a prova empírica da teoria.
E.g.: nós nunca poderemos de fato empiricamente testar o Big Bang. Vai ficar sempre na teoria plausível.
Say o caso da possibilidade de partículas andarem @ velocidades superluminíferas...
Poderemos modelizar em supercomputadores um túnel; uma fonte produtora e aceleradora de determinadas partículas (as a serem testadas na tese...); blah blah blah...
Mas pra se ter a prova de que elas viajaram de fato @ velocidade > c elas têm que de fato saírem de Genebra, andarem os 730km e chegarem a  San Grasso...
Claro que tem tanta estatística nesse tipo de "medição" que vai ter até modelos pra interpretarem as leituras dos instrumentos 
Mas SUPOSTAMENTE elas terão que de fato terem andado tal distância em tempo menor que o previsto pra velocidade c
E não simplesmente serem softwares + pixels...

Veja o æter... Certamente que se poderá modelizá-lo em computador integrado às eq. de Maxwell (eletrodinâmica "clássica").
Se poderia incluso modelizar uma situação correspondente ao (experimento) Michelson-Morley. Mas será que o universo real (claro: axioma anti-solipsismo ON ) funciona assim como nosso modelo informático

[Gigaview]

Ok...Mas por que um experimento em escala reduzida de um fenômeno como o de Urey-Miller, ou outro qualquer, não pode ser considerado uma simulação? A idéia não é simular a sopa primordial num balão de vidro e dar umas descargas elétricas para ver o que acontece ? Ninguém coletou uma amostra de sopa primordial, ela foi simulada artificialmente através de uma receita de bolo (= instruções = código = tipo de linguagem de programação química). Uma simulação tem que ser necessariamente um experimento abstrato, realizado por um computador?

Veja o æter... Certamente que se poderá modelizá-lo em computador integrado às eq. de Maxwell (eletrodinâmica "clássica"). Se poderia incluso modelizar uma situação correspondente ao (experimento) Michelson-Morley. Mas será que o universo real (claro: axioma anti-solipsismo ON ) funciona assim como nosso modelo informático

Veja que interessante:

The Nature of Scientific Proof in the Age of Simulations
BY KEVIN HENG
Is numerical mimicry a third way of establishing truth?


VIEW ISSUE

Empiricism lies at the heart of the scientific method. It seeks to understand the world through experiment and experience. This cycle of formulating and testing falsifiable hypotheses has amalgamated with a modern form of rationalism—the use of reasoning, mathematics, and logic to understand nature. These schools of thought are couched in centuries of history and, until recently, remained largely distinct.

Proponents of empiricism include the 18th-century Scottish philosopher David Hume, who believed in a subjective, sensory-based perception of the world. Rationalism is the belief that the use of reasoning alone is sufficient to understand the natural world, without any recourse to experiment. Its roots may be traced to the Greek philosophers Aristotle, Plato, and Pythagoras; its more modern proponents include Kant, Leibniz, and Descartes.

A clear example of both practices at work is in the field of astronomy and astrophysics. Astronomers discover, catalog, and attempt to make sense of the night sky using powerful telescopes. Astrophysicists mull over theoretical ideas, form hypotheses, make predictions for what one expects to observe, and attempt to discover organizing principles unifying astronomical phenomena. Frequently, researchers are practitioners of both subdisciplines.

Problems in astrophysics—and physics, in general—may often be rendered tractable by concentrating on the characteristic length, time, or velocity scales of interest. When trying to understand water as a fluid, it is useful to treat it as a continuous medium rather than as an enormous collection of molecules, because it makes it vastly easier to visualize (and compute) its macroscopic behavior. Although the Earth is evolving on geological time scales, its global climate is essentially invariant from one day to the next—hence the difficulty in explaining the urgency of climate change to the public. The planets of the Solar System do not orbit a static Sun, as it performs a ponderous wobble about its center of mass due to their collective gravitational tug, but it is often sufficient to visualize it as being so. Milankovitch cycles cause the eccentricity and obliquity of the Earth’s orbit to evolve over hundreds of thousands of years, but they are essentially constant over a human lifetime.

This separation of scales strips a problem down to its bare essence, allowing one to gain insight into the salient physics at the scale of interest.

Multiscale problems, on the other hand, do not lend themselves to such simplification. Small disturbances in a system might show up as big effects across myriad sizes and time scales. Structures on very large scales “talk” to features on very small scales and vice versa. For example, a grand challenge in astrophysics is understanding planet formation—being able to predict the diversity of exoplanets forming around a star, starting from a primordial cloud of gas and dust. Planet formation is an inherently multiscale problem: Uncertainties on microscopic scales, such as how turbulence and the seed particles of dust grains are created, hinder our ability to predict the outcome on celestial scales. Many real-life problems in biology, chemistry, physics, and the atmospheric and climate sciences are multiscale as well.

By necessity, a third, modern way of testing and establishing scientific truth—in addition to theory and experiment—is via simulations, the use of (often large) computers to mimic nature. It is a synthetic universe in a computer. One states an equation (or several) describing the physical system being studied, programs it into a computer, and marches the system forward in space and time. If all of the relevant physical laws are faithfully captured, then one ends up with an emulation—a perfect, The Matrix–like replication of the physical world in virtual reality.

In astronomy and astrophysics, this third way has come into its own, largely due to the unique status of astronomy as an experimental science. Unlike other, laboratory-based disciplines, astronomers may not exert full control over their experiments—one simply cannot rearrange objects in the sky. Astronomical phenomena often encode information about a subpopulation of a class of objects at a very specific moment in their evolution. To understand the entire population of a class of objects across cosmic time requires large computer simulations of their formation and evolution. Examples of different classes of objects include exoplanets, stars, black holes, galaxies, and even clusters of galaxies. The hope is that these simulations lead to big-picture understanding that unifies seemingly unrelated astronomical phenomena.

Computational Astrophysics Grows

During the 1940s through the 1980s, the late, distinguished Princeton astrophysicist Martin Schwarzschild was one of the first to use simulations to gain insights into astronomy, harnessing them to understand the evolution of stars and galaxies. Schwarzschild realized that the physical processes governing stellar structure are nonlinear and not amenable to analytical (pencil and paper) solutions, because it requires an understanding of the physics of nuclear burning, while galaxies are hardly perfect spheres. He proceeded to investigate them both using numerical solutions generated by large computers (at that time).

Both lines of inquiry have since blossomed into respected and full-fledged subdisciplines in astrophysics. Nowadays, an astrophysicist is as likely to be found puzzling over the engineering of complex computer code as he or she is to be found fiddling with mathematical equations on paper or chalkboard.

From the 1990s to the present, the approach of using computer simulations for testing hypotheses flourished. As technology advanced, astronomical data sets became richer, motivating the need for more detailed theoretical predictions and interpretations. Computers became more prevalent and faster, alongside rapid advances in the algorithmic techniques developed by computational science. Inexorably, the calculations produced by large simulations evolved to resemble experimental data sets in size, detail, and complexity.

Computational astrophysicists now come in three variants: engineers to build the code, researchers to formulate hypotheses and design numerical experiments, and others to process and interpret the resulting massive output. Supercomputing centers function almost like astronomical observatories. For better or worse, this third way of establishing scientific truth appears to be here to stay.

Seeking Truth

In a series of lunchtime conversations with astrophysicist Piet Hut of the Institute for Advanced Study in Princeton, I discovered that we were both concerned about the implications of these ever-expanding simulations. Computational astrophysics has adopted some of the terminology and jargon traditionally associated with the experimental sciences. Simulations may legitimately be regarded as numerical experiments, along with the assumptions, caveats, and limitations associated with any traditional, laboratory-based experiment. Simulated results are often described as being empirical, a term usually reserved for natural phenomena rather than numerical mimicries of nature. Simulated data are referred to as data sets, seemingly placing them on an equal footing with observed natural phenomena.

The Millennium Simulation Project, designed and executed by the Max Planck Institute for Astrophysics in Munich, Germany, provides a pioneering example of such an approach. It is a massive simulation of a universe in a box, elucidating the very fabric of the cosmos. The data sets generated by these simulations are so widely used that entire workshops are organized around them. Mimicry has supplanted astronomical data.

It is not far-fetched to say that all theoretical studies of nature are approximations. There is no single equation that describes all physical phenomena in the universe—and even if we could write one down in principle, solving it would be prohibitive, if not downright impossible. The equations we study as theorists are merely approximations of nature. Schrödinger’s equation describes the quantum world in the absence of gravity. The Navier-Stokes equation is a macroscopic description of fluids. Newton’s equation describes gravity accurately under terrestrial conditions, superseded only by Einstein’s equations under less familiar conditions.

To understand the orbital motion of exoplanets around distant stars, it is mostly sufficient to consider only Newtonian gravity. To understand the appearance of these exoplanets’ atmospheres requires approximating them as fluids and understanding the macrosopic manifestations of the quantum mechanical properties of the individual molecules: their absorption and scattering properties. Each of these governing equations is based on a law of nature—the conservation of mass, energy, or momentum, or some other generalized, more abstract quantity such as potential vorticity. One selects the appropriate governing equation of nature and solves it in the relevant physical regimes, thus creating a model that captures a limited set of salient properties of a physical system. The term itself is widely abused—a “model” that is not based on a law of nature has little right to be called one.

Bigger, Better, Faster: A Need for Standards of Quality

A fundamental limitation of any simulation is that there is a practical limit to how finely one may slice space and time in a computer such that the simulation completes within a reasonable amount of time (say, within the duration of one’s Ph.D. thesis). For multiscale problems, there will always be phenomena operating on scales smaller than the size of one’s simulation pixel. Astrophysicists call these subgrid physics—literally physics happening below the grid of the simulation. This difficulty of simulating phenomena from microscopic to macroscopic scales, across many, many orders of magnitude in size, is known as a dynamic range problem.

As computers become more powerful, one may always run simulations that explore a greater range of sizes and divide up space and time ever more finely, but in multiscale problems there will always be unresolved subgrid phenomena. Astrophysics and climate science appear to share this nightmare. In simulating the formation of galaxies, the birth, evolution, and death of stars are determining the global appearance of these synthetic galaxies themselves. Galaxies typically span tens of thousands of light years across, whereas stars operate on scales that are roughly 100 billion times smaller.

The climate of Earth appears to be significantly influenced by clouds, which both heat and cool the atmosphere. On scales of tens to hundreds of kilometers, the imperfect cancellation between these two effects is what matters. To get the details of this cancellation correct, we need to understand how the clouds formed and how their emergent properties developed, which ultimately requires an intimate understanding of how the microscopic seed particles of clouds were first created. Remarkably, uncertainties about cloud formation on such fine scales are hindering our ability to predict whether a given exoplanet is potentially habitable. Cloud formation remains a largely unsolved puzzle across several scientific disciplines. In both examples, it remains challenging to simulate the entire range of phenomena, due to the prohibitive amount of computing time needed and our incomplete understanding of the physics involved on smaller scales.

Another legitimate concern is the use of simulations as “black boxes” to churn out results and generate seductive graphics or movies without deeply questioning the assumptions involved. For example, simulations involving the Navier-Stokes equation often assume a Newtonian fluid—one that retains no memory of what was done to it in the past and offers more resistance or friction when layers of it are forced to slide past one another. Newtonian fluids are a plausible starting point for a rich variety of simulations, ranging from planetary atmospheres to accretion disks around black holes. Curiously, several common fluids are non-Newtonian. Dough is an example of a fluid with a memory of its past states, whereas ketchup tends to become less viscous when it is increasingly deformed. Attempting to simulate these fluids using a Newtonian assumption is an exercise in futility.

To use a simulation as a laboratory, one has to understand how to break it—otherwise, one may mistake an artifact as a result. In approximating continua as being discrete, one has to pay multiple penalties. Spurious oscillations or enhanced viscosity that are artifacts of this procedure may easily be misinterpreted as being physically meaningful. When one slices up space and time in a simulation, it may introduce features that look like real waves or make the fluid more viscous in an artificial way. The conservation of mass, momentum, and energy—cornerstones of theoretical physics—may no longer be taken for granted in a simulation and depend on the numerical scheme being employed, even if the governing equation conserves all of these quantities perfectly on paper.

Despite these concerns, a culture of “bigger, better, faster” is prevailing. It is not uncommon to hear discussions centered on how one can make one’s code more complex and run even faster on a mind-boggling number of computing cores. It is almost as if gathering exponentially increasing amounts of information will automatically translate into knowledge, that the simulated system attains self-awareness. As terabytes upon terabytes of information are being churned out by ever more massive simulations, the gulf between information and knowledge is widening. We appear to be missing a set of guiding principles—a metacomputational astrophysics, for lack of a better term.

Questions for metacomputational astrophysics include: Is scientific truth more robustly represented by the simplest or the most complex model? (Many would say simplest, but this view is not universally accepted.) How may we judge when a simulation has successfully approximated reality in some way? (The visual inspection of a simulated image of, say, a galaxy versus one obtained with a telescope is sentimentally satisfying, but objectively unsatisfactory.) When is “bigger, better, faster” enough? Does one obtain an ever-better physical answer by simply ramping up the computational complexity?

An alternative approach is to construct a model hierarchy—a suite of models of varying complexity that develops understanding in steps, allowing each physical effect to be isolated. Model hierarchies are standard practice in climate science. Focused models of microprocesses (turbulence, cloud formation, and so on) buttress global simulations of how the atmosphere, hydrosphere, biosphere, cryosphere, and lithosphere interact.

Reproducibility and Falsifiability

With increasingly complex simulations, there are also questions surrounding the practice of science. It is not unheard of to encounter published papers in astrophysics where insufficient information is provided for the reproduction of simulated results. Frequently, the computer codes used to perform these simulations are proprietary and complex enough that it would take years and the dedicated efforts of a research team to completely re-create one of them. Scientific truth is monopolized by a few and dictated to the rest. Is it still science if the results are not readily reproducible? (Admittedly, “readily” has a subjective meaning.)

There are also groups and individuals who take the more modern approach of making their codes open source. This has the tremendous advantage that the task of scrutinizing, testing, validating, and debugging the code no longer rests on the shoulders of an individual, but of the entire community. Some believe this amounts to giving away trade secrets, but there are notable examples of researchers whose careers have blossomed partly because of influential computer codes they made freely available.

A pioneer in this regard is Sverre Aarseth, a Cambridge astrophysicist who wrote and gave away codes that computed the evolution of astronomical objects (planets, stars, and so on) under the influence of gravity. Jim Stone of Princeton and Romain Teyssier of Zurich are known for authoring a series of codes that solve the equations of magnetized fluids, which have been used to study a wide variety of problems in astrophysics. Volker Springel of Heidelberg made his mark via the Millenium Simulation Project. In all of these cases, the publicly available computer codes became influential because other researchers incorporated them into their repertoire.

A related issue is falsifiability. If a physical system is perfectly understood, it comes with no freedom of specifying model inputs. Technically, astrophysicists call these free parameters. Quantifying how the sodium atom absorbs light provides a fine example—it is a triumph of quantum physics that such a calculation requires no free parameters. In large-scale simulations, there are always physical aspects that are poorly or incompletely understood and need to be mimicked by approximate models that specify free parameters. Often, these pseudomodels are not based on fundamental laws of physics but consist of ad hoc functions calibrated on experimental data or smaller-scale simulations, which may not be valid in all physical regimes.

An example is the planetary boundary layer on Earth, which arises from the friction between the atmospheric flow and the terrestrial surface and is an integral part of the climatic energy budget. The exact thickness of the planetary boundary layer depends on the nature of the surface; whether it is an urban area, grasslands, or ocean matters. Such complexity cannot be directly and feasibly computed in a large-scale climate simulation. Hence, one needs experimentally measured prescriptions for the thickness of this layer as inputs for the simulation. To unabashedly apply these prescriptions to other planets (or exoplanets) is to stand on thin ice. Worryingly, there is an emerging subcommunity of researchers switching over to exoplanet science from the Earth sciences who are bringing with them such Earth-centric approaches.

To form large-scale galaxies, one needs prescriptions for star formation and how supernovas feed energy back into their environments. To simulate the climate, one needs prescriptions for turbulence and precipitation. Such prescriptions often employ a slew of free parameters that are either inadequately informed by data or involve poorly known physics.

As the number of free parameters in a simulation increase, so does the diversity and variety of simulated results. In the most extreme limit, the simulation predicts everything—it is consistent with every outcome anticipated. A quote attributed to John von Neumann describes it best, “With four parameters, I can fit an elephant and with five I can make him wiggle his trunk.” Inattention to falsifiability has been chided by Wolfgang Pauli, who remarked, “It is not only incorrect, it is not even wrong.” A simulation that cannot be falsified can hardly be considered science.

Simulations as a third way of establishing scientific truth are here to stay. The challenge is for the astrophysical community to wield them as transparent, reproducible tools, thereby placing them on an equally credible footing with theory and experiment.

https://www.americanscientist.org/article/the-nature-of-scientific-proof-in-the-age-of-simulations

Acho que isso responde a minha pergunta.

[Gorducho]

Digamos que são posturas epistemológicas.
Eu não concordo. Claro, os teoristas ao fazerem uma simulação ficarão satisfeitos consigos próprios ao chegarem a resultados compatíveis com suas teses.
Mas nada nunca garantirá que no universo real as coisas se passem assim. Se poderá (say) numa câmara fechada simular o ambiente de Venus e tirar certas conclusões disso. Mas nada nunca garantirá que de fato lá em Venus as coisas se passem assim. Nunca jamais tem como prever TODAS as variáveis e situações que podem acontecer em escala natural e na realidade.
Veja que com toda firmemente estabelecida análise dimensional, ainda assim fazem testes em túneis de vento na medida do possível em tamanho real.

In the most extreme limit, the simulation predicts everything— until aparecer uma coisa com a qual os teóricos não contavam.


Veja nas inimaginavelmente sofisticadas industrias aeronáutica + automotiva...
Mesmo todas as simulações não substituem centenas de horas de voo ou rodagem de provas em todos tipos imagináveis de terrenos reais. O motor acoplado a uma carga funcionando centenas de horas na bancada, &c.

Claro, uma simulação poderá provar que "vida" pode surgir a partir dum mix de certos ingredientes e condições termodinâmicas. Isso sim, sem duvidas 
Se puserem certo mix de ingredientes numa camara selada a dadas pressões, temperaturas, descargas elétricas, convecções, correntes internas...
e aí aparecerem organismos "vivos", sim isso sim provaria a tese essa no caso.
Mas será em situações muito específicas pra provar certos pontos.

[Gigaview]

Devemos considerar também a natureza das simulações. Simulações com processos estocásticos são diferentes de simulações com processos determinísticos. Por exemplo, uma simulação que usa cadeias Markov  é totalmente diferente de uma simulação de Monte Carlo tanto na complexidade da modelagem quanto na análise de resultados.

[pehojof]

Scientific truth is monopolized by a few and dictated to the rest.

    

Por favor, se alguém puder confirmar que eu li mesmo essa afirmação, que ela está mesmo ali e que não é uma peça que meus olhos estão me pregando, ficarei muito grato. E, sim, eu vi o contexto muito bem também. Esse "rest" inclui, ou melhor, refere-se especificamente a outros pesquisadores da mesma área! 

Se isso não é uma ditadura que toma espaço sem limites se não for contida de alguma forma, não sei mais o que pode ser. Agora, simulação, ou seja, cálculos, não estabelecem mais só verdades matemáticas, mas a realidade mesma! 

[Buckaroo Banzai]

Se algo é falso, será eventualmente possível que alguém da área pertinente assim demonstre.

Não é "proibido" assumir ser sem fazer essa demonstração, de qualquer forma, embora elucubrações que tomem o aparentemente verdadeiro como falso naturalmente receberão menor consideração.

[pehojof]

Se algo é falso, será eventualmente possível que alguém da área pertinente assim demonstre.

Sim, em quanto tempo?

[Gigaview]

Scientific truth is monopolized by a few and dictated to the rest.

    

Por favor, se alguém puder confirmar que eu li mesmo essa afirmação, que ela está mesmo ali e que não é uma peça que meus olhos estão me pregando, ficarei muito grato. E, sim, eu vi o contexto muito bem também. Esse "rest" inclui, ou melhor, refere-se especificamente a outros pesquisadores da mesma área! 

Se isso não é uma ditadura que toma espaço sem limites se não for contida de alguma forma, não sei mais o que pode ser. Agora, simulação, ou seja, cálculos, não estabelecem mais só verdades matemáticas, mas a realidade mesma! 

Isso nunca foi novidade. A Ciência sempre teve esse lado hermético e nunca foi democrática. A dúvida, se apesar disso, ela continua sendo Ciência é totalmente pertinente.

Outro exemplo pavoroso: https://www.scientificamerican.com/article/do-seed-companies-control-gm-crop-research/

[Gorducho]

Sim, em quanto tempo?

A devido tempo...
Teorias como o calórico;
a teoria "clássica" das radiações (catástrofe do uv);
a mecânica newtoniana a nível da astronomia;
a tese que banhos quentes eram nefastos à saúde...
foram sem problemas mostradas falsas por pessoas das respectivas áreas.
Claro: tem resistências, feudos. Mas via de regra as evidencias acabam se impondo dentro do mainstream.

Agora, se restrições legais em determinada jurisdição impedem que sejam feitas pesquisas científicas, isso é outra coisa.
É proibição de exercício de atividade em determinada jurisdição. Por definição, NADA tem a ver com "Ciência". É proibição de exercício da atividade científica.
Os outros profissionais da área, não comprometidos com interesses em restringir a atividade, devem é tornar públicas a eventual existência de censura à atividade.
Em última análise, num sistema democrático,a solução pra essas distorções cabe à população e seus representantes eleitos. Será um problema político e NÃO científico.

[Gigaview]

Sim, em quanto tempo?

A devido tempo...
Teorias como o calórico;
a teoria "clássica" das radiações (catástrofe do uv);
a mecânica newtoniana a nível da astronomia;
a tese que banhos quentes eram nefastos à saúde...
foram sem problemas mostradas falsas por pessoas das respectivas áreas.
Claro: tem resistências, feudos. Mas via de regra as evidencias acabam se impondo dentro do mainstream.

Adicionando um pouco mais de paranóia...

Quanto tempo???

Em alguns casos pode ser complicado quando a replicação depende de recursos que não estão disponíveis ou estão sob controle de uma minoria que deseja manter o monopólio do conhecimento. Estou me referindo à disponibilidade de imensos aceleradores de partículas, radiotelescópios especializados, super microscópios eletrônicos, computadores quânticos, laboratórios orbitais e de toda a tecnologia desenvolvida pela iniciativa privada em posse de laboratórios farmacêuticos, empresas aeroespaciais, indústria armamentícia, institutos privados de pesquisa, etc. que certamente já possuem conhecimento que não é compartilhado por interesses econômicos.

...

Às vezes a humanidade é beneficiada pelo vazamento de informações. É o caso do transistor (de semicondutores) e circuitos integrados, cuja tecnologia vazou da Área 51 a partir da reengenharia da nave alienígena recuperada em Roswell em 1947. Outras tecnologias decorrentes da Ciência extraterrestre e espiritual ainda permanecem sob segredo como a viagem no tempo (Montauk), controle da meteorologia e de terremotos (HAARP), supressão de tecnologias (Phoebos Cartel), tecnologia avançada de lasers e hologramas para enganar as pessoas (Project Blue Beam) e acobertamento de corpos celestes (NASA/Nibiru/Planet X).

[Gorducho]

que deseja manter o monopólio do conhecimento.

+ aí não se pode considerar como deveras Ciência. Pra nós público, deve ser encarado como arte prática — tecnologia.
Se aparece uma arma biológica, desde o (nosso) ponto-de-vista da comunidade em geral (incluindo a "científica"), isso não será "informação científica" e sim um dado prático acerca duma coisa que existe (o gás ou líquido, digamos) que exerce efeitos catastróficos.
Claro: será "científico" o conhecimento obtido sobre os efeitos dessa AB sobre os organismos obtidos pelas necropsias e estudos efetivados por pesquisadores verdadeiramente independentes. Claro: como isso é difícil de se avaliar (a vera independência e descomprometimento), o certo é ficar c/o pé atrás mesmo e não dizer MUUU!!! pro que é divulgado.

tecnologia desenvolvida pela iniciativa privada em posse de laboratórios farmacêuticos, empresas aeroespaciais, indústria armamentícia, institutos privados de pesquisa, etc. que certamente já possuem conhecimento que não é compartilhado por interesses econômicos.

Desde o ponto-de-vista da (nossa, geral) comunidade externa à organização, não é "conhecimento científico".
Say determinada semente produzida em laboratórios e patenteada sem que os inventores permitam estudos acerca dela...
A comunidade externa vai saber a produtividade dela por ha sob certas condições;
as características do produto germinado (nutrientes, gorduras...).
Isso é conhecimento "científico" acerca daquele produto.
Como ele (as sementes no exemplo) foi desenvolvido não é conhecimento científico pra comunidade externa não comprometida com a organização desenvolvedora do produto/tecnologia.
Não fica comprometido o conceito de "ciência" proper.

[Buckaroo Banzai]

Pelo pouco que sei, patente implica na abertura de "todas" as informações sobre a tecnologia.

Creio que estejam misturando "segredos de negócio" com patentes.

Ex.: colocaram no abacate um ou outro gene animal que faz com que supra todas as necessidades nutricionais de carne, sem ser. Será publicado que gene é esse e etc. Talvez até mesmo o modo exato usado para fazer a inserção dele, bem como o genoma em si.

Pesquisadores poderiam estudar a coisa a vontade, mas esses abacates não poderiam ser reproduzidos sem licença, e provavelmente mesmo a mesma adição feita a bananas, abóboras, ou outra coisa. Mas podem até mesmo desenvolver aperfeiçoamentos não-óbvios e patenteá-los também.

https://en.wikipedia.org/wiki/Sufficiency_of_disclosure

Sufficiency of disclosure or enablement is a patent law requirement according to which a patent application must disclose a claimed invention in sufficient detail for the notional person skilled in the art to carry out that claimed invention. The requirement is fundamental to patent law: a monopoly is granted for a given period of time in exchange for a disclosure to the public how to make or practice the invention.

A idéia é justamente aumentar as chances de progresso tecnológico ao tornar público o funcionamento, ainda tendo no entanto direito exclusivo à exploração comercial da tecnologia por um prazo, em vez de se ver tendo que manter segredo a fim de obter algum lucro.




Uma mistura das duas coisas talvez fosse estes não serem genes animais adicionados a um vegetal, mas "genes inéditos", de alguma forma inventados do zero, mas que produzissem esse mesmo efeito desejado. Talvez tenham desenvolvido algum software mirabolante que meio que darwinisticamente "inventa" genes adequados a um dado genoma tendo como meta um dado resultado, e então eles produzem esse gene sinteticamente e o adicionam. O programa em si talvez possa ser um "segredo de negócio," e deixar todos apenas coçando a cabeça sobre como é que se chega àquela seqüência inédita sob os parâmetros desejados. 

[Gorducho]

Tinha em mente o Outro exemplo pavoroso:, trazido pelo Sr. Gigaview...