Life: one possible abiogenesis narrative (with the help of Monod)

I spent my whole professional life researching healthcare technologies, but I never studied current theories of abiogenesis, the natural process by which life arises from non-living matter, such as simple organic compounds. 

But while reading “Chance and Necessity: Essay on the Natural Philosophy of Modern Biology” by Jacques Monod, I became curious.  I read some stuff, bridged it with some personal narrative.

To be clear, we do not know how life appeared, and what follows is a mere narrative, with a limited scientific basis.

Let us imagine that a unicellular organism appears, but as a random event.  It can duplicate every day and dies after seven days.  After only 100 days, the population of organisms will be 10^30, which is roughly what the Earth's ecosphere can sustain.  But of course, this assumes that there is no selection pressure, e.g., that all organisms die of old age.  If we introduce a death rate of 50%, the population will never develop.  But as soon as the death rate goes below 50%, the population growth explodes.  With a death rate of just under 40%, we reach the ecosystem limit within one year.

My point is that once the first living organism appears (some authors call it LUCA, the Last Universal Common Ancestor), life is inevitable.  But what is life?  This is a particularly difficult question.  Probably a decent tentative answer is that LUCA had a boundary (e.g., a membrane) that separated it from the rest of the environment; a metabolism, which would enable it to capture and convert energy in the environment, to sustain all its functions, and to keep its internal entropy low; and to replicate into another organism with low loss of its information content.

I picture life as the eternal fight between order and chaos, where chaos is the thermodynamic inevitability of entropic death, and order is the persistence of this self-replicating stuff that fights the good fight to prevent chaos from changing itself.  And funny enough, life wins when it fails, mutations occur, and more resilient forms of life are pruned through natural selection.  So, in a way, the best friends of life are death and chaos.

Several abiogenesis hypotheses propose that life emerged on Earth more than 3.5 billion years ago from prebiotic chemistry: a set of chemical processes occurring before the existence of living organisms. In some scenarios, these reactions occurred in environments such as shallow ponds, hydrothermal systems, mineral surfaces, volcanic regions, or other chemically active settings, where simple molecules were exposed to energy sources, including sunlight, geothermal heat, electrical discharges, and chemical gradients.

Among the many elements available on the early Earth, a small group became especially central to life: carbon, hydrogen, nitrogen, oxygen, phosphorus, and sulphur, often abbreviated as CHNOPS. These elements form the backbone of most biological molecules. Other elements and ions, including calcium, magnesium, iron, nickel, zinc, and manganese, also became important in biological chemistry, especially as structural components or catalytic cofactors.

Experiments have shown that several key classes of biological molecules, or their precursors, can form under plausible prebiotic conditions. These include amino acids, sugars, lipid-like molecules, nucleobases, and nucleotide-related compounds. However, the transition from these simple compounds to self-sustaining, evolving chemical systems remains one of the most difficult questions in origin-of-life research.

One influential hypothesis is the RNA World. According to this idea, an early stage of life-like chemistry was dominated by RNA or RNA-like polymers. RNA is a polymer made of nucleotides. Each nucleotide contains a ribose sugar, a phosphate group, and one of four nitrogenous bases: adenine, uracil, cytosine, or guanine. RNA is especially interesting because it can both store information and catalyse chemical reactions. At some stage, RNA-like molecules may have become sufficiently abundant and stable for some folded forms to catalyse reactions, possibly including those that helped generate more RNA-like material.

At the same time, simple lipid-like molecules could have spontaneously assembled into vesicles: small membrane-bound compartments. If RNA-like molecules became trapped inside such vesicles, these structures would have resembled early protocells. These primitive compartments would not yet have been true modern cells, but they could have created local chemical environments in which useful molecules were retained, concentrated, and exposed to selection.

Over time, RNA molecules and short chains of amino acids may have begun to interact. These short amino-acid chains, called peptides, could have stabilised RNA structures and improved their catalytic efficiency. This stage is sometimes described as an RNA–peptide world. In such a system, molecular structures that were more stable, more efficient, or more easily replicated would have become more common. In this sense, a primitive form of natural selection could have acted before the existence of fully modern cells.

Eventually, longer peptides and then proteins appeared. Proteins are chemically more versatile than RNA and have become the dominant biological catalysts. Their emergence would have allowed increasingly reliable replication, more complex metabolism, and more efficient control of chemical reactions. The ancestors of ribosomes probably evolved gradually within this RNA–peptide context. Modern ribosomes still preserve a central catalytic role for RNA: the formation of peptide bonds is carried out by ribosomal RNA, not by protein. This strongly suggests that today’s translation machinery retains traces of an ancient RNA-based past.

DNA probably appeared later. Deoxyribonucleotides are structurally similar to ribonucleotides, but they contain deoxyribose instead of ribose, and DNA uses thymine instead of uracil. DNA is more chemically stable than RNA and is better suited to long-term information storage. Once protein enzymes had evolved, the conversion of ribonucleotides into deoxyribonucleotides could have enabled the emergence of DNA genomes. From that point onward, biological information could be stored more reliably in DNA, transcribed into RNA, and translated into proteins.

Long before the appearance of modern organisms, protocells therefore may have evolved into increasingly complex cellular systems containing genetic polymers, catalytic RNAs, proteins, primitive metabolic networks, and membrane-based energy-conservation mechanisms. ATP eventually became the main energy currency of cells, although the exact timing and nature of early ATP-producing systems, including ATP synthase, remain debated.

After a long period of early evolution, one lineage, or population of related lineages, gave rise to the Last Universal Common Ancestor, or LUCA. LUCA was not the first living organism. It was already a relatively advanced cellular system, with DNA, RNA, proteins, ribosomes, a genetic code, and complex metabolism. It is called “last universal” because all known organisms alive today — bacteria, archaea, and eukaryotes — descend from it. Life as we know it did not begin with LUCA. Rather, LUCA marks the deepest common root we can infer from living organisms today. The origin of life lies further back, in the long and still partly mysterious transition from prebiotic chemistry to evolving protocells.

Maxwell’s demon and the cost of control

In my research on how the central nervous system controls human movement, I stepped into the Uncontrolled Manifold hypothesis proposed by John Scholz and Gregor Schöner, and later popularised by Mark Latash.  I always explained this hypothesis to my students and coworkers by positing that controlling a quantity would entail a metabolic cost, and thus the parsimonious view of evolution led humans to keep control only of quantities that directly affect the motor goal. Control only what is necessary.

But I never had a true theoretical basis for this.  Recently, while reading “Chance and Necessity: Essay on the Natural Philosophy of Modern Biology” by Jacques Monod, I found something that could help. It is related to the so-called Maxwell’s demon.

Maxwell imagined a tiny being that could open and close a small door between two gas-filled containers. It lets only fast molecules pass one way, and only slow molecules pass the other way. This seems to create a temperature difference without doing work, decreasing entropy and apparently violating the second law of thermodynamics.

The modern resolution is this: the demon must measure the molecules’ speeds and use that information to decide when to open the door. The measurement itself can, in principle, be performed with very little cost, but the demon has finite memory. Sooner or later, it must erase the information it has collected in order to continue operating.

Here comes a long train of concepts that I cannot detail, as they are too complex.  In What is Life, Schrodinger proposes that living organisms produce negentropy, e.g. consume energy to keep their internal entropy low, at the cost of increasing the entropy of their environment. In 2009, Mahulikar & Herwig redefined thermodynamic negentropy as the specific entropy deficit of the dynamically ordered sub-system relative to its surroundings.

Shannon proposed that a Gaussian distribution has the highest entropy among all distributions with the same mean and variance (makes sense: if an event is totally random, it is normally distributed). He uses negentropy as the measure of distance, for a given distribution, from the normal one. 

In 1929, Leó Szilárd suggested that the apparent paradox of Maxwell’s demon experiment could be solved if one accepted that the demon's information about the molecule's velocity cost the same amount of entropy that was missing.  More specifically, the so-called Szilárd engine considers Maxwell's set-up, but with only a single gas particle in a box. If the demon knows which half of the box the particle is in (equivalent to a single bit of information), it can close a shutter between the two halves of the box, close a piston unopposed into the empty half of the box, and then extract kBTln2 joules of useful work if the shutter is opened again. The particle can then be left to expand isothermally back to its original equilibrium volume. In just the right circumstances, therefore, the possession of a single bit of Shannon information really does correspond to a reduction in the entropy of the physical system. The global entropy is not decreased, but information-to-free-energy conversion is possible.

In 1953, Léon Brillouin derived a general equation stating that changing the value of an information bit requires at least kBTln2 energy. This is the same energy that Leó Szilárd's engine produces in the idealised case. In his book, he further explored this problem, concluding that any cause of this bit-value change (measurement, a decision about a yes/no question, erasure, display, etc.) will require the same amount of energy.

In fact, one can generalise: any information that has a physical representation must somehow be embedded in the statistical mechanical degrees of freedom of a physical system. Thus, Rolf Landauer argued in 1961, if one were to imagine starting with those degrees of freedom in a thermalised state, there would be a real reduction in thermodynamic entropy if they were then reset to a known state. This can only be achieved under information-preserving microscopically deterministic dynamics if the uncertainty is somehow dumped somewhere else – i.e. if the entropy of the environment (or the non-information-bearing degrees of freedom) is increased by at least an equivalent amount, as required by the Second Law, by gaining an appropriate quantity of heat: specifically, kBTln2 of heat for every 1 bit of randomness erased.

On the other hand, Landauer argued, there is no thermodynamic objection to a logically reversible operation potentially being achieved in a physically reversible way in the system. It is only logically irreversible operations – for example, the erasing of a bit to a known state, or the merging of two computation paths – which must be accompanied by a corresponding entropy increase. When information is physical, all processing of its representations, i.e., generation, encoding, transmission, decoding, and interpretation, are natural processes in which entropy increases through the consumption of free energy.

Applied to the Maxwell's demon/Szilard engine scenario, this suggests that it might be possible to "read" the state of the particle into a computing apparatus with no entropy cost, but only if the apparatus has already been SET into a known state, rather than being in a thermalised state of uncertainty. To SET (or RESET) the apparatus into this state will cost all the entropy that can be saved by knowing the state of Szilard's particle.

I think all this provides the theoretical background to my postulate that control costs metabolic energy. 

 

Mikey D: As a franchising wraps into your personal life history

Yesterday I did something wrong (please do not tell my wife).  I wanted to eat a hamburger. I looked around, and now all the places serve things like “Black Icelandic three-horned beef burger, cream of Pecorino matured in fossa 25 years, beer mayo made with Korean green chicken eggs, DOP blue tomato for only €30”.  When the gentrification exceeds certain limits, my blue-collar soul reacts; I ended up with a Big Mac from the local McDonald's for €7.

With my burger in hand, enjoying a lovely spring day in Bologna, I let my thoughts run free, and for free association, as became usual recently, they landed in the remote past. 

It is 1988, and I land in Gainesville (FL) in my first overseas research job at the University of Florida with Prof Alì Seireg.  I had a big baggage: in the 80s, for the left-wing Italian youngsters like me, the USA was pretty much evil.  Ronald Reagan was finishing his presidential term, and the United States summarised all that we thought was wrong.  But I did not want these preconceptions to bias my experience.  So I reacted by trying to blend in as much as possible, putting an effort into learning the language, befriending locals, and using my spare time to do the most American things possible.  

The first McDonald’s restaurant opened in Rome in 1986 (actually, another opened the year before in Bolzano, but no one noticed). Located in Piazza di Spagna, he caused a tremendous popular reaction.  In a country of food lovers, fast food was the true evil, and having a temple of such evil in one of the most important squares in the country was an insult. It will take ten more years to see a McDonald's in Bologna.

So after my share of baseball games, I had to eat at McDonald’s.  I think I went to the one on University Avenue (picture is much more recent, though).

 

And what I learnt was that the fast-food restaurants were where real people went for lunch.  The food was decent, and you could have a burger for $1 and a Big Mac meal for $2.60.  Even back then, that was little money. Mikey D (as all the kids called it) was a family-friendly place, with small kids playing outside, where even poor families could dine out once in a while. Baseball games taught me that even without soccer, you could have a popular sport where blue-collar families could invest much of their joy (and sorrow). Sleazy bars taught me that in the middle of redneck land, with the gun rack on the pickup truck, you could listen to some of the best blues music.

I came back to Italy with a completely different perception of the United States.  In the late 80s, all the elements of current madness could be seen with a trained eye.  The extreme social injustice, the lack of public healthcare or education, and the racism were clearly visible.  But the American dream was still alive, or at least that’s what it looked like to that very young me version (mini-me?).  It was a tough place, but if you worked hard, you could improve your social and economic standing.  Probably more than the stagnant Italian society could offer, despite its catholic-socialist politics. Well, I was wrong; it did not end well, now we know.  But if I am what I am, it is also because of that experience, which taught me an incurable optimism that stayed with me most of the time afterwards. 

Fast forward to 1998.  I had a job, a family, a daughter, a car and a mortgage. My partner and I tried to be very present parents to our only daughter, who was then four years old.  Being all together was important, we tried to do it as much as possible.  But once in a while, my partner had an obligation or something, and my daughter stayed with me alone for a whole day.  To mitigate the absence of her mother, we would “fare i vizi” to spoil her somehow. And sometimes “i vizi” were to have lunch at McDonald’s, which was now quite popular even in Italy by then.

Many have tried to explain what terribly sad (and at the same time marvellous) business it is to have your children grow up. That little person you pampered and loved so much is gone; she will never come back.  Now there is a self-centred teenager, and now a young adult busy building her own life.  For me, those lunches at McDonald’s are a special memory, she and I, laughing for nothing, inventing silly stories to keep her entertained. So much love.

As a Professor of Bioengineering working on one of the largest Italian projects on disease prevention, I should tell you that eating fast food is bad for your health.  It is true, I never took care of my nutrition, and as a result, I am ageing very poorly.

But not everything we do has to make sense, be healthy, or be reasonable. Sometimes, having a burger can help you bring back two very fond memories, so you smile to the angels while you are eating, and the people around wonder what’s wrong with that old fool.

See you later, alligator

 

Una storia italiana

Ieri, un foglio trovato per caso in un archivio e un articolo su Nature mi hanno fatto tornare in mente una vicenda che ha segnato la mia carriera e, in termini di occasione mancata, la storia della mia città e, forse, del mio paese.  Sono passati 25 anni, durante i quali, se possibile, le cose sono peggiorate; penso sia ora di raccontare pubblicamente questa storia, perché dimostra come una certa miopia istituzionale possa avere impatti importanti a lungo termine sulla ricerca tecnologica.

Ma andiamo con ordine.  Il documento è una richiesta di supporto che inviai nel 2002 a una società scientifica, la European Society of Biomechanics, relativa a una proposta per la realizzazione di un’infrastruttura informatica europea denominata Living Human Digital Library. In quella proposta mi firmo come Fondazione B3C.  L’articolo su Nature, pubblicato il 27 aprile 2026, è intitolato “The arrival of digital twins and in silico trials in drug development”.

Per spiegare come questi due frammenti si collegano, devo raccontarvi anzitutto la mia carriera di ricercatore nel settore delle tecnologie mediche.

Mi laureai in ingegneria a Bologna nel 1988 e, dopo un breve periodo in industria, tornai all’università con una borsa di ricerca per lo sviluppo di metodi computazionali in ingegneria. La borsa prevedeva un periodo all’estero, trascorso negli USA, durante il quale lavorai con il Prof. Alì Seireg. In quel periodo capii che il tema di ricerca che più mi interessava era la bioingegneria, ossia lo sviluppo di tecnologie per la salute umana.  Tornai a Bologna con questa idea fissa in testa, che però non piacque ai miei capi universitari.  Mentre stavo considerando di tornare in America per sempre, l’Istituto Ortopedico Rizzoli, che era diventato da poco un istituto di ricovero e cura a carattere scientifico, mi offrì la possibilità di avviare un laboratorio di ricerca, il Laboratorio di Tecnologia Medica, che, dopo 37 anni, è ancora attivo.

Alla fine degli anni ’90 iniziai a capire che si potevano costruire modelli computerizzati a partire da dati clinici per supportare la decisione medica relativa a quel paziente: oggi li chiamiamo gemelli digitali in medicina.  Dato che il Rizzoli non disponeva di risorse di calcolo, iniziai a collaborare con il CINECA e, in particolare, con il responsabile della sezione di calcolo ad alte prestazioni, Sanzio Bassini.  Sviluppammo un software per la pianificazione della chirurgia dell’anca, ancora in uso, e poi iniziammo ad attrarre finanziamenti della Commissione Europea per i nostri progetti di ricerca.  Non avevamo le idee chiarissime, ma sapevamo che stava succedendo qualcosa di grosso.  Nel 2001 mettemmo assieme un po’ di coraggio e presentammo alle direzioni di CINECA e Rizzoli un progetto ambizioso: la creazione di un centro di competenza sul biocomputing (B3C), organizzato come fondazione, di cui i nostri enti sarebbero stati soci. Fu molto difficile superare le varie diffidenze, ma alla fine sembrava che ce l’avessimo fatta, anche grazie alla lungimiranza di un grande dell’epoca, Achille Ardigò, allora Commissario Straordinario del Rizzoli. Nonostante avesse già 80 anni allora, il Prof. Ardigò era un grande sostenitore dell’informatica in medicina.  

Nel 2002 ci trovammo davanti a un notaio per istituire la Fondazione B3C, quando arrivò il direttore amministrativo del Rizzoli, per informarci che quel giorno era arrivato il veto del Ministero della Salute alla partecipazione di Rizzoli a una fondazione di ricerca.

Per salvare quanto possibile, spostammo il B3C come divisione di ricerca di uno spin-off del CINECA, SCS S.r.l.  Io rimasi al Rizzoli; facevo il direttore scientifico di B3C a titolo gratuito. Dal 2001 al 2011 riuscimmo ad attirare quasi €7M di finanziamenti, quasi tutti europei o provenienti dall'industria, ma nonostante ciò non scalava, perché il centro di ricerca non aveva il giusto contenitore né il supporto delle istituzioni locali e nazionali.  Intanto l’idea della medicina in silico stava crescendo, ed io ero considerato uno dei massimi esperti ovunque, eccetto in Italia, dove il Rizzoli si rifiutava di darmi un incarico apicale.

Alla fine, accettai l’offerta di un’università inglese e me ne andai. Senza di me, il B3C sopravvisse per un paio d’anni, poi le vicende interne dell’azienda madre ne portarono alla chiusura, nonostante avesse un portfolio di finanziamenti pubblici e privati di tutto rispetto.  Bologna avrebbe potuto diventare la capitale europea della medicina in silico, ma non se ne fece nulla.

Avanti veloce.  Dal 2011 al 2018, nel Regno Unito, ho fondato e diretto l’Istituto Insigneo per la medicina in silico, che, quando l’ho lasciato nel 2018, coordinava il lavoro di oltre 300 ricercatori, di cui circa la metà erano professori provenienti da 29 diversi dipartimenti dell’Università di Sheffield o dell’azienda ospedaliera locale. In questi sette anni, Insigneo attrasse £48M di finanziamenti competitivi per la ricerca o di contratti industriali, e si posizionò come il centro di riferimento europeo per la medicina in silico.

Intanto, la medicina in silico, i digital twin e i metodi computazionali per sostituire la sperimentazione animale, forti dei sensori indossabili e dei metodi dell’intelligenza artificiale, rappresentano il futuro della medicina, che sarà dominato da quelle tecnologie che, 25 anni fa, a Bologna eravamo tra i primi al mondo a sperimentare.  

L’articolo su Nature, di fatto, riconosce che queste metodiche sono mainstream e che la loro importanza sta crescendo esponenzialmente. E allora, mentre lo leggevo, mi sono chiesto: cosa sarebbe successo se nel 2002 avessimo avuto a Bologna il supporto istituzionale che poi mi hanno dato a Sheffield? Che impatto avrebbe sull’economia regionale e nazionale se oggi, al Tecnopolo DAMA, accanto al Supercalcolatore Leonardo, ci fosse il centro di ricerca sulla medicina in silico più vecchio e prestigioso d’Europa?

Sono finiti i progetti di ricerca finanziati dal PNRR e tra poco finiranno anche quelli finanziati dal Piano Nazionale Complementare al PNRR.  Molti dei progetti finanziati avevano come focus l’uso delle tecnologie informatiche avanzate in medicina. E le istituzioni di ricerca con sede a Bologna hanno svolto un ruolo significativo in molti di questi progetti. A dispetto della pioggia di milioni di finanziamenti ricevuti, a causa di veti incrociati e di miopie di varia origine, non vedo neanche oggi le condizioni per creare a Bologna un centro di ricerca sulla medicina digitale.  

Sarà la seconda occasione persa per Bologna e per l’Italia.  Ma quante di queste occasioni si presentano?  E quanti altri potrebbero raccontare storie come la mia?  Se il PIL dell’Italia non cresce mai più del 2% all’anno (e in media molto meno) da un quarto di secolo, forse una ragione sono storie come queste?

Marco Viceconti

Le opinioni espresse nel presente contributo sono attribuibili esclusivamente all’autore e riflettono unicamente il suo punto di vista personale e scientifico. Esse non rappresentano necessariamente le posizioni, le politiche o gli orientamenti dell’istituzione di appartenenza, né di eventuali enti finanziatori o organizzazioni con cui l’autore intrattiene rapporti professionali o di collaborazione.

 

Chocolate kings and Angine de Poitrine

In 1975, many Italian boomers like me listened to the song “Chocolate Kings” by the Italian band PFM.  This is the original recording.

Today I was digging in my music collection, and I found it.  In 1975, my English was as bad as any Italian teenager of the time: the pen is on the table, the cat is under the table, and little more.  Now it is a bit better, so listening today, I realised how relevant the lyrics are also in today’s Trumpian USA:

Lyrics:

When I was born they came to free us
to heal our battle wounds
with photographs of big fat mama
the chocolate kings arrived
to feed us full of good intentions
and fatten us with pride
stars and candy bars!
Shirley Temple dipped her dimples
in favorite nursery rhymes
big mama's love was pure and simple
and gentle dollar signs
sang out lullabies
So sorry
her superman is losing fans
and I am so sorry
so sorry
they've packed her bags
they've stacked her flags
and we are so sorry
Her supermarket kingdom is falling
her war machines on sale
no one left to worship the heroes
her TV gods have failed
hope she takes a look in the mirror
while she is on her way home ...
Her supermarket kingdom is falling
her war machines on sale
no one left to worship the heroes
her TV gods have failed
So sorry
her superman is losing fans
and I am so sorry
so sorry
they've packed her bags
they've stacked her flags
and we are so sorry
new you and I know big fat mama
she took us for a ride
but musclemen are out of business
the chocolate kings are dying
you don't wanna waste your life for chocolate heaven
you like to stay alive
like to stay alive

Incidentally, the album was also adorned with two illustrations that I always found quite powerful, especially the one featuring the “fat mama” with the Marilyn Monroe mask.  Interestingly, the chocolate bar image (cover for the foreign market) is credited to David Draper, but the “fat mama” image for the Italian cover, I could not find attribution, although considering the period and label, it is likely to be Cesare Monti.    

The Italian version of the Wikipedia page on the album suggests that it was not well received in the USA and the UK; I do not know whether this is true, but it is credible.  In Italy, it also did not sell well, but for other reasons, I guess. 

The album was published by Dischi Numero Uno, a Milan label established in 1969 by Lucio Battisti, probably the most popular pop singer of the period, and Giulio Rapetti, a.k.a. Mogol, probably the most prolific lyricist of Italian pop music.  In charge of the label promotion was Mara Maionchi, who is now a well-known TV personality in Italy.  All this to say that this label was not an indie run by three revolutionary kids in a garage; the main goal of these people was to make money.  But in 1975, it was absolutely normal to publish music like that, which was extremely controversial. I wish we could say the same for 2026. 

This brings me to the present.  Recently, Spotify published for the first time the list of the most-streamed songs of all time.  I listened to some of them, and I found the lack of originality a common trait.  I started the usual boomer rant on how creative we were in the 70s and 80s, compared to today.  But then, by chance, I tripped on the live performance of the Angine de Poitrine recorded at radio KEXP.  Like everything truly different, you may like it or not.  But I think it is proof that there are young artists who are truly innovative, even today.  And the fact that five million people listened to their concert on YouTube tells something.

The difference is in the music industry. In 1975, Chocolate Kings was produced and published by the Italian discographic industry. Remember also the scene in Bohemian Rhapsody, the 2018 biopic film about Freddie Mercury, when they convince their producer to fund a concept rock album inspired by Italian opera (incidentally, A Night at the Opera was also published in 1975).

Today, you can find space in the music industry only if your "product" conforms to the average taste of some specific market segments.  This makes the next move easy: AI-generated music is rapidly flooding streaming platforms, with roughly 75,000 new AI-created tracks uploaded daily as of early 2026. 

The answer, in my opinion, is live concerts and self-production.  Fuck the industry and Long Live Rock.

 

Gli anziani e la casa (dei figli)

La Fondazione del Monte ha finanziato un’indagine sulle condizioni abitative della terza età a Bologna e provincia i cui risultati sono stati presentati mercoledì scorso. 

Un dato emerso da quello studio che ho trovato eclatante è che il 74% degli intervistati (over 60) vivono in una casa di proprietà. Peraltro questo è in linea con le percentuali a livello nazionale.

Un altro più preoccupante è che pochi degli anziani intervistati sono pronti ai cambiamenti della condizione abitativa che la fragilità potrebbe imporre. 

Sentendo la presentazione di questo ottimo studio, ho iniziato a seguire una linea di pensiero e con l'aiuto di un po’ di tecnologia  AI ho raccolto qualche dato. I numeri che riporto li ho recuperati con ChatGPT ma senza fare quella verifica delle fonti che è sempre necessario fare in un contesto scientifico; quindi vanno presi con beneficio di inventario.

Tre quarti di quelli che oggi hanno > 60 anni possiedono la prima casa. Il dato nazionale è simile. 

40 anni fa, nel 1985, questi avevano tra i 20 e i 50 anni. Stando nel mezzo, circa il 50% degli italiani sotto i 40 anni era già proprietario di una prima casa. 

Oggi solo il 25% degli under 40 vive in un alloggio di proprietà. Un fattore di questa diminuzione è il calo del reddito medio procapite: Il reddito procapite medio dal 1985 al 2025 è calato di circa un 10% (normalizzando per euro 2025). Ma soprattutto, io credo, il calo è legato alla forte precarietà occupazionale, che rende difficili gli investimenti di lungo termine come la prima casa.  

In un paese "normale" il 50% dei nostri figli avrebbe già una casa sua, il che consentirebbe a tanti anziani di "usare" la propria casa per assicurarsi una vecchiaia migliore.  Oggi la soluzione più usata è quella della nuda proprietà, in cui con i soldi che predi ora ti paghi la badante.  In altri paesi ci sono sperimentazioni più interessanti in cui cedi la tua casa ad un fondo pubblico-privato, che in cambio ti assicura una condizione abitativa adeguata alle tue condizioni di salute per tutta la vita residua, iniziando con il senior cohousing, fino alle varie forme di residenza sanitaria assistenziale, fino all'hospice.

Invece in un paese dove solo il 25% dei nostri figli possiede un tetto sopra la testa, è chiaro che la nostra casa diventa un bene da proteggere a tutti i costi, per assicurare che almeno a 50-60 anni, quando decediamo, una casa ce l'avranno.

Tutto questo per dire che le società sono un organismo e pensare di poter risolvere solo i problemi di un pezzo di tale organismo è sbagliato.  Se hai nostri figli non viene assicurato un minimo di futuro, come potremo noi, anche se vecchi e decrepiti, prenderci cura solo di noi stessi?

La darwinizzazione liberista del mercato del lavoro ha prodotto enormi danni sociali a fronte di una sostanziale stagnazione del PIL negli ultimi 20-30 anni.  E a pagarne il prezzo non sono solo i giovani, ma anche, indirettamente, gli anziani, per i motivi di solidarietà generazionale di cui parlavo sopra.

Le organizzazioni caritatevoli, il terzo settore e gli enti locali stanno facendo del loro meglio per mitigare gli effetti di questa “tempesta perfetta”, come si intitolava un libro del 2015 scritto dai colleghi dell’Università Cattolica del Sacro Cuore, Ricciardi, Atella, Cricelli e Serra, in cui si prediceva l’impatto che l’aumento della vita media avrebbe avuto sul sistema sanitario nazionale.  Ma se non si riesce a rifondare il patto sociale, affinché assicuri a buona parte delle giovani famiglie la possibilità di farsi un mutuo prima casa, il futuro sarà molto duro sia per i giovani che per noi vecchi.

Credibility assessment of predictive biophysical models: a well-established practice

A recent post by @BryceKillen showed that there is still much work to disseminate the mature regulatory science that is now well-established for biophysical models among biomechanics researchers.  What follows are some reflections I hope you will find useful; if any editor of a biomechanics journal thinks this debate is useful, I could make a longer version as publishable commentary. 

A good starting point for those interested is the Open Access book “Toward Good Simulation Practice”, which we published a few years ago.  I will pick freely from it, and in particular from Section 2, “Theoretical Foundations of Good Simulation Practice”.  There, we explain that models can be used in science:

– as tools used in the development and testing of new theories

– as tools for problem-solving

The first case is more complex, but it is rarely the scope of biomechanical modelling; let us just say that in that context, there is no model credibility, but only models (and thus theories we used to build them) that are falsified (or not) by experiments. When a model is used for problem-solving, we talk of the model’s credibility. The two cases should never be confused.

If we now restrict ourselves to problem-solving, there is a well-established body of knowledge, both theoretical and empirical, that all biomechanics researchers using predictive models should be familiar with. In addition to the mentioned book, probably the best source is the ASME VV40:2018 technical standard “Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices”. While the standard focuses on medical devices, ample literature and various regulatory authorities have acknowledged its general validity for any biomedical problem-solving application involving first-principle models (models based on prior mechanistic knowledge).

A first key step to assess credibility is to define the context of use. In fact, a model is never credible in general but with respect to a specific context of use. An important feature of a context of use is the acceptability threshold, defined as a norm and a value. The norm is necessary to reduce the Quantity of Interest (QI, what we want to predict) to a scalar, and the value is the maximum acceptable error according to that norm, for that specific context of use. 

This assumes that, at least in certain conditions, I can measure the QI with an accuracy that is at least one order of magnitude higher than the acceptability threshold for the predicted QI; this makes it possible to assume the measured values as true values and assume that all differences with the predicted values are prediction error (validation).  When this is not possible, things get tricky (but this discussion is beyond the scope of the comment).

My model predicts the QI as a function of certain inputs.  If, as in most cases, at last one of the inputs is a continuous quantity, even within the limited range of admissible values (input space), there are infinite inputs possible.  We define a model as credible if its prediction error is less than the acceptability threshold for any possible valid input.  To demonstrate this, one should proceed by induction, computing the prediction error for a very large number of valid input sets covering the entire input space.  Unfortunately, models are used when measurements are difficult to achieve, so in most cases, there is a scarcity of experimental true values to compare with. 

Enters the Verification, validation, uncertainty quantification and Applicability Analysis (VVUQA).  This developed as an engineering practice, but since then has received theoretical foundations (e.g., https://doi.org/10.1109/JBHI.2019.2949888). The idea is that even if I do not have enough validation experiments to demonstrate the credibility of a model by induction, I can still assess its credibility by decomposing its prediction error among its various sources and confirm that each error component behaves as expected. This supports the assumption of regularity necessary to assume that the prediction error obtained with a finite set of validation experiments is good enough to assess the credibility of the model.

Biophysical models are affected by approximation (numerical), aleatoric and epistemic error.  The VVUQA process first calculates the overall prediction error over the available validation experiments and confirms that such a value is smaller than the acceptability threshold. Then, various techniques can provide upper boundaries for the approximation error, so as to demonstrate that such an error component is negligible compared to the other two. Uncertainty quantification methods aim to demonstrate that the aleatoric component of the error is normally distributed with a mean close to zero, which allows us to assume a mean norm, such as a Root Mean Square Error, as a measure of the epistemic component of the prediction error. Last, applicability analysis looks at how the prediction error varies with the input values, and from that decides how much we can trust predictions made for input values far from those we tested in the validation experiments.

With this well-established practice, biomechanics researchers can assess if the predictive accuracy of their model is good enough for a defined problem-solving context of use.

 

Da Ciao 2001 a Vice Magazine

Da quando avevo 10-11 anni e per buona parte della mia vita, il mio soprannome è stato Vice, o Il Vice.  Viene dal mio cognome, Viceconti, ma è un bel soprannome, carico di significati secondari.  A un certo punto persino molti dei miei famigliari mi chiamavano Vice. L’ho dismesso quando sono andato a vivere in Inghilterra, dove il primo significato per la parola “Vice” è vizio, e come soprannome non mi sembrava carino, ma gli amici storici lo usano tutt’ora.

Vi racconto questo perché alcuni mesi fa ho scoperto Vice Magazine, una rivista in lingue inglese di controcultura giovanile pubblicata a partire dagli anni 90. Trovate la sua storia in questa pagina di Wikipedia.  Nel 1994, quando Vice Magazine nacque, nacque anche mia figlia, ed io non avevo certo il tempo o l’interesse di leggere riviste di controcultura.  La cosa mi è tornata in mente oggi quando un amico mi ha mandato il link ad un articolo intitolato “Si stava meglio quando c’era Vice”, che parla appunto della rivista, ma visto il mio percorso di pensionamento, gli si può dare anche un altro significato.

Leggere di Vice Magazine mi ha fatto ricordare il ruolo fondamentale che alcune riviste hanno avuto nella mia formazione: Ciao 2001, Il Male, e Frigidaire.  

Non ricordo quando ho iniziato a leggere Ciao 2001, ma direi attorno al 1975. In quegli anni Ciao 2011 era l’unica rivista con distribuzione nazionale in cui si parlava di musica rock.  L’altra rivista storica, il Mucchio Selvaggio, nascerà alcuni anni dopo, e negli anni della mia giovinezza trovarla era quasi un’impresa, per chi come me abitata in provincia.  E che provincia: la bassa ferrarese degli anni 70 dove sono cresciuto distava anni luce dal mondo della scena rock internazionale che Ciao 2001 raccontava. 

Grazie a Ciao 2001 ho imparato tante cose su una serie di gruppi rock di cui ancora non avevo sentito una sola canzone, dato che i dischi erano ancora più difficili da trovare che le riviste.  Per trovare dischi di musica un po' alternativa (nel 1976 Sanremo fu vinta da Peppino di Capri, quindi praticamente tutto era alternativo) bisognava andare a Bologna, o addirittura a Milano, luoghi lontanissimi e quasi irraggiungibili.  Grazie a Ciao 2001 però leggevo di questi gruppi musicali straordinari, e in ogni numero la doppia pagina centrale era un poster con una foto di un artista, di solito durante un concerto.  Presto la mia stanza fu tappezzata di questi posterini.  Quando poi finalmente la musica internazionale mi divenne accessibile, e potei ascoltare tutti questi artisti, era come se li conoscessi da sempre.  Ciao 2001 mi aiutò a superare l’isolamento culturale della bassa ferrarese, facendomi sognare di girare il mondo, come poi ho fatto.

Il Male uscì nel 1978 e in breve tempo divenne un fenomeno importante della controcultura italiana. Era chiaramente ispirato a Le Canard Enchaîné, che veniva pubblicato dal 1915, ma noi non lo sapevamo, e quindi per noi il Male era qualcosa di assolutamente nuovo. Il motto del Canard è "La libertà di stampa si consuma solo quando non si usa" e il Male quella libertà la usò fino in fondo, testandone i limiti.  Tra tutte le invenzioni, ne cito due. Convinsero Ugo Tognazzi e Raimondo Vianello, allora famosissimi, a partecipare ad una gigantesca goliardata che si materializzò con la riproduzione dentro al Male delle prima pagine di alcuni quotidiani nazionali che annunciavano che Tognazzi era il capo delle Brigate Rosse.  Come decine di altri adolescenti, lasciai il bella vista la prima pagina finta di Paese Sera sul tavolo di cucina, e a mio padre venne quasi un colpo.

Era Maggio del 1979; un mese prima il 7 Aprile 1979, erano stati arrestati i maggiori leader di Autonomia Operaia, tra cui Toni Negri, Emilio Vesce, Oreste Scalzone e Lanfranco Pace.  La questione del Processo 7 aprile, come poi venne chiamato è storicamente molto complessa, ma anche chi come me non aveva alcuna simpatia per Autonomia Operaia sentiva puzza di reati di opinione.  Lo “scherzo” del Male non fu quindi banalmente una goliardata ma un modo per far riflettere sul rischio di criminalizzare le opinioni invece delle azioni.

La seconda è stata talmente pesante anche dal punto di vista legale, che non sono riuscito a trovare online la vignetta originale. Durante l'Angelus del 10 settembre 1978, l’allora Papa Giovanni Paolo I (Albino Luciani) affermò che ““Dio è papà; più ancora è madre”. Nel numero successivo del Male c’era una vignetta che ritraeva un Dio piuttosto arrabbiato che prende il Papa, lo infila sotto al sottanone e grida: “ciuccia un po’ e dimmi se sa di latte!”.  Si fa molta fatica a trovare notizie online su questa storia, ma se ricordo bene il giornale fu sequestrato per vilipendio alla religione.  Per un diciasettenne come me, il Male apriva la porta ad una satira senza sconti: si può ridere di tutto, e i veri democratici sanno accettarla.  Sandro Pertini venne sfottuto da Andrea Pazienza in una vignetta sul Male: Pertini si fece due risate e invitò Pazienza al Quirinale per pranzo.

La rivista Frigidaire fu fondata da Sparagna, Scòzzari e Tamburini nel 1980.  C’erano un po' di cose sulla musica, un po' di inchieste, ma per me il cuore erano i fumetti.  Ne cito due: RanXerox e Zanardi.  Il primo scritto da Stefano Tamburini e disegnato da Tanino Liberatore, con qualche aiuto da Andrea Pazienza, che invece era autore unico di Zanardi. La prima storia di Zanardi esce su Frigidaire nel 1981, e nel 1988 Pazienza muore. Raccontare cos’era Bologna negli anni 80, richiederebbe un capitolo a sé, e poi è già stato fatto da tanti più titolati di me.  Il DAMS, Pazienza, Freak Antoni e gli Skiantos, il Gran Pavese.  Per me, appassionato di fumetti di supereroi fin da bambino, la scoperta di personaggi come RanXerox, Zanardi, o Ramarro, il supereroe masochista di Giuseppe Palumbo, apriva nuovi orizzonti.  Dopo la pesantezza degli anni 70 catto-comunisti, si parlava con allegria di sesso, droga e rock’n roll. Non andrà a finire bene: l’AIDS per il sesso, l’eroina per la droga e anche il rock non sta tanto bene.  Ma in quegli anni era tutto bellissimo.  Io quando non studiavo vivevo allo Spleen, circolo ARCI di Copparo dove facevamo rock demenziale, teatro comico, e gigantesche feste disco.  La controcultura che Frigidaire rappresentava è stata essenziale per me in quegli anni.

Stamattina ho letto un articolo che si leggono sempre meno libri e anche sempre meno riviste.  Forse non è così grave come sembra, forse i modi di consumare cultura e informazione stanno cambiando.  Ma sicuramente, per me almeno, Ciao 2001, Il Male, e Frigidaire hanno contribuito molto all’adulto che sono diventato.

 

2025: clinical trials are 1000 years old. Time to change them?

Abū ʿAlī al-Ḥusayn bin ʿAbdullāh ibn al-Ḥasan bin ʿAlī bin Sīnā al-Balkhi al-Bukhari is the formal name of Ali ibn Sina, known in Europe with his Latinised name, Avicenna.  In 1025, he completed The Canon of Medicine, an encyclopedia of medicine in five books, which remained a fundamental reference in any decent medical library for centuries, in one of the many translations from Farsi (Persian language).

However, one element of the Canon survives nearly untouched after 1000 years: how to assess the efficacy of a new treatment through experimentation, in short, clinical trials.  The seven conditions Ibn Sina proposes are purity of the drug, testing for only one disease, use of a control group, use of dose escalation, need for long-term observation, need for reproducible results, and the need for a human in addition to animal testing.  These are pretty much still the cornerstone concepts of contemporary clinical trial design.

The epistemological perspective of Ibn Sina was what we would today refer to as frequentist inference.  This worldview, mainly rooted in social sciences, relies on a few fundamental assumptions (epistemologists will forgive me a certain degree of oversimplification):

• Experimental observations can rarely be quantitative and are always biased (i.e., affected by systematic errors).
• Truth exists but is hidden.
• Experimental observations are collectively informative, whereas prior knowledge is considered scarcely informative; decisions are based solely on the likelihood of observing the data under various assumptions.
• Decisions are reduced to a binary choice between H0 (null hypothesis), which is the choice we would make in the absence of any experimental observation, and the alternative hypothesis H1, which is the opposite of H0.
• By default, we assume H0 to be true; we change our decision to H1 if there is overwhelming experimental evidence that H0 is false.

The key concept here is prior knowledge.  Prior knowledge is what we believe to be true before we start the experimental observations.  There are other domains of knowledge, such as physical sciences, where it is believed that we can provide causal explanations in the form of prior knowledge for some manifestations of reality. In such cases, prior knowledge can indeed be informative. This worldview can be summarised in the Bayesian inference, which assumes:

• Most experimental observations are expected to be quantitative, and our measurement methods are nearly free of systematic errors.
• Truth does not exist: there are beliefs (hypotheses that someone believes are true) that are more or less probable.
• Every hypothesis has a posteriori probability, defined as the product of the probability derived from the a priori knowledge available (prior) and that derived from experimental observations (likelihood).
• The hypothesis with the highest probability is used to make a decision.

The fact that two fundamentally different and largely incompatible worldviews coexist in science has always fascinated me. But it makes sense: in social science, very little can be "measured", and no one expects any "law of sociology" to exist any time soon (Interestingly, this has not always been the case: look at the concept of psychohistory in Isaac Asimov's science fiction novels). On the other hand, in physics and engineering, we trust we can observe experimentally natural phenomena in ways that yield reproducible, reasonably unbiased quantifications.  From these, we can formulate sufficiently universal causal explanations that, when resisted by extensive attempts of falsification, are considered "laws of physics". Within their limits of validity, we expect the laws of physics to be universally true; thus, we can use them as reliable prior knowledge in decision-making. 

The birth of modern physics is usually placed between 1543 (publication of Copernicus' "De Revolutionibus Orbium Coelestium" on heliocentric model) and 1687 (publication of Newton's "Philosophiæ Naturalis Principia Mathematica", unifying law of motion and universal gravitation). Maybe being biased by my being Italian, I like 1638 (publication in Leiden of "Discorsi e dimostrazioni matematiche intorno a due nuove science", The Discourses and Mathematical Demonstrations Relating to Two New Sciences, Galileo Galilei's final book.  Whatever the date, this is some six hundred years after ibn Sina canon.  And, to be fair, Galileo's new science dealt mostly with falling stones, which are much simpler than living organisms.

Thus, I dare to say that ibn Sina was right in conceiving the testing of new treatment as the purest frequentist investigation. So were the many medical researchers followed him through the centuries, always using the same approach.  For centuries, investigating human health and diseases was quite close to social science: little could be measured, every observation was inevitably biased, and prior knowledge had little informative value.

I teach my students that the music changed in 1943 when renowned physicist Erwin Schrödinger was invited to give a course of public lectures at Trinity College in Dublin. Schrödinger was already world-famous for his quantum theory work, so it surprised everyone when he announced that the course would be titled "What is life?".  In 1944, he published a book from these lectures entitled "What Is Life? The Physical Aspect of the Living Cell".  Schrödinger's ideas apparently had zero impact on biomedical researchers. Still, the seed was planted: "How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry?". Or, in our words, can we use the laws of physics and chemistry as prior knowledge to investigate the efficacy of new treatments better?

It was a long and winding journey from Schrödinger's book to the Avicenna Research Roadmap, which postulated the possibility of using in silico methodologies to better investigate the efficacy of new treatments by leveraging on a large body of prior knowledge developed by physiologists, biophysicists and bioengineers, starting from the seminal work of Hodgkin and Huxley on the action potential of neurons, published in 1952.  But today, the body of prior knowledge about the causation of human pathophysiology phenomena which resisted extensive falsification is substantial.  Why should we not use it also for the assessment of new treatments? 

The clever combination of computer modelling and clinical experimentation offers alternatives to animal experimentation, the reduction of the cohorts to be enrolled in clinical trials, a drastic reduction of the attrition rate (number of new drugs that fail to show safety and efficacy), reduce drastically the time to market, and the costs of development.  Why are we resisting such innovation?

My explanation is that most experts working for drug regulatory agencies share the classic frequentist inference worldview; moving to a Bayesian inference worldview is a vast cultural change, a true cultural revolution that scares many practitioners.  But as we celebrate the 1000 years of the Canon of Medicine, I beg all stakeholders to stop postponing this radical change.  Like all revolutions, it will be painful and ridden by mistakes, but ultimately, it is worth the risk.

In Silico World is over; long live In Silico World

Five minutes ago, we uploaded the last contractual deliverable on the EC portal; tomorrow, the In Silico World project will come to its planned end after four years of intense activities.

This is, for me, an emotionally overloaded end for several reasons.  We started writing the ISW proposal in 2019 as I had just returned to Italy after seven years as Director of the Insigneo Institute for In Silico Medicine at the University of Sheffield, UK.  For the third time in my life, I was starting a research group from scratch, but I was also going back to full-time research after years of serving in a leadership position that left little time for my own research interests.  

I had put a lot of energy into the Avicenna support action and the relative research roadmap on In Silico Trials, and I was delighted that after a long gestation in 2018, the Avicenna Alliance was finally established to give all companies involved in this emerging sector the possibility to speak with one voice.  However, the Avicenna roadmap's vision was not becoming a reality, at least not with the speed we hoped. 

I had experience, time (as my new research group was slowly forming and my teaching had not started yet), and a clear goal in mind. The missing element arrived when the EC published, as part of the H2020 work programme, the call for proposal "SC1-DTH-06-2020: Accelerating the uptake of computer simulations for testing medicines and medical devices".  Leveraging on 30 years of networking, I quickly formed the core consortium, inviting only people I had already worked with in the past and that I trusted unconditionally. We got excellent scores from the reviewers, and we got funded.

It was a fantastic journey; ISW was the project of my maturity, everything came easy, and the management office I formed to help me run the project was excellent.  I am proud of our results; I genuinely believe that ISW has profoundly impacted the adoption of In Silico Trials in Europe, something not every EU-funded project can say.

This end is more emotionally loaded than the beginning.  The end of the ISW project overlaps with the end of my active research career; as I explained in another blog post, I will not apply for any further research funding and will focus the remainder of my career before retirement on teaching.  So, ISW will be my last EU-funded project.

As the first events to present FP10 are being announced, I cannot avoid returning with the memory to the end of FP4, when I got my first tiny EU grant funded. If I had a successful career, this is mainly due to the European Framework Program funding system, which allowed me to conduct independent research for over 30 years, even when my national environment would have made it impossible.  Having my own funding gave me the freedom I wanted if not a fast career progression (meritocracy has never been a thing in Italy). 

The European Union and its Framework Program funding system sustained my research, advanced my career, and allowed me to work and befriend amazing people all over Europe and, to a lesser extent, even abroad. It forced me to structure my scientific curiosities into a vision I pursued relentlessly for 25 years or so. It exposed me to very different cultures, giving me a healthy relativism about the “right” way to conduct yourself.

So, thank you, European Union, with all my heart.  I am, and I will be until I live, a European Citizen.

From the tyger to the dying of the night

Around 1996, I chose a stanza from the poem The Tyger by William Blake as my email signature: I already blogged in the past about this: The Tyger is back. Here it is:

    Tyger Tyger, burning bright,

    In the forests of the night;

    What immortal hand or eye,

    Could frame thy fearful symmetry?

Here, you can find the full poem.

What fascinated me in that verse was the "fearful symmetry", that emotion a young engineer like me experienced looking closely at living organisms. 

I used it for 15 years, dropping it when I moved to the University of Sheffield.  In 2018, when I returned to Bologna, the temptation was too strong, and I put it back as part of my email signature.

Today, I am going to replace it.  My new signature citation will be:

    Do not go gentle into that good night.

    Rage, rage against the dying of the light.

    Do not go gentle into that good night, Dylan Thomas, in  Collected Poems (1952)

It is the closing verse of a poem Dylan Thomas wrote in the form of a Villanelle, a nineteen-line poetic form consisting of five tercets followed by a quatrain. The full text can be found here.  The sentence "Rage, rage against the dying of the light" closes every other stanza.  This reminds me of one of the folk songs I loved most when I was young, Riturnella, in the version of Musica Nova. It is a traditional from Calabria, recovered in the seventies.

But the reason for this change is another.  I am entering a time in my life when you are constantly confronted with death.  That of your parents, your youth's hero, and your own. It is something new for me; trust me, it is tough.  Now, I suddenly understand something I read years ago: "all elders have a baseline of depression".  Yes, sure, I can see it now.

You need to fight it; you cannot abandon yourself to it. But how?  Well, today, I found a citation to Dylan Thomas's poem, and I had a lamplight moment: considering who I am, the best way is to be pissed off about it. So this is my plan: I do not want to go gentle into that good night; I will rage, rage against the dying of the light.

And since I am still spending half of my life reading and writing emails ( I need to do something about this as well), the best place to remind me of this is my email signature, right? 

That's done, and I also added to my daily playlist a good song from Rage Against the Machine.

done.  Let's rage.

Does interdisciplinary science truly exist?

This is a rhetorical question; the answer is, of course, “yes” because there are researchers who stray out of the confines of their specific scientific domain and wander out toward another scientific domain, ending in that no man’s land so dear to Michel Foucault. 

The In Silico World consortium has recently released in open access a report entitled “Regulatory barriers to the adoption of in silico trials”, which summarises years of work on this specific topic.  In chapter 4 of this report, entitled “A reflection on interdisciplinary decision-making”, we debate with a very narrow focus the complex issue of recognising justified true belief in an interdisciplinary domain”. But from it, we drew a more general reflection that we propose here.

Suppose we accept Thomas Kuhn's idea that in each scientific domain, periods of cumulative progress are periodically interrupted by periods of revolutionary science when the whole domain jumps from one paradigm to another. In that case, we see how interdisciplinary scientists are heralds of a particular class of paradigm change, modifying the confines of that scientific domain (or creating a new one). 

But these researchers travel in dangerous, unexplored lands. The biggest risk is the lack of well-tested pragmatic epistemology. There are many reasons why science fractions knowledge space into scientific domains.  A particularly important one is the need for a pragmatic, operational interpretation of the process through which a group of peers agree on a particular belief.  The best way to collectively decide when a belief is a Justified True Belief varies considerably over the knowledge space.  The ways physical and social sciences decide what a Justified True Belief is are very different and form what we call the pragmatic epistemology of each subdomain of science.

The scientific method is unforgiving; in particular, if the peers of a scientific domain choose an ineffective pragmatic epistemology, the result will be that they will tend to assume as true (and build upon it) tentative knowledge that is then falsified at a later stage. Everything produced using that tentative knowledge must be trashed when this happens, and the overall cost can be very high.  So eventually, the peers of each scientific domain develop a set of operational rules tempered at the fire of experience to decide how, in that specific knowledge territory, a Justified True Belief is considered as such.

If you are conducting interdisciplinary research, you are wandering out of the comfort zone of your domain of origin.  For example, say you are a physicist who has started exploring particular aspects of living organisms, a topic traditionally within the remit of biology.  The pragmatic epistemologies of biology and physics are profoundly different, and rightfully so. Our physicist is faced with two choices: either continue to be coherent with his/her domain of origin and use the pragmatic epistemology of physics or adopt that of the host domain, biology.  The first option is usually healthier: not having been trained as a biologist, the choice to adopt the pragmatic epistemology of biology can be challenging for a physicist (and vice versa, of course).  But in both cases, the choice is always poor.  That is because that researcher is not investigating physics or biology but a new knowledge domain (let us call it biophysics) for which a well-tested pragmatic epistemology is not yet available. 

Interdisciplinary research is precious because it expands the scientific enquiry to previously untouched portions of the knowledge space. However, we must accept that it operates in difficult conditions from an epistemological point of view, and the quality of the knowledge produced might not be as high as that provided by other, more established domains. It also requires a very special mindset, which is less rigid and more accepting. However, the biggest challenge is the need for epistemological attention. Most scientists are uninterested in how knowledge is produced from a philosophical point of view; like cops, they apply the existing laws with little interest in how the legislative process works.  Interdisciplinary scientists do not have this luxury; they need to understand the philosophical basis of their work and spend time debating how to decide what is true.

“Does interdisciplinary science truly exist?” ©2024 by Marco Viceconti is licensed under Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0).

This opinion piece was published in my personal blog, and as an Open Access document on Zenodo.

European Union: from the land of rights to the land of rules

As usual, this blog post is inspired by a series of events, conversations, news, and opinions I collected in the last few weeks, which condensated the need to share an opinion piece through this channel publicly. As always, for everything posted in this blog, these are my personal opinions, which do not necessarily represent the official views of my employer or any of the organisations I am affiliated with in various roles.

The topic is complex and politically sensitive. It is also very general, dealing with how the European Union produces its policies. My narrow perspective refers to the specific domain of in silico medicine technologies. I am not qualified to generalise, but I suspect the problem I am describing below is not specific to a particular area of innovation.

I always have been a champion of the European Union. Compared to the USA, the European Union has been, for many years, the land of rights. Coherent with the values of the majority of its citizens, we created a society where healthcare and education are rights, citizens' privacy is protected, and so on. I am very proud of this. But I am afraid the last few years transformed the EU into the land of rules and bureaucracy. This is crippling our ability to innovate and compete, I believe.

The introduction of in silico medicine is revolutionising healthcare. The ability of computer models, built with advanced techniques such as Artificial Intelligence, to predict changes in the health state of individual subjects opens amazing opportunities but also, as is always the case with disruptive technologies, a whole new set of potential risks for individuals and society. The differences in recent policy-making between the USA and the EU are creating dramatic disparities between researchers and companies working in this field.

When the General Data Protection Regulation (GDPR) was introduced in 2016, many, including myself, praised the European Commission. Eight years ago, it was already evident that information technology companies were abusing the personal data they collected, health information being very sensitive, which was already having a major impact on my research domain.  However, as the GDPR was enforced, biomedical researchers in some member states faced huge difficulties.  The idea of demanding the member states define the rules under which exceptions to the GDPR could be made for scientific research, while it was probably a smart political move to achieve complex legislation within a reasonable timeframe, created a nightmare in which every member state has different rules and multicentric studies across member states are very difficult.  I am sure the legislators had no intention to prevent ethically robust biomedical research, but de facto, this is what has happened. 

This story is well known to all operators in the field and shows, in my opinion, a pattern.  The European Parliament decides to legislate on a socially relevant topic, which has significant implications for complex innovations, usually technological.  Technical advisors are mobilised to support the legislative process, and the legislation tries its best to capture all possible implications of such innovation and how the legislation will impact the socioeconomic processes that involve such innovation. The results are large, complex legislations aimed to “cover all bases”.

However, for disruptive innovation, no group of experts, no matter how good, can foresee all the special cases and all the implications that such extensive policies can have. And the more complex the legislation is, the higher the chance it will produce unforeseen and undesired effects.  In many cases, the issues are related to the correct interpretation of the law and its application into procedures that streamline whatever legal process economic operators need to do to comply with.  The problem is that no one is in a position to handle this post-legislative process.  The European Parliament cannot manage the application of its laws, and the European Commission does not always have the in-house technical expertise necessary to do this.

I suggest that for such complex pieces of legislation, a federal agency should be appointed to oversee the application of such legislation, providing guidelines addressing concrete cases as they emerge, offering support and training, and uniforming the procedures across the union.

In silico medicine, particularly In Silico Trials, has been one of these disruptive innovations.  In the USA, where such federal authority exists (Food and Drug Administration, FDA), the first guideline on using computer modelling and simulation in the certification of medical devices was published in 2016.  The Medical Device Regulation (EU 2017/745) acknowledges the growing role of software artefacts in medical products. However, in 2024, we still do not have a guideline that assists notified bodies in deciding when evidence obtained in silico can be accepted.  The only technical standard for in silico methodologies is the ASME VV40:2018, whose production was driven by the FDA, which recognised the need.  Only this year, the IEC-ISO started a workgroup on the topic that will produce an EU-harmonised standard in some years, but this is only because the community of practice has lobbied for it.  When a new in silico methodology for all types of medical products is developed, one can ask the FDA for qualification advice to explore if such methodology suits regulatory purposes. In Europe, the European Medicine Agency (EMA) provides this only for drug development tools; nothing is available for medical device development tools.

Such a condition is reversing the flow: historically, medical companies would certify their products first in the EU and then in the USA. Today, for in silico solutions, many companies choose the opposite strategy and start with the USA regulator, ensuring clear, reliable regulatory pathways for such products.

In front of us, we have the application of the Artificial Intelligence Act in the next three years and the completion of the legislative process for the European Health Data Space regulation.  Both pieces of legislation could have a tremendous impact on health technology innovation. Without central authorities with the technical skills to provide guidelines on these new legislations, the EU will face the same shortcomings as the MDR and GDPR.  

“European Union: from the land of rights to the land of rule” ©2024 by Marco Viceconti is licensed under Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0).

This opinion piece was published in my personal blog, and as an Open Access document on Zenodo.

“European Union: from the land of rights to the land of rules” © 2024 by Marco Viceconti is licensed under Creative Commons Attribution-NoDerivatives 4.0 International.

How to gently retire

It has been two years since I have posted on this blog.

The reason is simple: 2023 and 2024 have been crazy, so busy that I could never find the time to let my mind go free and produce stories that, in my humble opinion, are worth to be shared.

I hope things will improve in 2025, primarily because I am starting to implement my "Gentle Retirement" plan.  I decided to write about such a plan for two reasons: the first is that one element of this plan will drastically reduce my attendance at conferences and other similar events, so I want to inform my many friends and colleagues who will not see me anymore about my future.  The second and most important is that all my closest work friends are in their sixties, or close to that,  so I am sure they are all wondering the best way to retire.  I am not sure mine is the best, but it might be worth sharing how and why I plan to approach this final passage of my professional career.

First, I will give a brief history of myself to contextualise this.  After a short but fundamental period in the USA working with Prof Alì Seireg (see this article on one of his many achievements), I returned to Bologna (IT) and, in late 1989, started the Medical Technology Lab at the Rizzoli Orthopaedic Institute. I worked there until 2011. When I left, the lab hosted 45 between PhD students, post-docs and researchers. I moved to the University of Sheffield, where in 2012, we started the Insigneo Institute for In Silico Medicine. I directed Insigneo for seven years, driving it to become the largest research institute on this topic in Europe.  In 2018, I returned to Bologna as a full professor of industrial bioengineering at the Alma Mater Studiorum - University of Bologna. I also had a joint appointment as director of my old lab at Rizzoli. For the third time, I had to restart from scratch, and despite my promises to take it easy after the crazy years with Insigneo, at the end of 2023, my group counted around 40 researchers.

Comes in 2024, and life is getting complicated.  Working in Italy has never been easy, but the considerable funding of the COVID-19 recovery plan combined with our proverbial administrative ineptitude makes our daily work a nightmare.  The right-wing government is underfunding public healthcare, and working in a research hospital like Rizzoli is becoming more and more difficult. On top of this, I experienced some serious health problems.  And suddenly, I realise that for the first time in my career, the pain is more than the gain.  Time to retire.  But how can I retire while preserving as much of my legacy as possible? 

The first thing I have already cut is the time spent travelling to conferences and similar events.  All the travel budget is now spent to support the travelling of my coworkers; they slowly have to become the faces of our team, replacing me in the perception of our community. 

I still have substantial funding until 2026.  However, I will not apply for any further funding; on the contrary, I will support my coworkers who are in the position to have their own research funding to pursue opportunities.

From Sept 1st, 2024, I will resign as Director of the Medical Technology Lab at the Rizzoli Institute.  The lab has three senior researchers who already run it without me for the seven years while I was in Sheffield; in addition, two of my younger coworkers have a tenure-track position, so I am optimistic the lab will survive my departure. 

At the end of the year, we will close my largest EU grant as coordinator, the In Silico World project.  This four-year endeavour has been a fantastic journey; On Sept 3rd, 2024, we will present the results to the research community in Stuttgart in conjunction with the VPH conference. 

After that, I will be left with primarily national funding that will finish in 2025, except for one project.  As these projects close, I will support the post-doctoral staff in finding a new position elsewhere.  I also hope there will be a tenure-track position in my university department to continue my academic legacy. 

By the end of 2025, I will be left only with the coordinating role in the DARE project, in which my responsibility as a spoke leader is primarily managerial.  Any research activity will be continued by those coworkers who managed to retain a tenure-track position in Bologna.

I will close all my professional social network accounts as my research career fades. I might open one to stay in touch with friends, but only to discuss non-professional topics. I will continue to post occasionally on this blog, but only about culture, society, and philosophy. 

In April 2027, I will be 66 years old.  Depending on my health, I could retire then or wait a few more years, during which I will focus on teaching and tutoring. The mandatory retirement age for full professors in Italy is 70, so I could keep teaching until 2031.

I do not have much more to offer regarding scientific discovery.   In math, the Field Medal can be won only by those 40 or younger. I always thought that was an exaggeration, but I must admit that my creativity has constantly decreased in the last years.  What I can still do is share the significant experience I accumulated in these many years with the researchers in training through teaching and tutoring.

Those who know me tend to describe me as a workaholic. So how will I manage with all that free time?  My wife and I would love to spend time in other places worldwide, so if you need a visiting teacher, talk to me.  Then, I need to go back and brush up on my musical skills, which I did not cultivate in the last 40 years. I would also like to volunteer; it seems a moral imperative in this growingly egoistic society. So, do not worry about me; I will manage even without working 14 hours per day, seven days per week. 

As I fade from the public eye, being part of this international research community has been an honour.  thanks to all of you who worked, debated, revised, laughed, and argued with me during this long career. 

Farewell.

Marco

Credibility of predictive Data-driven vs Knowledge-driven models: a layperson explanation

In science the concept of truth has a meaning quite different than its colloquial use.  Scientists observe a natural phenomenon and formulate different hypotheses on why things happen in the way we observe them. There are many ways to formulate such hypotheses, but the preferred one is to express them in quantitative, mathematical terms, which makes it easier to test whether they are well founded.  

Once a certain hypothesis is made public, all scientists investigating the same natural phenomenon start to design experiments that could demonstrate that the hypothesis is indeed wrong.  It’s only when all possible attempts have been made, and the hypothesis has resisted to all such attempts to prove it wrong, that we can call this a “scientific truth”. What that means is “so far no one could prove it wrong, so we temporarily assume it to be true”.

Achieving a scientific truth is a long and costly process, but it is worth it: once a hypothesis becomes a scientific truth, or as we will call it from now on, scientific knowledge, it can be used to make predictions on how to best solve problems related to the natural phenomenon it refers to. At the risk of oversimplifying, physics aims to produce new scientific knowledge, which engineering uses to solve the problems of humanity. 

For the purpose of this note, is important to stress that the mathematical form chosen to express the hypothesis cannot deny all pre-existing scientific knowledge accumulated so far.  For example, we are quite sure that matter/energy cannot be created or destroyed, but only transformed; this is called law of conservation in physics. Hence, any mathematical form we use to express a scientific hypothesis must not deny the law of conservation.

But the need to solve humanity problems cannot wait for all the necessary scientific knowledge to be available, considering it may take even some centuries for scientists to produce it.  Thus, scientists have developed methods that can be used to solve practical problems even if no knowledge is available, as long as there is plenty of quantitative data obtained from observing the phenomenon of interest.  When the necessary scientific knowledge is available, we solve problems by developing predictive models that are based on such knowledge; otherwise, we use models that are developed only using observational data.  We call the first type knowledge-driven models, and the second type data-driven models.  The first type of models includes those built from the scientific knowledge provided by physics, chemistry, and physiology, for example.  Data-driven models include statistical models and the so-called Artificial Intelligence (AI) models (e.g. machine-learning models).

Now, if the problem at hand is critical (for example in case a wrong solution may threaten the life of people) before we use a model to solve such problem we need to be fairly sure that its predictions are credible, which means sufficiently close to what does happen in reality.  Thus, for critical problems, assessing the credibility of a model is vital. Most problems related to human health are critical, so it should not be a surprise that assessing the credibility of predictive models is a very serious matter in this domain. Unfortunately, assessing the credibility of a data-driven model turns out to be very different from assessing the credibility of a knowledge-driven model.  While the precise explanation of why these are different is quite convoluted and require a solid grasp of mathematics, here we provide a layperson explanation, aimed to all healthcare stakeholders who by training do not have such mathematical background, but still need to make decisions on the credibility of models.

In order to quantify the error made by a predictive model, we need to observe the phenomenon of interest in a particular condition, measure the quantities of interest, then reproduce the same conditions with the model, and compare the quantities it predicts to those measured experimentally.  Of course, this can be done only in a finite number of conditions; but how can be sure that our model will continue to show the same level of predictive accuracy when we use it to predict the phenomenon in a condition different from those we tested?  Here is where the difference on how the model was built plays an important role. 

For knowledge-driven models it can be demonstrated that those mathematical forms chosen to express that knowledge, forms that must be compatible with all pre-existing scientific knowledge, ensure that if the model makes a prediction for a condition close to one tested, also its prediction error will be close to the one quantified in the test. This allows us to assume that once we quantified the prediction error for a sufficiently large number of conditions within a range, for any other condition within that range of conditions the prediction error will remain comparable.  The benefit of this is that for knowledge-driven models we can conduct a properly designed validation campaign, at the end of which we can state with sufficient confidence the credibility of such model.

However, this is not true for data-driven models.  In theory, a data-driven model could be very accurate for one condition, and totally wrong for another close to the first one.  So, the concept of credibility cannot be stated once and for all.  Assessing the credibility of a data-driven model is a continuous process; while we use the model, we periodically need to confirm that the predictive accuracy remains within the acceptable limits, by comparing the model’s predictions to new experimental observations.

To further complicate the matter, sometime a model is composed of multiple parts, some built using a data-driven approach, others built using a knowledge-driven approach.  In such complex cases the model must be decomposed into sub-models, and each needs to assessed in term of credibility in the way most appropriate for its type.

In conclusion, when no scientific knowledge is available for the phenomenon of interest, only data-driven models can be used. In that case, credibility assessment is a continuous process, like quality assessment. After the model is in use, we periodically need to reassess its predictive accuracy against new observational data.  Instead, when scientific knowledge is available, knowledge-driven models are preferable because their credibility can be confirmed with a finite number of validation experiments.

Positioning In Silico Medicine as a computationally-intensive science: a call to arms

In the last few months I followed with growing interest the recent developments of computational sciences, and I felt compelled to raise a warning, which becomes a call for engagement to the entire In Silico Medicine community.

At risk of oversimplifying, with the launch of the EuroHPC initiative the European Commission has made a clear move toward two directions: exascale computing (development and effective use of new computer systems capable of 10^18 floating point operations per second) and quantum computing (use of quantum phenomena to perform computation).  

Because of the strategic nature of this initiative, all computational sciences are slowly being divided into those that are considered computationally-intensive, and those that are not: to the first will be asked to contribute to the definition of the specifications of these new exascale and quantum computing system (codesign); as part of this they  most likely will receive dedicated funding, directly of by earmarking funding for solutions that exploit high performance computing (HPC), as we already saw in some Covid-related call in H2020.  I am less familiar with the other regions of the world, but my impression is that the political agenda around the strategic value of HPC is the same in USA, China, Japan, India, etc.  Thus, I dare to say that probably the same trend is being observed everywhere.

There are some domains that are unquestionably seen as HPC science: Weather, Climatology and solid Earth Sciences; Astrophysics, High-Energy Physics and Plasma Physics; Materials Science, Chemistry and Nanoscience. When we look at Life Sciences and Medicine, the picture is blurred: there is a clear case for molecular simulations, but much less clarity for single-cell system biology, and even less for system physiology.  In Silico Medicine, intended as the clinical and industrial application of computational biomedicine methods, is in my opinion at the present far from making a clear case for being and HPC Scientific domain.  2013 Chemistry Nobel Prize was given to a group of computational chemists; it will take still some decades before we can expect a medicine noble prize by a computational researcher.

Having worked in this field from its beginnings 20 years ago, I can understand why most of the in silico medicine researchers see the computational challenge as an immaterial detail: as community our main focus is still on the credibility in the clinical and regulatory context of our predictions.

But I am worried that if we lose this train, it might take a long while before another pass.  I think that as we prepare for Horizon Europe or for the next round of NIH and NSF funding, we need to start thinking seriously where is the added-value of porting our applications to HPC architectures, and to develop a HPC Science research agenda where scalability is key.  We need to think grand science, from a computational point of view.  And we need to pursue computational grand challenges: can we simulate a phase III clinical trial by running 1000 patient-specific models?  Can we model all cells in a whole tumour? Can we model the electrophysiology of all the cardiomyocytes in a human heart?  Can we couple a whole fluid-electro-mechanical model of the heart with a full fluid-chemo-mechanical model of the lungs?

Another thing we need to start to work as a community is the idea of the Virtual Physiological Human.  There is a funny story here: soon after the term was coined in 2005, we started to defend by those who were asking: Are you planning capture the entire human physiology in a single computer model?  At that time of course the answer was no, not even close.  But I think that now this idea should be brought back, if not as a feasible goal any time soon, at least as something to aim to.  We have great models for the bones, joints and muscles; for the heart; for the pancreas, for the liver; for the lungs.  Can we aim to a neuromusculoskeletal model of human movement?  Or a cardiovasculorespiratory model of the body oxygenation dynamics?

This is a grand challenge for our community, and I call you all to arms.  Make sure all those who are thinking in this direction, seniors and juniors, join the #Scalability channel:

https://insilicoworld.slack.com/archives/C0151M02TA4 

if you click the link and you get a message saying that you are not a member yet, follow this other link and request to join:

http://insilico.world/scalability-support-channel/

I also ask all of you to start posting your scalability challenges.  If you do not  have any, this means you are not thinking big enough, so try again :-).  

We need to get as soon as possible a good representation of what are the HPC needs for the In Silico Medicine community, and joining the #Scalability channel is the most effective way. As a bonus, the top HPC experts in Europe who are partners in the CompBioMed Centre of Excellence will be happy to share their wisdom with you through the same channel and help you to address your scalability issues in the most effective way.