A Science of Wholeness Awaits Us

Just as the melody is not made up of notes nor the verse of words nor the statue of lines, but they must be tugged and dragged till their unity has been scattered into these many pieces, so with the World to whom I say Thou Martin Buber

We creatures of the 21st Century, grandchildren of the Enlightenment, like to think that our particular brand of rationality has finally established a basis for understanding the world in which we live. Of course, we don’t have all the details worked out, but the foundation is solid. 

We might be chastened by the precedent of Lao Tzu and Socrates and Hypatia fof Alexandria and Thomas Aquinas and Lord Kelvin, who thought the same thing. I wonder if the foundation of our world-view is really made of more durable stuff than theirs. In fact, founding our paradigm in the scientific method offers us something that earlier sages did not have: we can actually compare in detail the world we observe and the consequences of our physicalist postulates. The results are not reassuring. In recent decades, the science establishment has willfully ignored observations of phenomena that call into question our foundational knowledge.

Reductionism is the process of understanding the whole as emergent from the parts. The opposite of reductionism is holism: understanding the parts in terms of their contribution to a given whole. It’s fair to say that all of science in the last 200 years has been reductionist. Physical law is the only fundamental description of nature. Chemistry could, in principle, be derived from physics (if only we could solve the Schrödinger equation for hundreds of electrons); living physiology could be understood in terms of chemistry; and ecology could be modeled in terms of individual behaviors. 

Curiously, there are holistic formulations of physics that are mathematically equivalent to the reductionist equations, but in practice, physicists use the differential equations, which are the reductionist version. 

Biological function is explained by a process of evolution through natural selection that made them what they are. Holism in evolution is called “teleology”, and is disparaged as unscientific. But when features of physics appear purposeful, there is no agreement among scientists how to explain them. Most physicists would avoid invoking a creator or embedded intelligence, even at the cost of telling stories about vast numbers of unobservable universes outside our own. This is the most common explanation for the fact that the rules of physics and the very constants of nature—things like the charge on an electron and the strength of the gravitational force—these things seemed eerily to have been fine-tuned to offer us an interesting universe; most other choices for the basic rules of physics might have produced dull uniformity, without stars or galaxies, without chemistry, without life.

But I am racing ahead of the story. The question I want to ask is whether we are missing something in reasoning exclusively from the bottom up, explaining all large-scale patterns as emergent results of small-scale laws. I want to suggest that this deeply-ingrained pattern of thought may be holding science back. Are there large-scale patterns waiting to be discovered? Are there destined outcomes that help us understand the events leading to a predetermined denouement? Even formulating such questions is controversial; and yet, we see hints pointing in just this direction, both from micro-science of quantum mechanics and from studies of the Universe on its largest scale.

Science is all about observing nature and noticing patterns which might be articulated as theories or laws. When these patterns connect nearby events that can be observed at one time by one person, they are easy to spot. When the patterns involve distant events and stretch over time and space, they may go undetected for a long while. This can lead to an obvious bias. Scientists are more inclined to formulate laws of nature that connect contiguous events than laws that connect events that are separated spatially and temporally, just because these global patterns are harder to see.

The physical laws that were formulated and tested in the 19th and 20th century were all mediated by local action. The idea that all physical action is local was formalized by Einstein, and has been baked into our theories ever since. But there is a loophole, defined by quantum randomness. Roughly speaking, Heisenberg’s Uncertainty Principle says that we can only ever know half the information we need to predict the future from the past at the microscopic level. Is the other half replaced by pure randomness, devoid of any patterns that science might discern? Or might it only appear random, because the patterns are spread over time and space, and difficult to correlate? In fact, the existence of such patterns is an implication of standard quantum theory. (This is one formulation of the theorem about quantum entanglement, proved by J.S. Bell in 1964.) Speculative scientists and philosophers relate this phenomenon to telepathic communication, to the “hard problem” of consciousness, and to the quantum basis of life.

I hope to explore this topic in a new ScienceBlog forum beginning in 2021. Here are four examples of the kinds of phenomena pointing to a new holistic science.

1. Michael Levin and the electric blueprint for your body

We think of the body as a biochemical machine, proteins and hormones turned on in the right places at the right times to give the body its shape. Levin is clear and articulate in making the case that the body develops and takes shape under a global plan, a blueprint, and not just a set of instructions. This is true for humans and other mammals, but it is easier to prove it for animals that regenerate. Humans can grow back part of a liver. An octopus can grow a new leg; a salamander can grow a new leg or tail tail; a zebrafish can grow back a seriously damaged heart; starfish and flatworms can grow back a whole body from a small piece.

Consider the difference between a blueprint and an instruction set. An instruction set says

1. Screw the left side of widget A onto the right side of gadget B.
2. Take the assembly of widget+gadget and mount it in front of doodad C, making sure the three tabs of C fit into the corresponding holes in B

A blueprint is a picture of the fully assembled object, showing the relationship of the parts.

Ikea always gives you both. With the instructions only, it is possible to complete the assembly, but only if you don’t make any mistakes. And if the finished object breaks, the instruction set will not be sufficient to repair it. The fact that living things can heal is a strong indication that they (we) contain blueprints as well as instruction sets. The instruction set is in the genome, together with the epigenetic information that turns genes on and off as appropriate; but where is the blueprint?

Prof Michael Levin of Tufts University has been working on this problem for almost 30 years. The answer he finds is in electrical patterns that span across bodies. One of the tools he pioneered is voltage reporter dyes that glow in different colors depending on the electric potential. Here is a map of the voltage in a frog embryo, together with a photomicrograph.

from Levin’s 2012 paper

Levin’s lab has been able to demonstrate that the voltage map determines the shape that the tadpole grows into as it develops. Working with planaria flatworms, rather than frogs, their coup de grace was to modify these voltage patterns “by hand”, creating morphologies that are not found in nature, such as the worm with two heads and no tail.

This is stunning work, documenting a language in biology that is every bit as important as the genetic code. Of course, I am not the first to discover Dr Levin’s work; but it is underappreciated because the vast majority of smart biologists are focusing on biochemistry and it is a stretch for them to step out of the reductionist paradigm.

(I wrote more about Levin’s work two years ago. Here is a video which presents a summary in his own words.)

2. Cold Fusion

Two atomic nuclei of heavy hydrogen can merge to create a single nucleus of helium, and tremendous energy is released. This process is not part of our everyday experience because the hydrogen nuclei are both positively charged and the energy required to push them close enough together that they will fuse is also enormous. So fusion can happen in the middle of the sun, where temperatures are in the millions of degrees, and fusion can happen inside a thermonuclear bomb. But it’s hard as hell to get hydrogen to fuse into helium, and, in fact, physicists have been working on this problem for more than 60 years without a viable solution.

Except that in 1989, the world’s most eminent electrochemist (not exactly a household name) announced that he had made fusion happen on his laboratory bench, using the metal palladium in an apparatus about as complicated as a car battery.

Six months later, at an MIT press conference, scientists from prestigious labs around the world lined up to announce they had tried to duplicate what Fleischmann had reported with no success. The results were un-reproducible. Cold Fusion was dead, and the very word was to become a joke about junk science. Along with the vast majority of scientists, I gave up on Cold Fusion and moved on. 22 years passed. Imagine my surprise when I read in 2011 that an Italian entrepreneur had demonstrated a Cold Fusion boiler, and was taking orders!

The politics of Cold Fusion is a story of its own. I wrote about it in 2012 (not for ScienceBlog). The Italian turned out to be a huckster, but the physics is real.

I began reading, and I became hooked when I watched this video. I visited Cold Fusion labs at MIT, Stanford Research Institute, Portland State University, University of Missouri, and a private company in Berkeley, CA. I went to two Cold Fusion conferences. I became convinced that some of the claims were dubious, and others were convincing. There is no doubt in my mind that Cold Fusion is real.

Physicists were right to be skeptical. The energy for activation is plentiful enough, even at room temperature, but the problem is to concentrate it all in one pair of atoms. Left to its own devices, energy will spontaneously spread itself out— that’s what the science of thermodynamics is all about. To concentrate an eye-blink worth of energy in just two atoms is unexpected and unusual. But things like this have been known to happen, and a few times before they’ve taken physicists by surprise. Quantum mechanics plays tricks on our expectations. A laser can concentrate energy, as billions of light particles all march together in lock step. Superconductivity is another example of what’s called a “bulk quantum effect”. Under extraordinary circumstances, quantum mechanics can leap from the tiny world of the atom and hit us in the face with deeply unexpected, human-scale effects that we can see and touch.

There are now many dozens of labs around the world that have replicated Cold Fusion, but there is still no theory that physicists can agree on. What we do agree is that it is a bulk quantum effect, like superconductivity and lasers. When the entire crystal (palladium deuteride) asks as one quantum entity, strange and unexpected things are possible.

For me, the larger lesson is about the way the science of quantum mechanics developed in the 20th Century. The equations and formalisms of QM are screaming of connectedness. Nothing can be analyzed on its own. Everything is entangled. The quantum formalism defies the reductionist paradigm on which 300 years of previous science had been built.

And yet, physicists were not prepared to think holistically. We literally don’t know how. If you write down the quantum mechanical equations for more than two particles, they are absurdly complex, and we throw up our hands, with no way to solve the equations or even to reason about the properties of the solutions. The many-body quantum problem is intractable, except that progress has been made in some highly symmetrical situations. A laser consists of a huge number of photons, but they all have a single wave function, which is as simple as a wave function can be. Many-electron atoms are conventionally studied as if the electrons were independent (but constrained by the Pauli Exclusion Principle). Solid state physics is built on bulk quantum mechanics of a great number of electrons, and ingenious approximations are used in combination with detailed measurements to reason about how the electrons coordinate their wave state.

Cold Fusion presents a huge but accessible challenge to quantum physicists. Beyond Cold Fusion lie a hierarchy of problems of greater and greater complexity involving quantum effects in macroscopic objects.

In the 21st Century, there is a nascent science of quantum biology. It is my belief that life is a quantum state.

3. Life coordinates on a grand scale

There are many examples of coordinated behaviors that are unexplained or partially explained. This touches my own specialty, evolution of aging. The thesis of my book is that aging is part of an evolved adaptation for ecosystem homeostasis, integrating the life history patterns of many, many species in an expanded version of co-evolution. My thesis is less audacious than the Gaia hypothesis.

  • Monarch butterflies hibernate on trees in California or Mexico for the winter. In the spring, they migrate and mate and reproduce, migrate and mate and reproduce, 6 or 7 times, dispersing thousands of miles to the north and east. Then, in the fall, the great great grand offspring of the spring Monarchs undertake the entire migration in reverse, and manage to find the same tree where their ancestor of 6 generations spent the previous winter. [Forest service article]
  • Zombie crabs have been observed in vast swarms, migrating hundreds of miles across the ocean floor. Red crabs of Christmas Island pursue an overland migration

  • Sea turtles from all over the world arrange for a common rendezvous once a year, congregating on beaches in the Caribbean and elsewhere. Their navigation involves geomagnetism, but a larger mystery is how they coordinate their movements.
  • Murmuration behavior in starlings has been modeled with local rules, where each bird knows only about the birds in its immediate vicinity; but I find the simulations unconvincing, and believe our intuition on witnessing this phenomenon: that large-scale communication is necessary to explain what we see.
  • Monica Gagliano has written about plants’ ability to sense their biological environment and coordinate behaviors on a large scale. This is her more popular book.

4. The Anthropic Coincidences, or the Improbability of Received Physical Laws

For me, this is the mother of all scientific doors, leading to a radically different perspective from the reductionist world-view of post-enlightenment science. Most physicists believe that the laws of physics were imprinted on the universe at the Big Bang, and life took advantage of whatever they happened to be. But since 1973, there has been an awareness, now universally accepted, that the laws of nature are very special, in that they lead to a complex and interesting universe, capable of supporting life. The vast majority of imaginable physical laws give rise to universes that are terminally boring; they quickly go to thermodynamic equilibrium. Without quantum mechanics, of course, there could be no stable atoms, and everything would collapse into black holes in short order. Without a very delicate balance between the strength of electric repulsion and the strong nuclear force, there would be no diversity of elements. If the gravitational force were just a little weaker, there would be no galaxies or stars, nothing in the universe but spread-out gas and dust. If our world had four (or more) dimensions instead of three, there would be no stable orbits, no solar systems because planets would would quickly fly off into space or fall into the star; but a two-dimensional world would not be able to support life because (among other reasons) interconnected networks on a 2D grid are very limited in complexity.

Stanford Philosophy article
1995 book by Frank Tipler and John Barrow
Just Six Numbers by Martin Rees

Most scientists don’t take account of this extraordinary fact; they go on as if life were an inevitability, an accident waiting to happen. But those who have thought about the Anthropic Principle fall in two camps:

The majority opinion:  There are millions and trillions and gazillions of alternative universes. They all exist. They are all equally “real”. But, of course, there’s no one looking at most of them.  It’s no coincidence that our universe is one of the tiny proportion that can support life; the very fact that we are who we are, that we are able to ask this question, implies that we are in one of the extremely lucky universes.

The minority opinion:  Life is fundamental, more fundamental than matter.  Consciousness is perhaps a physical entity, as Schrödinger thought; or perhaps it exists in a world apart from space-time, as Descartes implied 300 years before Schrödinger; or perhaps there is a Platonic world of “forms” or “ideals” [various translations of Plato’s είδος] that is primary, and that our physical world is a shadow or a concretization of that world.  One way or another, it is consciousness that has given rise to physics, and not the other way around.

If you like the multi-universe idea, you will want to listen to the recent Nobel Lecture of Roger Penrose. He races to summarize his life’s work on General Relativity to end the lecture with evidence from maps of the Cosmic Microwave Background of fossils that came from black holes in a previous universe, before our own beloved Big Bang.

I prefer the minority view, not just because it provides greater scope for the imagination [Anne of Green Gables]; there are scientific reasons that go beyond hubristic disregard of Occam’s razor in postulating all these unobservable universes.

  • Quantum mechanics requires an observer.  Nothing is reified until it is observed, and the observer’s probes help determine what it is that is reified.  Physicists debate what the “observer” means, but if we assume that it is a physical entity, paradoxes arise regarding the observer’s quantum state; hence the “observer” must be something outside the laws that determine the evolution of quantum probability waves.  Cartesian dualism provides a natural home for the “observer”.
  • Parapsychology experiments provide a great many indications that awareness (and memory) have an existence apart from the physical brain.  These include near-death experiences, telepathy, precognition, and clairvoyance.
  • Moreover, mental intentions have been observed to affect reality.  This is psychokinesis, from spoon-bending to shading the probabilities dictated by quantum mechanics.

Finally, the idea that consciousness is primary connects to mystical texts that go back thousands of years. 

Dao existed before heaven and earth, before the ten thousand things.  It is the unbounded mother of all living things.

                     — from the Dao De Jing of Lao Tzu

Please look for my new page at ExperimentalFrontiers.ScienceBlog.com, coming soon.

What to Look For in a Biological Clock

In this article, I’m reporting on 

  • new proteomic clock from Adiv Johnson and the Stanford lab of Benoit Lehalier
  • new methylation clock developed with “deep learning” algorithms by an international group from Hong Kong 
  • the advanced methylation clock developed by Morgan Levine, Len Guarente, and Elysium Health


Aging clocks = algorithms that compute biological age from a set of measurable markers. Why are they interesting to us? And what makes one better than another?

The human lifespan is too long for us to do experiments with anti-aging interventions and then evaluate the results based on whether our subjects live longer. The usefulness of an aging clock is that it allows us to quickly evaluate the effects on aging of an intervention, so we can learn from the experiment and move on to try a variant, or something different.

Many researchers are skeptical about using clock algorithms to evaluate anti-aging interventions. I think they are right to be asking deep questions; I also think that in the end the epigenetic clocks in particular will be vindicated for this application.

It may seem obvious that we want the clock to tell us something about biological aging at the root level. We are entranced by the sophisticated statistical techniques that bioinformaticists use to derive a clock based on hundreds of different omic factors. But all that has to start with a judgment about what’s worth looking at.

Ponder this: The biostatisticians who create these clocks are optimizing them to predict chronological age with higher and higher correlation coefficient r. But if they achieve a perfect score of r=1.00, the clock becomes useless. It cannot be used to tell a 60-year-old with the metabolism of a 70-year-old from another 60-year-old with the metabolism of a 50-year-old, because both will register 60 years on this “perfect” clock.

It’s time to back up and ask what we think aging is and where it comes from, then optimize a clock based on the answer. As different people have different answers, we will have different clocks. And we can’t objectively distinguish which is better. It depends on whose theory we believe.

Straw man: AI trained to impute age from facial photos now has an accuracy of about 3½ years, in the same ballpark with methylation clocks. If we used these algorithms to evaluate anti-aging interventions, we would conclude that the best treatments we have are facelifts and hair dye.

Brass tacks: People with different positions about the root cause of aging all agree that (a) aging manifests as damage, and (b) methylation and demethylation of DNA take place under the body’s tight and explicit site-by-site regulation.

But what is the relationship between the methylation and the damage? There are three possible answers.

  1. (from the “programmed” school) Aging is programmed via epigenetics. The body downregulates repair mechanisms as we get older, while upregulating apoptosis and inflammation to such an extent that they are causes of significant damage.
  2. (from the “damage” school) The body accumulates damage as we get older. The body tries to rescue itself from the damage by upregulating repair and renewal pathways in response to the damage.
  3. (also from the “damage” school) Part of the damage the body suffers is dysregulation of methylation. Methylation changes with age are stochastic. Methylation becomes more random with age.

My belief is that (1), (2), and (3) are all occurring, but that (1) predominates over (2). The “damage” school of aging would contend that (1) is excluded, and there are only (2) and (3).

How can these three types of changes contribute to a clock? 

(3) makes a crummy clock, because, by definition, it’s full of noise and varies widely from person to person and from cell to cell. There is no dispute that a substantial portion (~50%) of age-related changes in DNA methylation are stochastic. But these changes are not useful and, in fact, most of the algorithms used to construct methylation clocks tend to exclude type (3) changes. I won’t say anything more about stochastic changes in methylation, but I’ll acknowledge that there is more to be said and refer you to this article if you’re interested in methylation entropy.

If you are from the “damage” school, you don’t believe in (1), so this leaves only type (2). If changes in methylation are the body trying to rescue itself, then any intervention that makes the body’s methylation “younger” is actually dialing down protection repair. You expect that reducing methylation age will actually hasten aging and shorten life expectancy. You have every reason to distrust a clinical trial or lab experiment that uses methylation age as criterion for success.

White cell count is used as a reliable indication of cancer. As cancer progresses, white cell count increases. The higher a person’s white cell count, the closer he is to death. So let’s build a “cancer clock” based on white blood count, and let’s use it to evaluate anti-cancer interventions. The best intervention is a chemical agent that kills the most white blood cells. It reliably sets back the “cancer clock” to zero and beyond. But we’re puzzled when we find that people who get this intervention die rapidly, even though the cancer clock predicted that they were completely cured. The problem is that white blood cells are a response to cancer, not its cause.

If you are from the “programmed” school, you think that (1) predominates, and that a clock can be designed to prefer type (1) changes to (2) and (3). Then methylation clocks measure something akin to the source of aging, and we can expect that if an intervention reduces methylation age, it is increasing life expectancy.

The fact that methylation clocks trained on chronological age alone (with no input concerning mortality or disease state) turn out to be better predictors of life expectancy than age alone is a powerful validation of methylation technology. But only if you believe (for other reasons) that methylation is an upstream cause of aging. You could expect this from either type (1) or type (2) methylation changes.

I believe that aging is an epigenetic life program, and that methylation is one of several epigenetic mechanisms by which it is implemented. That’s why I have faith in methylation clock technology.

Conversely, people who believe that the root cause of aging is accumulated damage are right to discount evidence from epigenetic clocks as it pertains to the efficacy of particular treatments. As in the cancer example above, treatments that create a younger methylation age can actually be damaging.

The basis for my belief that aging is an epigenetic program is the subject of my two books, and was summarized several years ago in this blog. I first wrote about methylation as a cause of aging in this space in 2013. For here and now, I’ll just add that we have direct evidence for changes of type (1). Inflammatory cytokines are up-regulated with age. Apoptosis is upregulated with age. Antioxidants are downregulated with age. DNA repair enzymes and autophagy enzymes and protein-folding chaperones are all down-regulated with age. All these are changes in gene expression, presumably under epigenetic control.

Which is more basic, the proteome or the methylome?

For reasons I have elaborated often in the past, I adopt a perspective on aging as an epigenetic program. I think of methylation clocks as close to the source, because methylation is a dispersed epigenetic signal. But the proteome is, by definition, the collection of all signals transmitted in blood plasma, including all age signals and transcription factors that help to program epigenetics cell-by-cell. The proteome is generated by transcription of the DNA body-wide, which transcription is controlled by methylation among other epigenetic mechanisms. So one might argue from this that the methylome is further upstream than the proteome. On the other hand, methylation is just one among many epigenetic mechanisms, and the proteome is the net result of all of them. On this basis, I would lean toward a proteomic clock as being a more reliable surrogate for age in clinical experiments, even better than methylation clocks. It is a historic fact, however, that methylation clocks have a 6-year headstart. Methylation testing is entering the mainstream, with a dozen labs offering individual readings of methylation age, priced to attract end-users.

Let’s see if proteomic clocks can catch up. The new technology is based on SOMAscan assays, and so far is marketed to research labs, not individuals or doctors, and it is priced accordingly. The only company providing lab services is SOMAlogic.com of Boulder, CO. SOMAscan is an aptamer-based proteomics assay capable of measuring 1,305 human protein analytes in serum, plasma, and other biological matrices with high sensitivity and specificity.” [ref] As I understand it, they have a microscope slide with 1305 tiny dots, each containing a different aptamer attached to a fluorescent dye. An aptamer is like an engineered antibody, optimized by humans to mate to a particular protein. Thus 1305 different proteins can be measured by applying a sample (in our case, blood plasma) to the slide, chemically processing the slide to remove aptamers that have not found their targets, then photographing the slide and analyzing the readout from the fluorescent dye.

Aptamers are synthetic molecules that can be raised against any kind of target, including toxic or non immunogenic ones. They bind their target with affinity similar or higher than antibodies. They are 10 fold smaller than antibodies and can be chemically-modified at will in a defined and precise way. [NOVAPTech company website]

Curiously, aptamers are not usually proteins but oligonucleotides, cousins of RNA, simply because the chemical engineers who design and optimize these structures have had good success with the RNA backbone. The SOMA in SOMAlogic stands for “Slow Off-rate Modified Aptamers”, meaning that the aptamers have been modified to make them stick tight to their target and resist dissociating.

An internal proteome-methylome clock?

It’s possible that there is a central clock that tells the body “act your age”. I have cited evidence that there is such a clock in the hypothalamus, and that it signals the whole body via secretions [20152017].

Another possibility is a dispersed clock. The body’s cells manufacture proteins based on their epigenetic state, the proteins are dispersed in the blood, some of these are received by other cells and affect the epigenetic state of those cells. This is a feedback loop with a whole-body reach, and it is a good candidate for a clock mechanism in its own right.

I’m interested in the logic and the mathematics of such a clock in the abstract. Any feedback loop can be a time-keeping mechanism. Such a mechanism is
_____Epigenetics ⇒ Protein secretion ⇒ Transcription factors ⇒ Epigenetics
This is difficult to document experimentally, but it is an attractive hypothesis because it would explain how the body’s age can be coordinated system-wide without a single central authority, which would be subject to evolutionary hijacking, and might be too easily affected by individual metabolism, environment, etc. But the body’s aging clock must be both robust and homeostatic. If it is thrown off by small events, it must return to the appropriate age.  So my question—maybe there are readers who would like to explore this with me—is whether it is logically possible to have a timekeeping mechanism that is both homeostatic and progressive, without an external reference by which it can be reset.

Last year, Lehalier and a Stanford-based research group jumpstarted the push toward a methylomic aging clock with this publication [my write-up here]. The same group has a follow-up, published a few weeks ago. The new work steps beyond biologically agnostic statistics to incorporate information about known functions of the proteins that they identified last year. The importance of this is twofold: It suggests targets for anti-aging interventions. And it supports the creation of a clock composed of upstream signals that have been verified to have an effect on aging. I argued in the long Prelude above that this is exactly what we want to know in order to have confidence in an algorithmic clock as surrogate to evaluate anti-aging interventions.

They work with a database I had not known about before: the Human Ageing Genomic Resources Database.  HAGR indexes genes related to aging and summarizes studies that document their functions. Some highlights of the proteins they identified:

  • Inflammatory pathways are right up there in importance. No surprise here. But if you can use inflammatory epigenetic changes to make an aging clock, you have a solid beginning.
  • Sex hormones that change with age turn out to be even more prominent in their list. The first several involve FSH and LH. These are hormones connected with women’s ovarian cycles; but after menopause, when they are not needed, their prominence shoots up, and not just once-a-month, but always on. Men, too, show increases in LH and FSH with age, though they are more subtle. I first became aware of LH and FSH as bad actors from the writings of Jeff Bowles more than 20 years ago.
  • “GDF15 It is a protein belonging to the transforming growth factor beta superfamily. Under normal conditions, GDF-15 is expressed in low concentrations in most organs and upregulated because of injury of organs such as such as liverkidneyheart and lung.” [Wikipedia]  “GDF15 deserves a story of its own. The authors identify it as the single most useful protein for their clock, increasing monotonically across the age span. It is described sketchily in Wikipedia as having a role in both inflammation and apoptosis, and it has been identified as a powerful indicator of heart disease. My guess is that it is mostly Type 1, but that it also plays a role in repair. GDF15 is too central a player to be purely an agent of self-destruction.” [from my blog last year]
  • Insulin is a known modulator of aging (through caloric restriction and diabetes).
  • Superoxide Dismutase (SOD2) is a ubiquitous antioxidant that decreases with age, leaving the body open to ROS damage.
  • Motilin is a digestive hormone. Go figure. Until we understand more, my recommendation would be to leave this one out of the aging clock algorithm.
  • Sclerostin is a hormone for bone growth. It may be related to osteoporosis, and well worth inclusion. 
  • RET and PTN are called “proto-oncogenes” and are important for development, but associated with cancer later in life.

Which proteins are most relevant?

The Horvath clocks have been created using “supervised” optimization, which involves human intelligence that oversees the application of sophisticated algorithms. But what happens if you automate the “supervised” part? On the one hand, you must expect mistakes and missed opportunities that you wouldn’t have with human supervision. On the other hand, once you have a machine learning algorithm, you can apply it over and over to different subsets of the data, produce hundreds of different clocks, and choose those that perform best. That’s what Johnson and co-authors have done in the current paper. They describe creating 1565 different clocks based on different subsets of a universe of 529 proteins. In my opinion, their most important work combines biochemical knowledge with statistical algorithms. The work using statistical algorithms alone are much less interesting, for reasons detailed in the Prelude above.


This new offering from Lehalier and Johnson is a great step forward in that

  • proteins in the blood are a broader picture of epigenetics than methylation alone
  • specific proteins are linked to specific interventions that are reliably connected to aging in the right direction. Crucially, the clock is designed to have type (1) epigenetic changes (from the Prelude above) and to exclude type (2)

Next steps

  • to calibrate the clock not with calendar age but with future mortality. This would require historic blood samples, and it is the basis of the Levine/Horvath PhenoAge clock.
  • to optimize the clock separately for different age ranges or, equivalently, to use non-linear fitting techniques in constructing the clock algorithm
  • to commercialize the Aptomer technology, so that it is available more widely and more cheaply

Elysium Index

Elysium is a New York company advised by Leonard Guarente of MIT and Morgan Levine (formerly Horvath’s student, now at Yale). They have an advanced methylation clock available to the public, which they claim is more accurate than any so far. Other clocks are based on a few hundred CpG sites that change most reliably with age, but the Index clock uses 150,000 separate sites (!) which, they claim, offers more stability. The Horvath clocks can be overwhelmed by a single CpG site that is measured badly. (I have personal experienc with this.) Elysium claims that variations from one day to the next or one lab slide to the next tend to average out over such a large number of contributions. On the other hand, as a statistician, I have to wonder about deriving 150,000 coefficients from a much smaller number of inividuals. The problem is called overfitting, and the risk is that the function doesn’t work well outide the limited data set from which it was derived.

In connection with the DataBETA project, I have been talking to Tina Hu-Seliger, who is part of the Elysium team that developed Index. I am impressed that they have done some homework that other labs have not done. They compare the same subject in different slides. They store samples and freeze them and compare results to fresh samples. They compare different clocks using saliva and blood.

I wish I could say more but Elysium Index is proprietary. There is a lot I have not been told, and there is more that I know that I have been asked not to reveal. I don’t like this. I wish that all aging research could be open sourced so that researchers could learn from one another’s work.

Two other related papers

DeepMAge is a new methylation clock, published just this month, based on more sophisticated AI algorithms instead of the standard 20th-century statistics used by Horvath and others thus far. Galkin and his (mostly Hong Kong, mostly InSilico) team are able to get impressive accuracy in tracking chronological age. This technology has forensic applications, in which evidence of someone’s calendar age is relevant, independent of senescence.  And the technology may someday be the basis for more accurate predictions of individual life expectancy. But, as I have argued above, a good clock for evaluating anti-aging measures must look at more than statistics. Correlation is not the same as causation, and only detailed reference to the biochemistry can give confidence that we have found causation.

Biohorology is a review paper from some of this same InSilico team together with some prominent academics, describing the latest crop of aging clocks. The ms is long and detailed, yet it never addresses the core issue that I raise in the Prelude above, about the need to distinguish upstream causes of aging from downstream responses to damage.

The beginning of the ms contains a gratuitous and outdated dismissal of programmed aging theories.

“Firstly, programmed aging contains an implicit contradiction with observations, since it requires group selection for elderly elimination to be stronger than individual selection for increased lifespan.”

Personally, I bristle at reading statements like this. which ignore an important message of my own work and, more broadly, ignore the broadened understanding of evolution that has emerged over the last four decades.

“Secondly, in order for the mechanism to come into place, natural populations should contain a significant fraction of old individuals, which is not observed either (Williams, 1957).” 

This statement was the basis not just of Williams’s 1957 theory, but more explicitly of the Medawar theory 5 years earlier. Neither of these eminent scientists could have known that their conjecture about the absence of senescence in the wild would be thoroughly disproven by field studies in the 1990s, The definitive recent work on this subject is [Jones, 2014].

Take-home message

For the purpose of evaluating anti-aging treatments, the ideal biological clock should be created with these two techniques:

  • It should be trained on historic samples where mortality data is available, rather than current samples where all we know is chronological age, and
  • Components should be chosen “by hand” to assure all are upstream causes of aging rather than downstream responses to damage. (Type 1 from analysis above.)

Deep Mind Knows how Proteins Fold

This week, Deep Mind, a London-based Google company, claims to have solved the number one most consequential problem in computational biochemistry: the protein-folding problem.  If true, this could be the start of something big.

What does it mean, and why is it important? Let’s start with signal transduction. This is a word for the body’s chemical computer. The nervous system, of course, constitutes a signal-processing and decision-making engine; and in parallel, there is a chemical computer. The body has molecules that talk to other molecules that talk to other molecules, sending a cascade of ifs and thens down a chain of logic. The way molecules with very complex shapes fit snugly together is the language of the chemical computer. These molecules with intricate shapes are proteins, and they are not formed in 3D. Rather, DNA provides instructions for a linear peptide chain of amino acids which are transcribed in ribosomes (present in every cell) to create a chain of amino acids, chosen from a canonical set of 20. Each peptide chain folds into a protein with a characteristic shape, and it is these shapes that constitute the body’s signaling language. Most age-related diseases can be traced to an excess or a deficiency of these protein signal molecules.

So signal proteins are targets of medical research. Pharmaceutical interventions may modify signal transduction, perhaps by goosing signaling at some juncture, or by siphoning off a particular signal with another chemical designed to fit perfectly into its bumps and hollows. Up until now, there has been a lot of trial and error in the lab, looking for chemicals with complementary shapes. Imagine now that the Deep Mind press release is not exaggerating, and they really can reliably predict the shape that a peptide will take once it is folded. Then many months of laboratory experiments can be replaced with many hours of computation. All the trial-and-error work can be done in cyberspace. An inflection point in drug development, if it’s true.

Why it’s a Hard Problem

Computers solve large problems by breaking them down into a great many small ones. But protein folding can’t be solved by looking separately at each segment of the protein molecule. Everything affects everything else, and the optimal shape is a property of the whole. Proteins are typically huge molecules, with hundreds or thousands of amino acids chained together. The peptide bonds allow for free rotation. So the number of shapes you can form with a given chain is truly humongus. The sheer number of possibilities would overwhelm any computer program that tried to deal with the different shapes one at a time.

The thing that stabilizes a given shape is hydrogen bonding. Nominally, each hydrogen atom can form only one bond to a carbon or oxygen, but every hydrogen is a closet bigamist, and it longs to couple with a nearby carbon or (better still) oxygen atom even as it is bound primarily to its LTR partner. Every twist and bend in the molecular chain allows some new opportunities for hydrogen bonding, while removing others. The breakthrough in computing came 1% inspiration, 99% perspiration (Edisonn’s recipe). A key input was to map the structure of 170,000 known, natural proteins, and to train the computer to be able to retrodict the known results. Then, when working with a new and unknown shape, the computer makes decisions that are based on its past success.

How does it make the decisions? No one knows. One of the most successful techniques in artificial intelligence uses generic layers of input and output with programmable maps, and the maps are trained to give the right answer in known cases. But the fundamental logic that drives these decisions remains opaque, even to the programmers. 


It gets more complicated

Many proteins don’t have a unique folded state. They are in danger of folding the wrong way. So there are proteins called chaperones that help them to get it right. These chaperones don’t explicitly dictate the proetein’s final structure, but rather they place the protein in a protected environment. There are 20,000 different proteins needed in the human body, but only a handful of different chaperones.

Factoid: Most inorganic chemical reactions take place on a time scale of billionths of a second. Organic reactions are somewhat slower. But protein folding happens on a human time scale of seconds, or even minutes.

The AI that finds a protein’s ultimate structure must have knowledge of the environment in which the protein folds. It is not merely computing something intrinsic to the sequence of amino acids that makes up the nacent protein. To underscore this problem, proteins fold incorrectly almost as often as they fold correctly. There is an army of caretaker proteins that inspect and correct already-folded proteins. Misfolded proteins tend to clump together and there are chemicals specialized in puilling them apart. For the lost causes, there are proteasomes, which break the peptide bonds and recycle a damaged protein into constituent parts. The name ubiquitin derives from the fact that these protein recyclers are found in every part of every cell.

The question arises, how do these caretaker proteins know what is the correct shape and what is a misfolded shape? Remember that the number of chaperones and caretakers is vastly smaller than the number of proteins that they attend to, so they cannot contain detailed information about the proper conformation of each protein they service. And this leads to a deep question for AI: It’s hard enough to know how a particular protein chain will fold into a conformation that is thermodynamically optimized. But the conformation optimized for least energy may or may not be the one that is useful to the body.

Prions are mysterious

In the late 1970s, a young neurologist named Stanley Prusiner began to suspect that misfolded proteins could be infectious agents. He coined the term prion for a misfolded protein that could cause other proteins to misfold. This idea defied ideas about how pathogens evolve, and in particular ran afoul of Francis Crick’s Central Dogma of Molecular Biology, which said that information was always stored in DNA and transferred downstream to proteins.

The evolutionary provenance of prions remains a mystery, but it is now well-established that certain misfolded proteins can cause a chain reaction of misfolding. The process is as mysterious as it is frightening. Neil Ferguson, who has become infamous this year for his apocalyptic COVID contagion models, frightened the UK in an earlier episode into slaughtering and incinerating more than 6 million cows and sheep, in a classic example of panic leading to overkill.

Prusiner had to wait less than 20 years before the medical community acceded to his heresy. He was awarded the Nobel Prize in 1997.

Example and Teaser

This example is from a review I am preparing for this space next week. I am reading two recent papers about proteins in the blood that change as we age. Assuming that these signals are drivers of aging, what can be done to enhance the action of those that we lose, or suppress the action of those that increase with age? The connection to the present column is that knowledge of protein folding can be used to engineer proteins that redirect the body’s chemical signal transduction at a given intervention point. For example, FSH (follicle-stimulating hormone) is needed just a few days of a woman’s menstrual cycle, but FSH levels rise late in life, with disastrous consequences for health. FSH shoots up in female menopause, and in males it rises more gradually.

FSH drives the imbalance in blood lipids associated with heart disease and stroke. In lab rodents, FSH can be blocked with an antibody, or by genetic engineering, with consequent benefits for cardiovascular health [ref] and loss of bone mass [ref]. The therapy also reduces body fat “Here, we report that this antibody sharply reduces adipose tissue in wild-type mice, phenocopying genetic haploinsufficiency for the Fsh receptor gene Fshr. The antibody also causes profound beiging*, increases cellular mitochondrial density, activates brown adipose tissue and enhances thermogenesis.” [ref] In the near future, we may be able to use computer-assisted protein design to create a protein that blocks the FSH receptor and do safely in humans what was done with genetic engineering in mice.
*Beiging is turning white adipose tissue to brown. Briefly, the white fat cells are permanent and cause diabetes, while the brown are burned for fuel.