Is genetics still metaphysical? Part II. Is that wrong?

What is the role of theory vs empiricism in science?  How do these distinctions apply to genetics?

Yesterday, we discussed some of the history of contesting views on the subject.  Much of the division occurred before there was systematically theoretical biology.  In particular, when creationism, or divine creative acts rather than strictly material processes, was the main explanation for life and its diversity, the issues were contended in the burgeoning physical sciences, with its dramatic technological advances, and experimental settings, and where mathematics was a well-established part of the science and its measurement aspects.

Around the turn of the 20th century, Darwinian evolution was an hypothesis that not even all the leading biologists could accept.  Inheritance was fundamental to any evolutionary view, and inherited somethings seemed obviously to be responsible for the development of organisms from single cells (fertilized eggs). Mendel had shown examples of discretely inherited traits, but not all traits were like that.  Ideas about what the inherited units were (Darwin called them gemmules, Mendel called them Elements, and hereafter I'll use the modern term 'genes') were simply guesses (or just words).  They were stand-ins for what was assumed to exist, but in the absence of their direct identification they were, essentially, only metaphysical or hypothetical constructs.

The cloak of identity had serious implications.  For example, evolution is about inherited variation, but genes as known in Darwin's time and most of the later 19th century didn't seem to change over generations, except perhaps due to grotesquely nonviable effects called 'mutations'.  How could these 'genes', whatever they were, be related to evolution, which is inherently about change and relative positive effects leading to selection among organisms that carried them?

Many critics thought the gene was just a metaphysical concept, that is, used for something imagined, that could not in a serious way be related to the empirical facts about inherited traits. The data were real, but the alleged causal agent, the 'gene', was an unseen construct, yet there was a lot of dogma about genes.  Many felt that the life sciences should stick to what could be empirically shown, and shy away from metaphysical speculation.  As we saw yesterday, this contention between empiricism and theory was a serious part of the debate about fundamental physics at the time.

That was more than a century ago, however, and today almost everyone, including authors of textbooks and most biologists themselves, asserts that we definitely do know what a gene is, in great detail, and it is of course as real as rain and there's nothing 'metaphysical' about it.  To claim that genes are just imagined entities whose existential reality cannot be shown would today be held to be not just ignorant, but downright moronic.  After all, we spend billions of dollars each year studying genes and what they do!  We churn out a tsunami of papers about genes and their properties, and we are promised genetically based 'precision' medicine, and many other genetic miracles besides, that will be based on identifying 'genes for' traits and diseases, that is enumerable individual genes that cause almost any trait of interest, be it physical, developmental, or behavioral.  That's why we're plowing full budget ahead to collect all sorts of Big Data in genetics and related areas.  If we know what a gene is then the bigger the data the better, no?

Or could it be that much of this is marketing that invokes essentially metaphysical entities to cover what, despite good PR to the contrary, remains just empiricism?  And if it is just empiricism, why the 'just'?  Isn't it good that, whatever genes 'are', if we can measure them in some way we can predict what they do and live to ripe old ages with nary a health problem?  Can't we in fact make do with what is largely pure empiricism, without being distracted by any underlying law of biological causation, or the true nature of these causative entities--and deliver the miraculous promises? The answer might be a definitive no!

The metaphysical aspects of genes, still today
In essence, genes are not things, they are not always discrete DNA sequence entities with discrete functions, and they are not independently separable causative agents.  Instead, even the term 'gene' remains a vague, generically defined one.  We went through decades in the 20th century believing that a gene was a distinct bit of DNA sequence, carrying protein code. But it is not so simple.  Indeed, it is not simple at all. 

It is now recognized by those who want to pay attention to reality, that the concept of the 'gene' is still very problematic, and to the extent that assertions are made about 'genes' they are metaphysical assertions, no matter how clothed in the rhetoric of empiricism they may be.  For example, many DNA regions code for functional RNA rather than protein.  Much DNA function has to do with expression of these coding regions.  Many coding regions are used in different ways (for example, different exon splicing) in different circumstances.  Some DNA regions act only when they are chemically modified by non-DNA molecules (and gene expression works exclusively in that way). Some of 'our' DNA is in microbes that are colonizing us.  And 'traits' as we measure them are the result of many--often hundreds or more--DNA elements, and of interactions among cells.  Each cell's DNA is different at least in some details from that of its neighbors (due to somatic mutation, etc.).  And then there is 'the' environment!  This is central to our biological state but typically not accurately measurable.

Some discussion about these issues can be seen in a report of a conference on the gene concept in 2011 at the Santa Fe Institute.  Even earlier, in 2007 when it seemed we had really learned about genomes, hardly suspecting how much more there was (and is) still to be learned, a review in Genome Research was defined in an almost useless way as follows: 

Finally, we propose a tentative update to the definition of a gene: A gene is a union of genomic sequences encoding a coherent set of potentially overlapping functional products. Our definition sidesteps the complexities of regulation and transcription by removing the former altogether from the definition and arguing that final, functional gene products (rather than intermediate transcripts) should be used to group together entities associated with a single gene. It also manifests how integral the concept of biological function is in defining genes.

Really?!  Is that a definition or an academically couched but empty kicking of the can down the road while seeming to be knowledgeable and authoritative?  Or is it simply so empty as to be risible?

There are many now who advocate a 'Third Way' that in a rather generic sense of advocating less dogma and more integrative and indeed innovative or integrative approaches.  But even this doesn't say what the Third Way actually is, though one thing for sure is that it's every Third Way member's favorite way of coopting the concept of biological causation as his or her own.  I'm being cynical, and I'm associated with the Third Way myself and believe that serious rethinking about biological causation and evolution is in order, but that doesn't seem to be too unfair a way to characterize the Third Way's characterization of mainline genome-centered or perhaps genome-obsessed thinking. At least, it acknowledges that we don't just have 'genes' and 'environment', but that biological causality is based fundamentally on interactions of many different kinds. 

DNA is basically an inert molecule on its own

In genetic terminology, DNA is basically an inert molecule.  That is, whatever you want to call genes act in a context-specific way, and this goes beyond what is known as cis interactions among local DNA elements (like regulatory sequences flanking coding sequences) along a given strand. Instead, genetic function is largely a trans phenomenon, requiring interaction among many or even countless other parts of DNA on the different chromosomes in the cell.  And often if not typically, nothing happens until the coded product--RNA or protein--itself is modified by or interacts with other compounds in the cell (and responds to external things the cell detects).

Beyond even that complexity provides comparable evolutionary or physiological complexity.  There are many, perhaps often also countless alternative biological pathways to essentially the same empirical result (say, height or blood pressure or intelligence).  These causally equivalent combinations, if we can even use the term 'causal', are many and un-enumerated, and perhaps un-enumerable.  The alternatives may be biochemically different, but if it they confer essentially no difference in terms of natural selection, they are evolutionarily as well as physiologically equivalent. Indeed, the fact is that every cell, and hence every organism is different in regard to the 'causal' bases of traits.  We may be able to define and hence measure some result, such as blood pressure or reproductive fitness; but to speak of causes as if they are individually distinct or discrete entities is still essentially being metaphysical. Yet, for various sociocultural and economic reasons, we seem unwilling to acknowledge this.

You might object by saying that in fact most geneticists, from Francis Collins down to the peons who plead for his funding support, are being essentially empirical and not indulging in theory.  Yes, they drop words like 'gene' and 'epigenome' and 'microbiome' or 'network' or 'system', but this are on or over the edge of metaphysics (speculative guessing).  Many who feed at the NIH (and NSF) trough might proudly proclaim that they are in fact not dealing with airy-fairy theory, but simply delivering empirical and hence practical, useful results.  They do genomewide mapping because, or even proudly declaring, they have no causative theory for this disease or that behavioral trait.  Usually, however, they confound statistical significance with formal theory, even if they don't so declare explicitly.

For example, most studies of genotypes and genetic variation relative to traits like disease, are based on internal comparisons (cases vs control, tall vs short, smart vs not-smart, criminal vs non-criminal, addictive vs sober, etc.).  They don't rest on any sort of theory except that they do implicitly identify entities like 'genes'.  Often this is so metaphysical as to be rather useless, but it is only right to acknowledge that these results are occasionally supported by finding an indicated 'gene' (DNA sequence element), whose manipulation or variation can be shown to have molecular function relevant to the trait, at least under some experimental conditions.  But this causative involvement is usually quite statistical, providing only weak causative effects, rather than in any clear sense deterministic.  We are enabled by this largely pure empiricism to argue that the association we saw in our retrospective study is what we'll see prospectively as causation in the future.  And we now know enough to know that when it seems to work it is (as, indeed, in Mendel's own time) it's only the simplest tip of the causative iceberg.

We are tempted to believe, and to suggest, that this 'gene' (or genetic variant, an even cruder attempt at identifying a causative element) will be predictive of, say, a future disease at least in some above-average sense. That is, even if we don't know the exact amount of associated risk.  But even that is not always the case: the associated risks are usually small and data-specific and often vary hugely from study to study, over time, or among populations.  That means, for example, that people--typically by far most people--carrying the risk variant will not get the associated disease! It may often do nothing when put into, say, a transgenic mouse.  The reason has to be context, but we usually have scant idea about those contexts (even when they are environmental, where the story is very similar). That is a profound but far under-appreciated (or under-acknowledged) fact with very widespread empirical support!

Indeed, the defense of pure empiricism is one of convenience, funding-wise among other reasons; but perhaps with today's knowledge all we can do if we are wedded to Big Data science and public promises of 'precision' genomic prediction.  When or if we have a proper theory, a generalization about Nature, we can not only test our empirical data agains the theory's predictions, but also use the theory to predict new, future outcomes with a convincing level of, yes, precision. Prediction is our goal and the promises (and, notably, research funding) rest on prediction, not just description. So, as Einstein (and Darwin) felt, an underlying theory of Nature makes data make sense. Without it we are just making hopeful guesses.  Anyone who thinks we have such a theory based on all the public rhetoric by scientists is, like most of the scientists themselves, confusing empiricism with theory, and description with understanding. Those who are thoughtful know very well that they are doing this, but can't confess it publicly.  Retired people (like me) are often less inhibited!

Or could there perhaps be another way to think about this, in which genetics as currently understood remains largely metaphysical, that genetics is real but we simply don't yet have an adequate way of thinking that will unite empiricism to some underlying global reality, some theory in the proper scientific sense?

Tomorrow we'll address the possibility that genetics is inherently metaphysical in that there isn't any tractably useful universal natural law out there to be discovered.

Is genetics still metaphysical? Part I. Some general history.

In very broad terms, modern science has had debates about two basic kinds of approaches to understanding the world.  To over-simplify, they are the empirical and the theoretical approaches. Some argue that we can know only what we can detect with our sensory systems (and machines to extend them), but we can never know what general causal principles account for those data, or even if such real, true principles exist. Others view science's essential job as not just accumulating collections of data, which are necessarily imperfect, but to use such observations to build a picture of the true, or perfect underlying regularity--the 'laws' of Nature.

In the former case we just have to make measurements and try to show the ways in which comparable situations lead to comparable outcomes.  In the latter, we want what we call 'theory', that is, perfect generalizations that tell us how a given situation will turn out, and what the causal reasons are.  The standard assumption of the physical sciences is that Nature is, indeed, universally law-like.  Variables like the gravitational constant and the speed of light really are universally, precisely constant.

These are age-old differences, often 'just' philosophical, but they're quite important.  Comparably important are the still-unanswered question as to whether any phenomena in Nature is irreducibly probabilistic rather than deterministic, or whether probabilistic aspects of Nature really just reflect our imperfect sampling and measurement. This is the important distinction between epistemology--how we know things, and ontology--how things really are.  Can we ever tell the difference?

Empiricism is in some ways the easy part.  We just go out and make measurements and let them accumulate so we can generalize about them.  That's a lot of slogging to get the data, but all you have to do is be systematic and careful.  Don't give me airy generalizations, just the facts, please!

In other ways, theory is the easy part.  All you have to do is sit in your armchair, as the proverbial denigratory model has it, and make up something that sounds exotic (or even mathematically intricate) and claim you know how Nature 'is'.  Data are imperfect, so don't bother me about that! There are long traditions in both kinds of approach, and to a great extent it's only been the past few hundred years in which there has been melding of these two basic approaches.

Often, theory hypothesizes some fundamental objects whose properties and actions can only be seen indirectly, as they are manifest in measurable phenomena. Here there is a delicate boundary between what is essentially 'metaphysical' as opposed to real.  Many object to the use of metaphysical concepts and claims as being essentially untestable, and argue that only empiricism is real and should be taken seriously.  In the 19th and early 20th centuries, as technology revealed more and more about unseen Nature, things that were not yet seen directly but that could be hypothesized and assigned to things we could measure, we taken as true by some but denigrated as metaphysical by pure empiricists.

These distinctions were never that clear, in my view (even if they provided jobs for philosophers to write about).  Empiricism is retrospective but understanding requires some sorts of predictability, which is prospective.  If we cannot reliably generalize, if the same conditions don't always lead to the same result, how can the observing the former lead us to the latter?  Predictive power is largely what we want out of science, even if it's just to confirm our understanding of Nature's laws.

Until fairly recently, these issues have mainly been housed in the physical sciences, but since Linnaeus' time, but especially after Darwin and Wallace, the issues have applied to biology as well.
In this brief series we'll try to explore whether or how we can think of biology as the result of such universal laws or whether all we can do is make observations and rough causal generations about them. What is the place for strong causal theory in biology, or are empiricism and very general notions of process enough?

An example from the early prime era in modern science is the 'atom'.  Matter was conceived as being composed of these unseen particles, that accounted for the weight and properties of chemicals, and whose movement accounted for the weight, temperature, and pressure in gases.  Similar kinds of issues related to electromagnetism: what 'was' it?

An important late 19th-early 20th century example had to do with the existence of 'ether' as the medium through which electromagnetic radiation moved.  Ether could not be seen or felt but wavelike radiation had to be waves in something, didn't it?  Late-century tests failed to find it (e.g., the famous Michelson-Morely experiment).  In well-known interchanges at the time, figures like Ernst Mach, Albert Einstein and Max Planck thought about and debated whether there was a 'real' underlying general 'fabric' of Nature or whether specific empirical data simply showed us enough, and trying to delve deeper was dealing in metaphysics.  Many felt that was simply not justified--measurement or empiricism was what science could hope for.  On the other hand some, like Einstein, were convinced that Nature had a universal, and real underlying reality of which measurements were reflections.  He felt that theory, and in this case mathematics, could reveal or even 'intuit' Nature's underlying fabric.  An interesting article by Amanda Gefter in Nautilus science magazine deals with some of this history, with useful references.

So what about biology?
Biology had been largely a descriptive or even theological field before it became a modern science. But then came Darwin and his idea of evolution.  He viewed natural selection as a kind of Newtonian universal force.  Was it a type of explanation fitted simply around the empirical data that had been collected by Naturalists, or did it constitute some form of universal theory of life as Darwin asserted? Selection as a force had to work through some 'medium' or elements of inheritance.   His causal elements ('gemmules') were (like Lamarck's before him) entirely invented to 'fit' what was being observed about the evolution of diversity.  Indeed, he modeled natural selection itself after intentional agricultural selection because the latter could be demonstrated by human intent, while the former was generally far too slow to observe directly.  But there had to be some 'units' of inheritance for it to work, so he essentially invented them out of thin air.  Even in the early 20th century, 'genes' (as they became known) were largely hypothesized units for whose physical nature--or even reality--there was only indirect empirical evidence.

Assuming these discrete causal particles could enable the force, natural selection, to work on adaptive change was much like assuming that electromagnetic radiation needed ether to do its job.  Since differential reproductive success is observable, one can always define it to be the result of selection and to assume some gene(s) to be responsible. The test for relative success is, after all, only a statistical one with subjective decision-making criteria (like significance level) in empirical data.  In that sense, natural selection is a very  metaphysical notion because after the fact we can always empirically observe what has succeeded over time, or what functions have evolved, and call that the result of selection.  Such an explanation can hardly be falsified.  What is the reality of the underlying force, that Darwin likened to gravity?  Since it is always dependent on changing local conditions, what sort of a 'law' is it anyway?  And if it's basically metaphysical, should we reject it?

Mendelian genetics as metaphysics
If selection is a process, like gravity, it had to work on objects.  Because individual organisms are temporary (they all die), the objects in question had to be transmitted from parent to offspring.  That transmission was also found, by Mendel's experiment, to be a regular kind of process.  Mendel's causative 'elements', that we now call 'genes', appeared in his carefully chosen pea experiments to be transmitted as discrete things.  They fit the discretely causative world of the energized new field of atomic chemistry (see my Evolutionary Anthropology article on Mendel), with its idea that a chemical is made up of a particular kind of atom (thought by some to be multiples of hydrogen at the time), and Mendel's statistical tests showed a reasonably good fit to that discrete-unit worldview (indeed accusations that he or his assistants cheated may reflect his acceptance of discrete underlying but unseen and hence metaphysical, elements). But what were these genes?  In what serious sense did they exist as things rather than just an imaginary but essentially unconstrained variables conjured up to account for actual observations--of some sorts of inheritance, that of discretely varying traits--whose actual nature was entirely inaccessible?

These questions became very important in the debate about how evolution worked, since evolution required inheritance of favored states.  But what Mendelian analysis, the only 'genetic' analysis available at the time, showed was that the causal genes' effects did not change, and they only were shown to fit discretely varying traits, not the quantitative traits of Darwinian evolution.  For these reasons even many mainline evolutionary biologists felt that genes, whatever they were, couldn't account for evolution after all.  Maybe geneticists were indulging in metaphysics.

This was similar to the situation that engaged Einstein, Ernst Mach, and others about physics, but when it came to biology, the difference between empiricism and metaphysics became, literally, quite lethal!  The tragic impact of Profim Lysenko in the Soviet Union was due to a direct rejection by the scientific power structure that he established based on promises of rapid adaptation in plants, for example to the long, frozen Soviet winters, without adaptive 'genes' having to arise by evolution's slow pace.  As I summarized in another Ev. Anth article, it was in part the alleged 'metaphysical' nature of 'genes' in the early 20th century that Lysenko used to reject what most of us would call real science, and put in place an agricultural regime that failed, with mortally disastrous consequences. Along the way, Lysenko with Stalin's help purge many skilled Soviet geneticists, leading many of them to tragic ends. The mass starvation of the era of Lysenkoist agriculture in the USSR may in part have been the result of this view of theoretical science (of course, Lysenko had his own theory, which basically didn't work as it was as much wishful thinking as science).

But how wrong was it to think of genes as metaphysical concepts at the time?  Mendel had showed inheritance patterns that seemed to behave, statistically, as if they were caused by specific particles. But he knew many if not most traits did not follow the same pattern.  Darwin knew of Mendel's work (and he of Darwin's), but neither thought the other's theories were relevant to his own interests.

But in the first part of the 20th century, the great experimental geneticist TH Morgan used Mendelian ideas in careful breeding experiments to locate 'genes' relative to each other on chromosomes.  Even he was an empiricist and avowedly didn't really deal with what genes 'were', just how their causal agency was arranged.

Mendel's work also provided a research experimental approach that led via Morgan and others to the discovery of DNA and its protein coding sequences.  We call those sequences 'genes' and research has documented what they are and how they work in great detail.  In that sense, and despite early vague guesses about their nature, for most of a century one could assert that genes were in fact quite real, not metaphysical, entities at all.  Not only that, but genes were the causal basis of biological traits and their evolution!

But things have turned out not to be so simple or straightforward.  Our concept of 'the gene' is in rather great flux, in some ways each instance needing its own ad hoc treatment.  Is a regulatory element a 'gene', for example, or a modified epigenetic bit of DNA?  Is the 'gene' as still often taught in textbooks still in fact largely a metaphysical concept whose stereotypical properties are convenient but not nearly as informative as is the commonly presented view, even in the scientific literature?

Are we still resting on empiricism, invoking genetic and evolutionary theory as a cover but, often without realizing it, fishing for an adequate underlying theory of biological causation, that would correspond to the seamless reality Einstein (and Darwin, for that matter) felt characterized Nature? Is the gene, like Procrustes, being surgically adapted after the fact, to fit our desired tidy definition?  Is claiming a theory on which genetic-based predictions can be 'precise' a false if self-comforting claim, as a marketing tool by NIH, when in fact we don't have the kind of true underlying theory of life that Einstein dreamed of for physics and the cosmos?

We'll deal with that in our next posts.

Science journals: Anything for a headline

Well, this week's sensational result is reported in the Oct 5 Nature in a paper about limits to the human lifespan. The unsensational nature of this paper shows yet again how Nature and the other 'science' journals will take any paper that they can use for a cheap headline.  This paper claims that the human life span cannot exceed 115 (though the cover picture in a commentary in the same issue is a woman-- mentioned in the paper itself--who lived to be substantially older than that!).  The Nature issue has all the exciting details of this novel finding, which of course have been trumpeted by the story-hungry 'news' media.

In essence the authors argue that maximum longevity on a population basis has been increasing only very slowly or not at all over recent decades.  It is, one might say, approaching an asymptote of strong determination. They suggest that there is, as a result of many complex contributing factors-of-decline, essentially a limit to how long we can live, at least as a natural species without all sorts of genetic engineering.  In that sense, dreams of hugely extended life, even as a maximum (that is, if not for everyone), are just that: dreams.

This analysis raises several important issues, but largely ignores others.  First, however, it is important to note that virtually nothing in this paper, except some more recent data, is novel in any way.  The same issues were discussed at very great length long ago, as I know from my own experience.  I was involved in various aspects of the demography and genetics of aging, as far back as the 1970s.  There was a very active research community looking at issues such as species-specific 'maximum lifespan potential', with causal or correlated factors ranging from the effects of basic metabolism, or body or brain size.  Here's a figure from 1978 that I used in a 1989 paper




There was experimental research on this including life-extension studies (e.g., dietary restriction) as well as comparison of data over time, much as (for its time) the new paper.  The idea that there was an effective limit to human lifespan (and likewise for any species) was completely standard at that time, and how much this could be changed by modern technologies and health care etc. was debated. In 1975, for example (and that was over 40 years ago!), Richard Cutler argued in PNAS that various factors constrained maximum lifespan in a species-related way.  The idea, and one I also wrote a lot about in the long-ago past, is that longevity is related to surviving the plethora of biological decay processes, including mutation, and that would lead to a statistical asymptote in lifespan.  That is, that lifespan was largely a statistical result rather than a deterministically specified value.  The mortality results related to lifespan were not about 'lifespan' causation per se, but were just the array of diseases (diabetes, cancer, heart disease, etc.) that arose as a result of the various decays that led to risk increasing with duration of exposure, wear and tear, and so on, and hence were correlated with age.  Survival to a given age was the probability of not succumbing to any of these causes by that age.

This paper of mine (mentioned above) was about the nature of arguments for a causally rather that statistically determined lifespan limit.  If that were so, then all the known diseases, like heart disease, diabetes, cancer, and so on, were irrelevant to our supposed built-in lifespan limit!  That makes no evolutionary sense, since evolution would not be able to work on such a limit (nobody's still reproducing anywhere near that old).  It would make no other kind of sense, either.  What would determine such a limit and how could it have evolved?  On the other hand, if diseases--the real causes that end individual lives--were, together, responsible for the distribution of lifespan lengths, then a statistical rather than deterministic end is what's real.  The new paper doesn't deal with these, but by arguing that there is some sort of asymptotic limit, it implicitly invokes some sort of causal, evolutionarily determined value, and that seems implausible.

Indeed, evolutionary biologists have long argued that evolution would produce 'negative pleiotropy', in which genomes would confer greater survival at young ages, even if the result was at the expense of greater mortality later on.  That way, the species' members could live to reproduce (at least, if they survived developmentally-related infant mortality), and they were dispensable at older ages so that there was no evolutionary pressure to live longer.   But that would leave old-age longevity to statistical decay processes, not some built-in limit.

Of course, with very large data sets and mortality a multicausal statistical process, rare outliers would be seen, so that more data meant longer maximum survival 'potential' (assuming everyone in a species somehow had that potential, clearly a fiction given genetic diseases and the like that affect individuals differently).  There were many problems with these views, and many have since tried to find single-cause lifespan-determining factors (like telomere decay, in our chromosomes), an active area of research (more on that below).  We still hunger for the Fountain of Youth--the single cause or cure that will immortalize us!

The point here is that the new paper is at most a capable but modest update of what was already known long ago.  It doesn't really address the more substantive issues, like those I mention above.  It is not a major finding, and its claims are also in a sense naive, since future improvements in health and lifestyles that we don't have now but that applied to our whole population could extend life expectancy--the average age at death--and hence the maximum to which anyone would survive. After all, when we had huge infectious disease loads, hardly anybody lived to 115, and in the old days of research, to which the authors seem oblivious, something like 90-100 was assumed to be our deadline.

The new paper has been criticized by a few investigators, as seen in reports in the news media coverage.  But the paper's authors probably are right that nothing foreseeable will make a truly huge change in maximum survival, nor will many survive to such an extended age.  Nor--importantly--does this mean that those who do luck out are actually very lucky: the last few years or decades of decrepitude may not be worth it to most who last to the purported limit. To think of this as more than a statistical result is a mistake.  Not everyone can live to any particular age, obviously.

The main fault in the paper in my view is the claim in essence to portray the result as a new finding, and the publication in a purportedly major journal, with the typical media ballyhoo suggesting that.

On the other hand....
On the other hand, investigators who were interviewed about this study (to give it 'balance'!) denigrated it, saying that novel medical or other (genetic?) interventions could make major changes in human longevity.  This has of course happened in the past century or two.  More medical intervention, antibiotics and vaccines and so on have greatly increased average lifespan and, in so doing in large populations, increased the maximum survival that we observe.  This latter is a statistical result of the probabilistic nature of degenerative processes like accumulating wear and tear or mutations, as I mentioned earlier.  There is no automatic reason that major changes in life-extending technologies are in the offing, but of course it can't be denied as a possibility either. Similarly, if, say, antibiotic resistance becomes so widespread that infectious diseases are once again a major cause of death in rich countries, our 'maximum lifespan' will start to look younger.

Those who argue against this paper's assertions of a limit must be viewed just as critically as they judged the new paper.  The US National Institute on Aging, among other agencies, spends quite a lot of your money on aging, including decades (I know because I had some of it) on lifespan determination.  If someone quoted as dissing the new 'finding' is heavily engaged in the funding from NIA and elsewhere, one must ask whether s/he is defending a funding trough: if it's hopeless to think we'll make major longevity differences, why not close down their labs and instead spend the funding on something that's actually useful for society?

There are still many curious aspects of lifespan distributions, such as why rodents have small bodies that should be less vulnerable per-year to cancer or telomere degradation etc. that relate to the number of at-risk cells, yet only live a few years.  Why hasn't evolution led us to be in prime health for decades longer than we are?  There are potential answers to such questions, but mechanisms are not well understood, and the whole concept of a fixed lifespan (rather than a statistical one) is poorly constructed.

Still, everything suggests that, without major new interventions that probably will, at best, be for the rich only, there are rough limits to how long anyone can statistically avoid the range of independent risk our various organ systems face, not to  mention surviving in a sea of decrepitude.

One thing that does seem to be getting rather old, is the relentless hyperbole of the media including pop-culture journals like Nature and Science, selling non-stories as revolutionary new findings.  If we want to make life better for everyone, not just researchers and journals, we could spend our resources more equitably on quality of life, and our research resources on devastating diseases that strike early in the lives we already are fortunate to have.

Is life itself a simulation of life?

It often happens in science that our theory of some area of reality is very precise, but the reality is too complex to work out precisely, or analytically.  This can be when we decide to use computer simulation of that reality to get at least a close approximation to the truth.  When a phenomenon is determined by a precise process, then if we increase the complexity of our simulation, and if the simulation really is simulating the underlying reality, then the more computer power we apply, the closer we get to the truth--that is, our results approach that truth asymptotically.

For example, if you want to predict the rotation of galaxies in space relative to each other, and of the stars within the galaxies, the theories of physics will do the job, in principle. But solving the equations directly the way one does in algebra or calculus is not possible with so many variables.  However, you can use a computer to simulate the movement and get a very good approximation (we've discussed this here, among other places).  Thus, at each time interval, you take the position and motion of each object you want to follow, and those measures of nearby objects, and use Newton's law of gravity to predict the position of the objects one time interval later.

If the motion you simulate doesn't match what you can observe, you suspect you've got something wrong with the theory you are using. In the case of cosmology, one such factor is known as 'dark matter'.  That can be built into models of galactic motion, to get better predictions.  In this way, simulation can tell you something you didn't already know, and because the equations can't be directly solved, simulation is an approach of choice.

In many situations, even if you think that the underlying causal process is deterministic, measurements are imperfect, and you may need to add a random 'noise' factor to each iteration of your simulation.  Each simulation will be slightly 'off' because of this, but you run the same simulation thousands of times, so the effect of the noise evens out, and the average result represents what you are trying to model.

Is life a simulation of life?
Just like other processes that we attempt to simulate, life is a complex reality.  We try to explain it with the very general theory of evolution, and we use genetics to try to explain how complex traits evolve, but there are far too many variables to predict future directions and the like analytically.   This is more than just because of biological complexity however, in part because the fundamental processes of life seem, as far as we can tell, inherently probabilistic (not just a matter of measurement error).  This adds an additional twist that makes life itself seem to be a simulation of its underlying processes.

Life evolves by parents transmitting genes to offspring.  For those genes to be transmitted to the next generation, the offspring have to live long enough, must be able to acquire mates, and must be able to reproduce. Genes vary because mutations arise.  For simplicity's sake, let's say that successful mating requires not falling victim to natural selection before offspring are produced, and that that depends on an organism's traits, and that genes are causally responsible for those traits.  In reality, there are other process to be considered, but these will illustrate our point.

Mutation and surviving natural selection seem to be probabilistic processes.  If we want to simulate life, we have to specify the probability of a mutation along some simulated genome, and the probability that a bearer of the mutation survives and reproduces.  Populations contain thousands of individuals, genomes incur thousands of mutations each generation, and reproductive success involves those same individuals.  This is far too hard to write tractable equations for in most interesting situations, unless we make almost uselessly simplifying assumptions.  So we simulate these phenomena.

How, basically, do we do this?  Here, generically and simplified, but illustrating the issues, is the typical way (and the way taken by my own elaborate simulation program, called ForSim which is freely available):

For each individual in a simulated population, each generation, we draw a random number based on an assumed mutation rate, and add the resulting number and location of mutations to the genotype of the individual.  Then for each resulting simulated genotype, we draw a random number from the probability that such a genotype reproduces, and either remove or keep the individual depending on the result.  We keep doing this for thousands of generations, and see what happens.  As an example, the box lists some of the parameter values one specifies for a program like ForSim.



Sometimes, if the simulation is accurate enough, the probability and other values we assume look like what ecologists or geneticists believe is going on in their field site or laboratory.  In the case of humans, however, we have little such data, so we make a guess at what we think might have been the case during our evolution.  Often these things are empirically estimated one at a time, but their real values affect each other in  many ways.  This is, of course, very far from the situation in physics, described above!  Still, we at least have a computer-based way to approximate our idea of evolutionary and genetic processes.

We run this for many, usually many thousand generations, and see the trait and genomic causal pattern that results (we've blogged about some of these issues here, among other posts).  This is a simulation since it seems to follow the principles we think are responsible for evolution and genetic function.  However, there is a major difference.

Unlike simulations in astronomy, life really does seem to involve random draws for probabilistic processes.  In that sense, life looks like it is, itself, a simulation of these processes.  The random draws it makes are not just practical estimates of some underlying phenomenon, but manifestation of the actual probabilistic nature of the phenomenon.

This is important, because when we simulate a process, we know that its probabilistic component can lead to different results each time through.  And yet, life itself is a one-time run of those processes. In that sense, life is a simulation but we can only guess at the underlying causal values (like mutation and survival rates) from the single set of data: what actually happened its one time through.  Of course, we can test various examples, like looking at mutation rates in bacteria or in some samples of people, but these involve many problems and are at best general estimates from samples, often artificial or simplified samples.

But wait!  Is life a simulation after all?  If not, what is life?
I don't want us to be bogged down in pure semantics here, but I think the answer is that in a very profound way, life is not a simulation in the sense we're discussing.  For the relevant variables, life is not based on an underlying theoretical process in the usual sense, of whose parameters we use random numbers to approximate in simulations.

For example, we evaluate biological data in terms of 'the' mutation rate in genomes from parent to offspring.  But in fact, we know there is no such thing as 'the' mutation rate, one that applies to each nucleotide as it is replicated from one generation to the next, and from which each actual mutation is a random draw.  The observed rate of mutation at a given location in a given sample of a given species' genomes depends among other things on the sex, the particular nucleotides surrounding the site in question (and hence all sites along the DNA string), and the nature of the mutation-detection proteins coded by that individual's genome, and mutagen levels in the environment.  In our theory, and in our simulations, we assume an average rate, and that the variation from that average will, so to speak, 'average out' in our simulations.

But I think that is fundamentally wrong. In life, every condition today is a branch-point for the future. The functional implications of a mutation here and now, depend on the local circumstances, and that is built into the production of the future local generations.  Life in fact does not 'average' over the genome and over individuals does not in fact generate what life does, but in a sense the opposite.  Each event has its own local dynamics and contingencies, but the effect of those conditions affects the rates of events in the future.  Everywhere it's different, and we have no theory about how different, especially over evolutionary time.

Indeed, one might say that the most fundamental single characteristic of life is that the variation generated here today is screened here today and not anyplace else or any time else.  In that sense, each mutation is not drawn from the same distribution.  The underlying causal properties vary everywhere and all the time.  Sometimes the difference may be slight, but we can't count on that being true and, importantly, we have no way of knowing when and to what extent it's true.

The same applies to foxes and rabbits. Every time a fox chases a rabbit, the conditions (including the genotypes of the fox and rabbit) differ. The chance aspect of whether it's caught or not are not the same each time, the success 'rate' is not drawn from a single, fixed distribution.  In reality, each chase is unique.

After the fact, we can look back at net results, and it's all too tempting to think of what we see as a steady, deterministic process with a bit of random noise thrown in.  But that's not an accurate way to think, because we don't know how inaccurate it is, when each event is to some (un-prespecified) extent unique.  Overall, life is not, in fact, drawing from an underlying distribution.  It is ad hoc by its very nature and that's what makes life different from other physical phenomena.

Life, and we who partake of it, are unique. The fact of local, contingent uniqueness is an important reason that the study of life eludes much of what makes modern physical science work.  The latter's methods and concepts assume replicable law-like underlying regularity. That's the kind of thing we attempt to model, or simulate, by treating phenomena like mutation as if they are draws from some basic underlying causal distribution. But life's underlying regularity is its irregularity.

This means that one of the best ways we have of dealing with complex phenomena of life, simulating them by computer, smoothes over the very underlying process that we want to understand.  In that sense, strangely, life appears to be a simulation but is even more elusive than that.  To a great extent, except by some very broad generalities that are often too broad to be very useful, life isn't the way we simulate it, and doesn't even simulate itself in that way.

What would be a better approach to understanding life?  The next generation will have to discover that.

Confessions of a Contrarian

If you care about some area of human life, and you look at it closely, it probably won't take you long to become a contrarian about it: to know it is to see its weaknesses.  Our fallibilities as a species are everywhere to be found. We could just accept that and motor along, or we can recognize, react, and resist!  Unfortunately, the older you get and perhaps the more secure (so you can afford to criticize), the more flaws you may see and the more you may wish to try to change them.

That often means criticizing, in part because better ways of doing things may not be obvious, thus suggesting positive changes not so easy.  Of course, established parts of society resist change.  The 'in's' usually want to protect their privilege, resources, and status quo.  The young may not see the issues or may be intimidated by the structures because it's risky to critique them (making it harder to get jobs, grants, etc.).  Resistance is difficult and often doesn't work.

But if you've read much of what we have to say here on MT, you know that I, at least, feel that pointing out problems is important, despite the obstacles.  Change often means mass, and often grass-roots, resistance.  But first, it requires recognition of the problems and that often in turn requires repetition.  If something is worth saying, and it's something people don't want to hear because it may threaten comfortable business-as-usual, it's worth repeating.  I do that sort of repetition here, but usually in the context of reasons for my view and/or thoughts about how to change things.

Some may see this as contrarian.  To that, I must plead guilty.

We here are not the only ones to note what is going on in terms of funding, careers in science, hyperbole in science reporting, PhDs without jobs or careers, but lots of debt, well into their 30's, the huge locked-in funding system of well-heeled, locked-in un-killable projects, universities hungry for more graduate students even though this mainly is for their own bragging rights (since the students aren't getting jobs), and so on.  These are real problems worth crabbing about.

Also, the inertia of science is such that, at present, we are ever-increasing the size and duration of projects that essentially just do the same thing as has been done for years before, with only minor tweaks in technology and major increases in scale, as if scale is a good substitute for thought--indeed, it's often presented that way, in the common implicit or even sometimes explicit boasting about hypothesis-free science.

Albert Einstein is often credited (falsely, apparently) with defining insanity as continuing to do the same thing and hoping the result will be different.   Even showing deep flaws in what is going on is often presented as a reason for doing even more of the same--for example, the commentary and article by Lek et al. in the 18 August issue of Nature.  Similar points can be made about the highly publicized issues of statistical inference that we've posted about before.  Nobody wants to, or perhaps knows how to, or dares to say that what we're doing is continuing along a path of wishful-thinking. Science is an Establishment that is naturally inertial and resists change.  But I believe the problems need to be pointed out.

Wendell Berry; Wikipedia (photo by By Guy Mendes) 

It may take a contrarian to assert this point of view.  Doing so may be in vain, but it isn't pointless and repetition does not make it false.  Our concern here on MT is about science and its position in society (I can be contrarian about other things, too!).  I try to be responsible in presenting my view, and to explain and to justify it. I am afraid I don't do it with eloquence.  But the poet-contrarian Wendell Berry, whom I have had the privilege of meeting a few times, does, having expressed contrariness quite well, as you can see on the OnBeing blogsite, and here's their link to Wendell reading it himself.  The poem begins, "I am done with apologies. If contrariness is my/inheritance and destiny, so be it. If it is my mission/to go in at exits and come out at entrances, so be it."  I won't complain if you leave here to go read the rest.

FAS - Fishy Association Studies

           

                                  On Saturday, July 19, 1879, the brilliant opera 

                                  composer, Richard Wagner, "had a bad night; 

                                  he thinks that...he ate too much trout."  

                                             Quoted from Cosima Wagner's Diary, Vol. II, 1878-83.

As I was reading Cosima Wagner's doting diary of life with her famous husband, I chanced across the above quote that seemed an appropriate, if snarky, way to frame today's post. The incident she related exemplifies how we routinely assign causation even to one-off events in daily life. Science, on the other hand, purports to be about causation of a deeper sort, with some sufficient form of regularity or replicability.

Cause and effect can be elusive concepts, especially difficult to winnow out from observations in the complex living world.  We've hammered on about this on MT over the years.  The best science at least tries to collect adequate evidence in order to infer causation in credible rather than casual ways. There are, for example, likely to be lots of reasons, other than eating trout, that could explain why a cranky genius like Wagner had a bad night.  It is all too easy to over-interpret associations in causal terms.

By such thinking, the above figures (from Wikimedia commons) might be interpreted as having the following predictive power:
     One fish = bad night
     Two fish = total insomnia
     Many fish = hours of nightmarish dissonance called Tristan und Isolde!

Too often, we salivate over GWAS (genomewide association studies) results as if they justify ever-bigger and longer studies.  But equally too often, these are FAS, fishy association studies.  That is what we get when the science community doesn't pay heed to the serious and often fundamental difficulties in determining causation that may well undermine their findings and the advice so blithely proffered to the public.

We are not the only ones who have been writing that the current enumerative, 'Big Data', approach to biomedical and even behavior genetic causation leaves, to say the least, much to be desired.  Among other issues, there's too much asserting conclusions on inadequate evidence, and not enough recognition of when assertions are effectively not that much more robust than saying one 'ate too much trout'.  Weak statistical associations, so typically the result of these association studies, are not the same as demonstrations of causation.

The idea of mapping complex traits by huge genomewide case-control or population sample studies is a captivating one for biomedical researchers.  It's mechanical, perfectly designed to be done by huge computer database analysis by people who may never have seen the inside of a wet lab (e.g., programmers and 'informatics' or statistical specialists who have little serious critical understanding of the underlying biology).  It's often largely thought-free, because that makes the results safe to publish, safe for getting more grants, and so on; but more than being 'captivating' it is 'capturing'.... a hog-trough's share of research resources.

The promise, not even always carefully hedged with escape-words lest it be shown to be wrong, is that from your genome your future biomedical (and behavioral) traits can be known.  A recent article in the July 28 issue of the Journal of the American Medical Association (JAMA), Joyner et al. describes the stubborn persistence of under-performing but costly research that becomes entrenched, a perpetuation that NIH's misnomered 'precision based genomic medicine' continues or even expands upon. Below is our riff on the article, but it's open-source so you can read the points they make and judge for yourself if we have the right 'take' on what they say.  It is one of many articles that have been making similar points....in case anyone is listening.

The problem is complex causation
The underlying basic problem is the complex nature of causation of 'complex' traits, like many if not most behavioral or chronic or late-onset diseases. The word complex, long-used for such traits, refers not to identified causes but to the fact that the outcomes clearly did not have simple, identified causes.  It seemed clear that their causation was due mainly to countless combinations of many individually small causal factors, some of which were inherited; but the specifics were usually unknown. Computer and various DNA technologies made it possible, in principle, to identify and sort through huge numbers of possible causes or at least statistically associated factors, including DNA sequence variants.  But underlying this source for this approach has been the idea, always a myth really, that identifying some enumerated set of causes in a statistical sample would allow accurate prediction of outcomes.  This has proven not to be the case nearly as generally as has been promised.

To me, the push to do large-scale huge-sample, survey-based genomewide risk analysis was at least partly justified, at least in principle, years ago when there might have been some doubt about the nature of the causal biology underlying complex traits, including the increasingly common chronic disease problems that our aging population faces.  But the results are in, and in fact have been in for quite a long time.  Moreover, and a credit to the validity of the science, is that the results support what we had good reason to know for a long time.  The results show that this approach is not, or at least clearly no longer the optimal way to do science in this area or contribute to improving public health (and much of the same applies to evolutionary biology as well).

I think it fair to say that I was making these points, in print, in prominent places, starting as long ago as nearly 30 years, in books and journal articles (and more recently here on MT), that is, ever since the relevant actual data were beginning to appear.  But neither I nor my collaborators were the original discoverers of this insight: instead, the basic truth has been known in principle and in many empirical experimental (such as agricultural breeding) and observational contexts, for nearly a century! Struggling with the inheritance of causal elements ('genes' as they were generically known), the 1930s' 'modern synthesis' of evolutionary biology reconciled (1) Darwin's idea of gradual evolution, mainly of quantitative traits, with the experimental evidence of the quantitative nature of their inheritance, and (2) the discrete nature of inheritance of discrete causal elements first systematically demonstrated by Mendel for selected 2-state traits.  That was a powerful understanding but in too many ways it has thoughtlessly been taken to imply that all traits, not just genes, are usefully 'Mendelian', due to substantial, enumerable, strongly causal genetic agents.  That has always been the exception, not the rule.

A view is possible that is not wholly cynical 
We have been outspoken about the sociocultural aspect of modern research, which can be understood by what one might call the FTM (Follow the Money) approach, in some ways a better way to understand where we are than looking at the science itself.  Who has what to gain by the current approaches?  Our understanding is aided by realizing that the science is presented to us by scientists and journalists, supplier industries and bureaucrats, who have vested interests that are served by promoting that way of doing business.

FTM isn't the only useful perspective, however.  A less cynical, and yet still appropriate way to look at this is in terms of diminishing returns.  The investment in the current way of doing science in this (and other areas) is part of our culture.  From a scientific point of view, the first forays into a new way or approach, or a theoretical idea, yield quick and, by definition, new results.  Eventually, it becomes more routine and the per-study yield diminishes. We asymptotically approach what we can glean from the approach.  Eventually some chance insight will yield some forms of better and more powerful approaches, whatever they'll be.

If current approaches were just yielding low-cost incremental gain, or were being done in well-off investigators' basement labs, it would be a normal course of scientific-history, and nobody would have reason to complain.  But that isn't how it works these days.  These days understanding via FTM is important: the science establishment's hands are in all our pockets, and we should expect more in return than the satisfaction that the trough has been feeding many very nice careers (including mine), in universities, journalism, and so on.  How, when, and where a properly increased expectation of science for societal benefits will be fulfilled is not predictable, because facts are elusive and Nature often opaque.  However, simply more-of-the-same, at its current costs, with continuing entrenched justification, isn't the best way for public resources to be used.

There will always be a place for 'big data' resources.  A unified system of online biomedical records would save a lot of excess repeat-testing and other clinical costs, if every doctor you consult could access those records.  The records could potentially be used for research purposes, to the (limited) extent that they could be informative.  For a variety of conditions that would be very useful and cost-effective indeed; but most of those would be relatively rare.

Continuing to pour research funds into the idea that ever more 'data' will lead to dramatic improvements of 'precision' medicine is far more about the health of entrenched university labs and investigators than that of the general citizenry. Focused laboratory work that is more rigorously supported by theory or definitive experiment, with some accountability (but no expectations nor promises of miracles) is in order, given what the GWAS etc. era, plus a century of evolutionary genetics, has shown. There are countless areas, especially many serious early onset diseases, for which we have a focused, persuasive, meaningful understanding of causation and where resources should now be invested more heavily.

Intentionally open-ended beetle collecting ventures joined at the hip to promises of 'precision' without those promising even knowing what that word means (but hinting that it means 'perfection'), or glorifying the occasional seriously good findings as if they are typical or as though more focussed, less open-ended research wouldn't be a better investment, is not a legitimate approach.  Yet that is largely what is going on today.  The scientists, at least the smart ones, know this very well and say so (in confidence, of course).

Understanding complex causation is complex, and we have to face up to that.  We can't demand inexpensive or instant or even predictable answers.  These are inconvenient facts few want to face up to.  But we and others have said this ad nauseam before, so here we wanted to point out the current JAMA paper as yet another formal and prominently published realization of the costly inertia in which we are embedded, and by highly capable authors. In any aspect of society, not just science, prying resources loose from the hands of a small elite is never easy, even when there are other ways to use those resources that might have better payoff for all of us.

Usually, such resource reallocation seems to require some major new and imminent external threat, or some unpredicted discovery, which I think is far more likely to come from some smaller operation where thinking was more important than cranking out yet another mass-scale statistical survey of Big Data sausage.  Still, every push against wasteful inertia, like the Joyner et al. JAMA paper,  helps. Indeed, those many whose careers are entrapped by that part of the System have the skills and neuronal power to do something better if circumstances enabled it to happen more readily.  To encourage that, perhaps we should stop paying so much attention to Fishy stories.

When scientific theory constrains

It's good from time to time to reflect on how we know what we think we know.  And to remember that, as it has been in any time in history, much of what we now think is true will sooner or later be found to be false or, often, only inaccurately or partially correct.  Some of this is because values change -- not so long ago homosexuality was considered to be an illness, e.g.  Some is because of new discoveries -- when archaea were first discovered they were thought to be exotic microbes that inhabited extreme environments but now they're known to live in all environments, even in and on us. And of course these are just two of countless examples.

But what we think we know can be influenced by our assumptions about what we think is true, too. It's all too easy to look at data and interpret it in a way that makes sense to us, even if there are multiple possible interpretations.  This can be a particular problem in social science, when we've got a favorite theory and the data can be seen to confirm it; this is perhaps easiest to notice if you yourself aren't wedded to any of the theories.  But it's also true in biology. It is understandable that we want to assert that we now know something, and are rewarded for insight and discoveries, rather than more humbly hesitating to make claims.

Charitable giving
The other day I was listening to the BBC Radio 4 program Analysis on the charitable impulse.  Why do people give to charity?  It turns out that a lot of psychological research has been done on this, to the point that charities are now able to manipulate us into giving.  If you call your favorite NPR station to donate during a fund drive, e.g., if you're told that the caller just before you gave a lot of money, you're more likely to make a larger donation than if you're told the previous caller pledged a small amount.

A 1931 advertisement for the British charity, Barnardo's Homes; Wikipedia

Or, if an advertisement pictures one child, and tells us the story of that one child, we're more likely to donate than if we're told about 30,000 needy children.  This works even if we're told the story of two children, one after the other.  But, according to one of the researchers, if we're shown two children at once, and told that if we give, the money will randomly go to just one of the children, we're less likely to give.  This researcher interpreted this to mean that two is too many.

But there seem to me to be other possible interpretations given that the experiment changes more than one variable.  Perhaps it's that we don't like the idea that someone else will choose who gets our money.  Or that we feel uncomfortable knowing that we've helped only one child when two are needy.  But surely something other than that two is too many, given that in 2004 so many people around the world donated so much money to organizations helping tsunami victims that many had to start turning down donations.  These were anonymous victims, in great numbers.  Though, as the program noted, people weren't nearly as generous to the great number of victims of the earthquake in Nepal in 2015, with no obvious explanation.

The researcher did seem to be wedded to his one vs too many interpretation, despite the contradictory data.  In fact, I would suggest that the methods, given what were presented, don't allow him to legitimately draw any conclusion.  Yet he readily did.

Thinness microbes?
The Food Programme on BBC Radio 4 is on to the microbiome in a big way.  Two recent episodes (here and here) explore the connection between gut microbes, food, and health and the program promises to update us as new understanding develops.  As we all know by now, the microbiome, the bug intimates that accompany us through life, in and on our body, may affect our health, our weight, our behavior, and perhaps much more.  Or not.

Pseudomonas aeruginosa, Enterococcus faecalis and Staphylococcus aureus on Tryptic Soy Agar.  Wikipedia

Obesity, asthma, atopy, periodontal health, rheumatoid arthritis, Parkinson's, Alzheimer's, autism, and many many more conditions have been linked with, or are suggested to be linked with, in one way or another, our microbiome.  Perhaps we're hosting the wrong microbes, or not a diverse enough set of microbes, or we wipe the good ones out with antibiotics along with the bad, or with alcohol, and what we eat may have a lot to do with this.

One of the researchers interviewed for the program was experimenting with a set of identical twins in Scotland.  He varied their diets having them eat, for example, lots of junk food and alcohol, or a very fibrous diet, and documented changes in their gut microbiomes which apparently can change pretty quickly with changes in diet.  The most diverse microbiome was associated with the high fiber diet. Researchers seem to feel that diversity is good.

Along with a lot of enthusiasm and hype, though, mostly what we've got in microbiome research so far is correlations.  Thin people tend to have a different set of microbes than obese people, and people with a given neurological disease might statistically share a specific subset of microbes.  But this tells us nothing about cause and effect -- which came first, the microbiome or the condition?  And because the microbiome can change quickly and often, how long and how consistently would an organism have to reside in our gut before it causes a disease?

There was some discussion of probiotics in the second program, the assumption being that controlling our microbiome affects our health.  Perhaps we'll soon have probiotic yogurt or kefir or even a pill that keeps us thin, or prevents Alzheimer's disease.  Indeed, this was the logical conclusion from all the preceding discussion.

But one of the researchers, inadvertently I think, suggested that perhaps this reductionist conclusion was unwarranted.  He cautioned that thinking about probiotic pills rather than lifestyle might be counterproductive.  But except for factors with large effects such as smoking, the effect of "lifestyle" on health is rarely obvious.  We know that poverty, for example, is associated with ill health, but it's not so easy to tease out how and why.  And, if the microbiome really does directly influence our health, as so many are promising, the only interesting relevant thing about lifestyle would be how it changes our microbiomic makeup.  Otherwise, we're talking about complexity, multiple factors with small effects -- genes, environmental factors, diet, and so on, and all bets about probiotics and "the thinness microbiome" are off.  But, the caution was, to my mind, an important warning about the problem of assuming we know what we think we know; in this case, that the microbiome is the ultimate cause of disease.

The problem of theory
These are just two examples of the problem of assumption-driven science. They are fairly trivial, but if you are primed to notice, you'll see it all around you. Social science research is essentially the interpretation of observational data from within a theoretical framework. Psychologists might interpret observations from the perspective of behavioral, or cognitive, or biological psychology, e.g., and anthropologists, at least historically, from, say, a functionalist or materialist or biological or post-modernist perspective. Even physicists interpret data based on whether they are string theorists or particle physicists.

And biologists' theoretical framework? I would suggest that two big assumptions that biologists make are reductionism and let's call it biological uniformitarianism. We believe we can reduce causation to a single factor, and we assume that we can extrapolate our findings from the mouse or zebrafish we're working on to other mice, fish and species, or from one or some people to all people. That is, we assume invariance rather than that what we can expect is variation. There is plenty of evidence to show that by now we should know better.

True, most biologists would probably say that evolutionary theory is their theoretical framework, and many would add that traits are here because they're adaptive, because of natural selection. Evolution does connect people to each other and people to other species, it has done so by working on differences, not replicated identity, and there is no rule for the nature or number of those differences or for extrapolating from one species or individual to another. We know nothing to contradict evolutionary theory, but that every trait is adaptive is an assumption, and a pervasive one.

Theory and assumption can guide us, but they can also improperly constrain how we think about our data, which is why it's good to remind ourselves from time to time to think about how we know what we think we know. As scientists we should always be challenging and testing our assumptions and theories, not depending on them to tell us that we're right.

Mammoth ego above all

From time to time our genetics colleagues propose projects that perhaps belong more to Hollywood or the video industry.  At least that way no inadvertent harm could be done.  But, hell, a big ego splash or some book royalties might make one a bit reckless.

I refer here to ideas about cloning extinct animal species from DNA rescued from preserved bone or tissues. Some recent species, like Dodos, might be directly clonable in some way, and in some senses could have some value--assuming that making one or a few of them is good for someone--themselves, that is, not just zoo-goers.

However, how do you do this?  The best idea would be to take an entire cell, re-set its DNA/RNA status to that of a fertilized egg, and let 'er rip.  But how?  Unless you can make that cell develop all by itself in a Petri dish, the answer isn't obvious.  The usual idea, at least for mammals, is to find a surrogate mother.

Generally the approach has been, for mammals at least, to extract a complete nuclear genome from a fossil specimen, take a surrogate mother's egg cell (from its ovary), remove its nuclear DNA, replace it with the fossil's DNA, and inject it into a hormonally prepared mother for gestation.  This is basically the 'Dolly' approach and to my (clearly incomplete) knowledge it is still the basic idea.  But there are problems with this, if you really want to revive a species as opposed just to getting a farm-worthy sheep or the like.

One problem is that you need the host-species' egg, and that means its mitochondrial DNA (mtDNA, which is not in the nucleus) and the RNA and other contents of the host egg cell.  Even if you can use an entire intact nuclear genome, the resulting new individual is necessarily a hybrid between its mother and the other exogenous DNA.  For agriculture, there are some well-known problems such as the fact that the host's cell will reflect some aspects of the host, like mutations in mtDNA or the RNA from its nucleus, and so on.  The new individual is in some senses already a bit old, for example.

These problems can probably be dealt with, or perhaps have already been.  But with extinct species there are other problems.  Among them is that the DNA is usually not entirely intact in any cell. From fossils or extinct species, it may be degraded into very small fragments.  By sequencing currently living relative species, we can identify roughly what part of the genome those fragments come from, and if we have several samples we could perhaps find in some sample each necessary fragments.  That's still not the same as having intact chromosomes, and almost certainly some large subset will be missing.  Then, however, you have to find the surrogate mother, and if the species is extinct you have to use a current, similar species to do this.

Mammoths and Neanderthals . . . . 
Let's for the moment ignore the fact that the cloned individual in these cases really won't be a cloned individual of its original species, but some kind of hybrid or chimera.  Suppose the attempt were made.  What then?

Two of the favorite species that the raw, I'd say cruel Frankenstein egotism and its simplistic portrayal of the idea to the public, are Neanderthals and mammoths.  Maybe the suggestions have only been publicity stunts, given the real issues in the world that serious geneticists could address, but they seem to have been offered seriously, and they show to me how science needs to be kept under control.

Neanderthals and mammoths are social beings.  Real ones were gestated in mothers of their own kind, eating their natural diet, in their natural social (and hence hormonal, physiological) circumstances, and born to families and nurturing, educating, protecting, natural social groups.  Once born, they were incorporated into the group in a way that they presumably evolved to be.

How should a cloned Neanderthal be treated?  It would be basically human: recall that we all carry some Neanderthal inheritance through extensive inter-mating way back then. Would it have civil rights, such as to education, a home, voting?  Could one experiment on it, to test aspects of its physiology? Could it be put on display in a zoo or some university museum?

What about mere mammoths?  One leading Mary Shelly imitator has suggested that cloned mammoths could be plunked back into 'natural' conditions in the Arctic, where there aren't (currently) too many humans around to destroy their environment--assuming, in a fantasy way, that commercial tour groups would not be immediately organized, to go gawk at them (including by 'scientists' who want then to publish papers in Nature on their behavior), and that they wouldn't be kept in zoos or labs in our august universities.  Such innocent-sounding ideas as cloning these extinct beings are far more egotism and ignorance--or willful avoidance of realities--than any sort of sound proposal.

Like humans, mammoths are social beings.  The Arctic today would not be a hospitable or normal place for a mammoth.  A surrogate elephant mother would not gestate a normal mammoth, in ways large or small that we can't really anticipate or even know.  It would be plunked down by itself or in a pack of clones that didn't have normal social life to be raised in.  Even if it had a mother who loved it rather than rejecting it as a freak, it would not be a mammoth mother, nor would its life  replicate the mammoth's natural existence as a herd species.  It is cruelty to suggest otherwise.

In memoriam, or a lesson never learned?
Let's not forget one Ota Benga, an African man, obtained from a slave trader, and exhibited at the St Louis World Fair in 1904 and then put on display--is there a better way to state this?--in the Bronx zoo!

From Benga Wikipedia page

Baartman, from her Wikipedia page

Nor let us forget poor Saartjie Baartman, a southern African native, put on display, naked, around Europe to exhibit her exaggerated private parts, in early 19th century Europe  Traveling freak shows, might one say?

It is to be noted that the people involved were leaders of their time, and people responsible for such dehumanizing practices have included prominent scientists at major universities.  Anthropologist Franz Boas requested that explorer Robert Peary bring an Inuk back from the Arctic, to be studied by scientists at Columbia.

Minik Wallace (also called Minik or Mene ) (ca. 1890 – October 29, 1918) was an Inuk brought as a child in 1897 from Greenland to New York with his father and others by the explorer Robert Peary. The six Inuit were studied by staff of the American Museum of Natural History, which had custody. The adults and one child died soon of tuberculosis (TB), and one young man was returned to Greenland. After deceiving Minik by a staged burial, the museum put the skeleton of his father on exhibit. Adopted by the museum's building superintendent, William Wallace, Minik did not return to Greenland until after 1910. A few years later, he came back to the United States, where he lived and worked until dying of influenza in the 1918 pandemic.  (Wikipedia)

. . . . . and nobody to defend them
In our age of science, the idea of cloning extinct species seems exciting and appealing on the surface, a kindly sort of rescue--until you think about it carefully.  As you can tell, I feel quite strongly about this.  It's playing games with other sentient organisms' lives for no seriously justifiable purpose.  If it's hard enough to justify or excuse what we allow to be done with mice and other species for research purposes (and I include myself among the accused), this is beyond beyond.

To me, personally, these juicy cloning suggestions reflect the cold, selfish, arrogance of science, and scientists in leading universities.  The poor target species have nobody to defend them, and that is why these proposals, irresponsibly presented in the media, can get off the ground.  One sees little censure of these ghoulish proposals, whose presumptive nature is essentially a continuation of the eugenics movement.

The video industry is as close as we need to get to these notions.  Let us not forget our history.