Friday, December 16, 2016

Is genetics still metaphysical? Part III. Or could that be right after all?

In the two prior parts of this little series (I and II), we've discussed the way in which unknown, putatively causative entities were invoked to explain their purported consequences, even if the agent itself could not be seen or its essence characterized.  Atoms and an all-pervasive ether are examples. In the last two centuries, many scientists followed some of the principles laid down in the prior Enlightenment period, and were intensely empirical, to avoid untrammeled speculation.  Others followed long tradition and speculated about the underlying essentials of Nature that could account for the empiricists' observations. Of course, in reality I think most scientists, and even strongly religious people, believed that Nature was law-like: there were universally true underlying causative principles.  The idea of empiricism was to escape the unconstrained speculation that was the inheritance even from the classical times (and, of course, from dogmatic religious explanations of Nature).  Repeated observation was the key to finding Nature's patterns, which could only be understood indirectly.  I'm oversimplifying, but this was largely the situation in 19th and early 20th century physics and it became true of historical sciences like geology, and in biology during the same time.

At these stages in the sciences, free-wheeling speculation was denigrated as delving in metaphysics, because only systematic empiricism--actual data!--could reveal how Nature worked. I've used the term 'metaphysics' because in the post-Enlightenment era it has had and been used in a pejorative sense.  On the other hand, if one cannot make generalizations, that is, infer Nature's 'laws', then one cannot really turn retrospective observation into prospective prediction.

By the turn of the century, we had Darwin's attempt at Newtonian law-like invocation of natural selection as a universal force for change in life, and we had Mendel's legacy that said that causative elements, that were dubbed 'genes', underlay the traits of Nature's creatures.  But a 'gene' had never actually been 'seen', or directly identified until well into the 20th century. What, after all, was a 'gene'? Some sort of thing?  A particle?  An action?  How could 'it' account for traits as well as their evolution?  To many, the gene was a convenient concept that was perhaps casually and schematically useful, but not helpful in any direct way.  Much has changed, or at least seems to have changed since then!

Genetics is today considered a mainline science, well beyond the descriptive beetle-collecting style of the 19th century.  We now routinely claim to identify life's causative elements as distinct, discrete segments of DNA sequence, and a gene is routinely treated as causing purportedly 'precisely' understandable effects.  If raw Big Data empiricism is the Justification du Jour for open-ended mega-funding, the implicit justifying idea is that genomics is predictive the way gravity and relativity and electromagnetism are--if only we had enough data!  Only with Big Data can we identify these distinct, discrete causal entities, characterize their individual effects and use that for prediction, based on some implicit theory or law of biological causation.  It's real science, not metaphysics!

But even with today's knowledge, how true is that?

The inherent importance of context-dependency and alternative paths
It seems obvious that biological causation is essentially relative in nature: it fundamentally involves context and relationships.  Treating genes as individual, discrete causal agents really is a form of metaphysical reification, not least because it clearly ignores what we know about genetics itself. As we saw earlier, today there is no such thing as 'the' gene, much less one we can define as the discrete unit of biological function.  Biological function seems inherently about interactions.  The gene remains in that sense, to this day, a metaphysical concept--perhaps even in the pejorative sense, because we know better!

We do know what some 'genes' are: sequences coding for protein or mature RNA structure.  But we also know that much of DNA has function unrelated to the stereotypical gene.  A gene has multiple exons and often differently spliced (among many other things, including antisense RNA post-transcription regulation, and RNA editing), combined with other 'genes' to contribute to some function.  A given DNA coding sequence often is used in different contexts in which 'its' function depends on local context-specific combinations with other 'genes'.  There are regulatory DNA sequences, sequences related to the packaging and processing of DNA, and much more.  And this is just the tip of the current knowledge iceberg; that is, we know there's the rest of the iceberg not yet known to us.

Indeed, regardless of what is said and caveats offered here and there as escape clauses, in practice it is routinely assumed that genes are independent, discrete agents with additive functional effects, even though this additivity is a crude result of applying generic statistical rather than causal models, mostly to whole organisms rather than individual cells or gene products themselves.  Our methods of statistical inference are not causal models as a rule but really only indicate whether, more probably than not, in a given kind of sample and context a gene actually 'does' anything to what we've chosen to measure. Yes, Virginia, the gene concept really is to a great extent still metaphysical.

But isn't genomic empiricism enough?  Why bother with metaphysics (or whatever less pejorative-sounding term you prefer)? Isn't it enough to identify 'genes', however we do it, and estimate their functions empirically, regardless of what genes actually 'are'?  No, not at all.  As we noted yesterday, without an underlying theory, we may sometimes be able to make generic statistical 'fits' to retrospective data, but it is obvious, even in some of the clearest supposedly single-gene cases, that we do not have strong bases for extrapolating such findings in direct causal or predictive terms.  We may speak as if we know what we're talking about, but those who promise otherwise are sailing as close to the wind as possible.

That genetics today is still rather metaphysical, and rests heavily on fancifully phrased but basically plain empiricism, does not gainsay that fact that we are doing much more than just empiricism, in many areas, and we try to do that even in Big Promise biomedicine.  We do know a lot about functions of DNA segments.  We are making clear progress in understanding and combatting diseases and so on.  But we also know, as a general statement, that even in closely studied contexts, most organisms have alternative pathways to similar outcomes and the same mutation introduced into different backgrounds (in humans, because the causal probabilities vary greatly and are generally low, and in different strains of laboratory animals) often has different effects.  We already know from even the strongest kind of genetic effects (e.g., BRCA1 mutations and breast cancer) that extrapolation of future risk from retrospective data-fitting can be grossly inaccurate.  So our progress is typically a lot cruder than our claims about it.

An excuse that is implicit and sometimes explicit is that today's Big Data 'precision, personalized' medicine, and much of evolutionary inference, are for the same age-old argument good simply because they are based on facts, on pure empiricism, not resting on any fancy effete intellectual snobs' theorizing:  We know genes cause disease (and everything else) and we know natural selection causes our traits.  And those in Darwinian medicine know that everything can be explained by the 'force' of natural selection.  So just let us collect Big Data and invoke these 'theories' superficially as justification, and mint our predictions!

But--could it be that the empiricists are right, despite not realizing why?  Could it be that the idea that there is an underlying theory or law-like causal reality, of which Big Data empiricism provides only imperfect reflections, really is, in many ways, only a hope, but not a reality?

Or is life essentially empirical--without a continuous underlying causal fabric?
What if Einstein's dream of a True Nature, that doesn't play dice with causation, was a nightmare.  In biology, in particular, could it be that there isn't a single underlying, much less smooth and deterministic, natural law?  Maybe there isn't any causal element of the sort being invoked by terms like 'gene'.  If an essential aspect of life is its lack of law-like replicability, the living world may be essentially metaphysical in the usual sense of there being no 'true' laws or causative particles as such. Perhaps better stated, the natural laws of life may essentially be that life does not following any particular law, but is determined by universally unique local ad hoc conditions.  Life is, after all, the product of evolution and if our ideas about evolution are correct, it is a process of diversification rather than unity, of local ad hoc conditions rather than universal ones.

To the extent this is the reality, ideas like genes may be largely metaphysical in the common sense of the term.  Empiricism may in fact be the best way to see what's going on.  This isn't much solace, however, because if that's the case then promises of accurate predictability from existing data may be culpably misleading, even false in the sense that a proper understanding of life would be that such predictions won't work to a knowable extent.

I personally think that a major problem is our reliance on statistical analysis and its significance criteria, that we can easily apply but that have at best only very indirect relationship to any underlying causal fabric, and that 'indirect' means largely unknowably indirect. Statistics in this situation is essentially about probabilistic comparisons, and has little or often no basis in causal theory, that is, in the reason for observed differences.  Statistics work very well for inference when properly distributed factors, such as measurement errors, are laid upon some properly framed theoretically expected result.  But when we have no theory and must rely on internal comparisons and data fitting, as between cases and controls, then we often have no way to know what part of our results has to do with sampling etc. and where any underlying natural laws, might be in the empirical mix--if such laws even exist.

Given this situation, the promise of 'precision' can be seen starkly as a marketing ploy rather than knowledgeable science.  It's a distraction to the public but also to the science itself, and that is the worst thing that can happen to legitimate science.  For example, if we can't really predict based on any serious-level theory, we can't tell how erroneous future predictions will be relative to existing retrospective data-fitting so we can't, largely even in principle, know how much this Big Data romance will approximate any real risk truths, because true risks (of some disease or phenotype) may not exist as such or may depend on things, like environmental exposures and behavior, that cannot be known empirically (and perhaps not even in theory), again, even in principle.

Rethinking is necessary, but in our current System of careerism and funding, we're not really even trying to lay out a playing field that will stimulate the required innovation in thought.  Big Data advocates sometimes openly, without any sense of embarrassment, say that serendipity will lead those with Big Data actually to find something important.  But deep insight may not be stimulated as long as we aren't even aware that we're eschewing theory basically in favor of pure extrapolated empiricism--and that we have scant theory even to build on.

There are those of us who feel that a lot more attention and new kinds of thinking need to be paid to the deeper question of how living Nature 'is' rather than very shaky empiricism that is easy, if costly, to implement but whose implications are hard to evaluate. Again, based on current understanding, it is quite plausible that life, based on evolution which is in turn based on difference rather than replicability, simply is not a phenomenon that obeys natural law in the way oxygen atoms, gravity, and even particle entanglement do.

To the extent that is the case, we are still in a metaphysical age, and there may be no way out of it.

1 comment:

Ken Weiss said...

Here's a very interesting article on ants and the incorrect ideas about how rigidly their social roles are programmed (i.e., they're not so highly programmed):

This is relevant to this post series, I think, because the gene is somewhat similarly held as a kind of metaphor for aspects of human society in terms of it being a mater controller of life etc. We live with lots of inaccurate metaphors, even in science. One can suggest reasons, but the important thing is to realize what we're doing and try to do better. Anthropologically speaking, we seem to prefer simple accounts of the world, however, and naturally model them after the structural nature of the societies in which we live.