Case 4
Living systems encompass a staggering range of scales, from the
interactions of individual molecules to the dynamics of whole
populations. The factors that govern the way they function -- the
material properties, the physical forces at work, the resources required
-- vary a great deal across those scales. (For example, the effects of
gravity are negligible at the molecular level, and nearly so for cells,
but often decisive at the larger phenotypic scales.) Mapping the
relationships between the things happening at each level is not only
mind-bogglingly difficult, it's also not at all clear that doing so will
provide meaningful answers to systemic questions.
The consequences of low-level activities at higher levels tend to be
impossible to predict a priori1. The mess of chemical
reactions going on around DNA give rise, through a looney set of
intermediate steps, to all kinds of protein molecules, which in turn
contribute in unexpected ways to changes in the structure and behaviour
of the containing cell, which in turn nudge other cells into slightly
different states, which in turn...
...until eventually here I am, writing these notes, and there
you are, reading them.
The remarkable feature of living systems is not, as the IDiots
bleat, that simple processes give rise to complexity -- we're all
familiar with that. Rather, it is the cohesive simplicity of the
outcome. It would be easy to imagine the low-level complications
aggregating up to create something ever more chaotically unpredictable,
but they don't. The intricacies smooth away and order emerges,
intimately dependent on what goes on below and yet astonishingly robust.
One aspect of cellular robustness is an armoury of techniques for
dealing with invaders. Like everything in life, these can be rather
ad hoc: just random heuristics that happened along and -- by
increasing the ability to reproduce successfully -- became endemic. One
such, which has only become understood recently, forms the basis of a
neat method for investigating experimentally the workings of individual
genes and their contributions to the overall behaviour of cells and
organisms.
The familiar double helix of DNA is the storehouse of genetic
information in cells, and almost all of it lives in the nucleus. To
actually use the information, individual genes are transcribed into
single strands of a related molecule, RNA, which then travels
outside the nucleus to the bits of cell machinery that build the
proteins from its template.
Viruses are much simpler. They don't have those bits of cell machinery
at all, relying on being able to hijack ours, and many don't bother
with the niceties of DNA. Instead, their genetic information is encoded
directly as RNA; in some cases as double strands.
Since double stranded RNA almost never occurs natively in eukaryotic
cells, its presence is an indicator that the sequences in question might
be alien and dangerous. There are protein complexes in the cell that
identify those sequences and stomp on them, suppressing their
expression. This ability is present throughout the eukaryote kingdom and
is obviously a pretty successful defence, but it can also be used to
mislead cells into suppressing the products of their own genes.
RNA interference, whose inventors won the Nobel Prize this year,
does exactly that trick: introduce double-stranded RNA into the cell
matching a legitimate gene, and bingo. The protective mechanism targets
the sequence and the corresponding protein doesn't get expressed. (The
suppression isn't absolute, but it's usually enough to effectively stall
pathways dependent on the protein.)
By inhibiting each gene in turn, it is possible to get some idea of
which ones contribute to particular cell features and activities, and
even of the order of expression of proteins along their relevant
pathways, and this has been done in, for example, the geneticist's
favourite Drosophila melanogaster. But interpreting the results
is far from straightforward and it's still a long way shy of explaining
how it all works.
For example, there are quite a lot of Drosophila genes that have
an impact on the overall shape of a cell -- making it spiky or
deformed, say -- but these effects may be consequent on very different
kinds of failure in the cell biology, and may be quite incidental to the
"real" function of the gene. The fact that disabling the gene screws up
the membrane morphology doesn't mean that the gene in question is "for"
membrane morphology2.
There is an ever-present danger that the answers we get from this kind
of investigation will be prejudiced by the questions being asked: if you
are looking for effects on cell structure, that's what you'll see. This
is not to suggest that asking such questions is invalid or unimportant
-- au contraire, mon frère -- just that we have to be
careful when drawing conclusions. There will almost always
be parts of the picture we aren't seeing3.
This is where an evolutionary perspective can help.
The "solutions" produced by natural selection are only ever answers to
the nebulous question "How can I reproduce better?" rather than, say,
"How can I fly?" or "How can I see?", even if they sometimes include
those latter features. Such solutions are intimately entwined with the
environments in which they developed, and are often quite difficult to
apprehend in the more linear terms favoured by humans. (This may be why
we design jumbo jets, while nature comes up with bumblebees.)
Evolutionary algorithms have been used to interesting effect in
software, simulation and industrial design in recent years, with some
notable successes. Such efforts can provide important insights into the
way evolution works with respect to a problem space.
The example under consideration just now uses a genetic algorithm to
develop rulesets for a cellular automaton.
The CA itself is entirely
deterministic, although the rulesets are combinatorially explosive and
not suited to encoding in the classic Wolfram "Rule 110" bit-table form.
A ruleset defines 100 rules, each of which votes for some piece of very
simple cell behaviour at the next turn based on neighbourhood state. The
majority vote wins. (This "democratic" approach is convenient for
evolution because it allows for genotype changes to accrue
incrementally.) The genetic algorithm used to evolve the rulesets uses
multi-point crossover with elitism (ie, some of the best performers in
each parental generation are preserved unchanged into the filial). I'm
not sure whether point mutations are also included, but I'd imagine so.
(Update: they are.)
The interesting feature here is the choice of selection criteria. Each
ruleset is run starting with a single seed cell in the centre of a 3d
space. Its fitness is scored according to growth after 50
generations and homeostasis (ie, maintaining the same overall
shape) at 100 and 150 generations. Homeostasis was chosen as a feature
that most living things seem to need and -- to a good approximation --
exhibit. There were also some boundary conditions on the space, to avoid
having to deal with infinitely large organisms.
This is a pretty abstract environment and might be expected not to
produce results with much relevance to biological systems, which are so
much more grubby and complicated. However, it actually turns out to be
rather illuminating.
The first lesson of the system was how good evolution is at exploiting
loopholes in the problem specification: early versions were quickly
dominated by rulesets that did exactly that, making use of the spatial
boundaries to maintain shape.
When these weaknesses were corrected, the most successful rulesets
tended to use strategies very similar to the way tissues grow in
real-world creatures, with an actively-growing layer at the bottom,
a dying-off layer at the top, and cell migration from one to the other.
This was unexpected.
Because the CA is deterministic, it's easy to experiment with successful
rulesets, making small changes to both their environment and their
genome. One such experiment involved switching off one gene at a time
and observing the results -- just as was done in Drosophila with
RNAi. It turned out that only a small number of genes were crucial to
homeostasis across a wide range of successful rulesets, all with pretty
much the same substance: if X then die. In other words, they were
genes for apoptosis.
From a biological perspective this may not seem a huge surprise: cell
death is essential to health in all multicellular organisms. What's
remarkable is that it is a consistently-evolved feature in such an
abstract mathematical space.
Another feature shared by most successful rulesets -- even though it was
not selected for explicitly -- was an ability to heal wounds: the
ruleset would generally restore its original shape reasonably quickly
after a chunk of cells was randomly killed. This was, of course, an
event that was never encountered during the rulesets' evolution.
Similar behaviour is also found in the embryonic stages of many
creatures, where it is assumed to be a specialised function evolved for
that purpose. But embryos are typically in the most protected
environment they will ever encounter -- they simply do not have to deal
with wound healing under any normal circumstance, and when they do
they're pretty much stuffed because it means their parent or egg
has been catastrophically damaged. As with the evolved CA rulesets,
embryonic wound healing is something that has likely never been
specifically tested by natural selection -- it simply doesn't provide a
reproductive advantage.
This is the real, remarkable result that links the CA experiment -- so
deliberately divorced from the material world -- with the earlier
discussion about relating Drosophila genes to cell structure.
Evolution in a complex system -- and even in a comparatively simple one
-- is not a matter of neat, linear relationships. Evolved behaviours
exist as complex networks, and some -- like wound healing -- may turn
out to be almost inevitable adjuncts to the things that are
genuinely advantageous.
1 This seems to me as good an argument as any against the infuriatingly obtuse claptrap of intelligent design. Cell mechanics are simply not amenable to a de novo design process. Life is so profoundly contingent, and the solutions it comes up with so Heath Robinson [US: Rube Goldberg], that it can only plausibly have reached its present state by trial and error. Any procedure by which a Flying Spaghetti Monster might have come up with the likes of us must be so suck it and see as to make the deity itself entirely superfluous.
2 Observe how basic vocabulary becomes fraught with risk in this sort of discussion; hence the quotes. Careless talk can easily lead to unproductive diversions in teleology. The reasons why things are as they are do not amount to a purpose.
3 Vaguely interesting bicycle analogy omitted. I may restore this in the submitted case paper if it seems pertinent when I get around to writing that.
1 This seems to me as good an argument as any against the infuriatingly obtuse claptrap of intelligent design. Cell mechanics are simply not amenable to a de novo design process. Life is so profoundly contingent, and the solutions it comes up with so Heath Robinson [US: Rube Goldberg], that it can only plausibly have reached its present state by trial and error. Any procedure by which a Flying Spaghetti Monster might have come up with the likes of us must be so suck it and see as to make the deity itself entirely superfluous.
2 Observe how basic vocabulary becomes fraught with risk in this sort of discussion; hence the quotes. Careless talk can easily lead to unproductive diversions in teleology. The reasons why things are as they are do not amount to a purpose.
3 Vaguely interesting bicycle analogy omitted. I may restore this in the submitted case paper if it seems pertinent when I get around to writing that.