Principle of Systems Biology illustrated using the Virtual Heart
and there’s other material on his site.
So I was interested to see Ann Gauger making a very similar set of points in this piece: Life, Purpose, Mind: Where the Machine Metaphor Fails.
In a recent essay Steve Talbott highlights the inadequacies of our current way of thinking and speaking about biology. He points out that organisms are more than the sum of their mechanisms. In fact, he rejects the machine metaphor as completely inadequate to describe living things. Living beings are adaptable and responsive to their environments, changing their behavior based on external cues and their own requirements. They are transformative, existing as entities that are much more than the molecules that compose them. They are not what they eat — they make what they eat into themselves. Living beings are integrated wholes that come from other living things. And they are more than their DNA. DNA requires a functional cellular environment to be properly read and interpreted, just as a cell requires DNA to be able to sustain itself. In order to understand the whole picture you have to look at the cell from many points of view, not just a gene-centric one.
This is really important, in practice, when we find that although many human disorders are highly heritable (often well over 50%), it is rare for any one allele to have more than a tiny effect – the odds ratios risk SNPs found in GWAS studies tend to be extremely small – the vast majority of people carrying the “risk” SNP can are perfectly healthy.
But far from this making Darwinian evolution less likely, as Gauger thinks it might:
Maybe biological systems do reflect intelligent agency, because intelligent agents are the only known source capable of designing, assembling, and then coordinating so many interrelated sub-systems into a functional whole. And maybe, by acknowledging this, we can come to understand biology better.
it seems to me to make it far more intuitive. After all, as Petrushka keeps pointing out, human intelligent agents are not very good at “designing, assembling, and then coordinating so many interrelated sub-systems into a functional whole” – what we tend to do is to go through a series of prototypes and upgrades, where we add things here and take off things there, until we end up with a Prius instead of a Horseless Carriage – something much more akin to evolutionary processes, where we discard the dud designs, and retain what works – breed from the winners, in effect.
And it also lets us view a genotype as a distribution of heritable chunks, where the phenotype reflects the summed effects, and where variation is pretty smooth.