Genetics: Environmental adapation

Monday 24th December, 2007 at 11:04 am 2 comments

Genetics is a hard subject.   Not so much due to the mechanics, which given time and passion, we can understand, adjust, tweak or even create.  Hard because of a human fallacy – Time.

Einstein was right – it’s all relative.  Our lifetimes are growing – reaching a 100 years isn’t science fiction – it’s becoming science fact.  Yet from an evolutionary perspective it’s a blink of an eye – nothing.  But then, from a genetic standpoint we’re using the wrong frame of reference.  What matters more are generations – like the rings in a tree – each depending on those that come before it.  The wind, rain, storms all have an effect on how the tree will survive and grow in the coming years – the quality of the air, the ground effect how and when the tree will bloom, flower or produce offspring.

Any offspring it produces, a gift from nature, will be influenced by those conditions present when it’s parent flourished and grew.  And so on, and so on.

A tree can survive for 500 years, producing a new generation every year or two.  Yet the metabolism of a tree is slow and steady.  In the lifetime of a tree it can produce 200 or so generations of itself.  Over time it will adapt to change and the final offspring it produces “may” have a few genetics tweaks that allow the new generation to survive a little better then its parent.   Humans on the other hand are much less prolific.  Each human generation produces 1 or 2 generations; where a generation is a unique coupling of 2 individuals.  Our metabolisms are very different from those discussed previously – burning brightly in the evolutionary night with sparks of change.

Recent studies indicate that humans are more sensitive to their environment than previously suspected.

These changes aren’t gross adaptations – where a new gene is introduced or the function of a gene changes dramatically – something geneticists generally use to measure genetic evolution.  These are subtle alterations to how genes are expressed.

It’s best explained with an analogy.

Think of a 3-door Fiat 500.  This is no ordinary Fiat – it’s design allows it to adapt and change to the environment.  Now over time, by producing offspring with other cars, it may be able to develop a sunroof or rear spoilers to travel more efficiently (or perhaps to better attract a mini cooper).  These changes would be significant gene changes and happen over hundreds or thousands of generations.  This Fiat can, however, respond to more subtle environmental pressures –  so a scarcity of fuel (food) help alter the expression of certain genes – allowing it to be more efficient in the use of fuel – or to develop a larger fuel tank.  Developing a fuel tank that’s larger means it can’t travel as far in the same period of time (it’s heavier) – so there’s a cost/benefit to this change.  Unfortunately – which this Fiat can adapt to the environmental pressures that it faces – it can’t predict what the outcome of these changes will be. 

Which is the rub – genetic evolution allows our genes to adapt to the environment but the outcome of these changes can’t be understood in the current context.  It takes time.  Time is needed to see if these changes are positive or negative.  But these, again, are black and white terms – which don’t make sense in the broader context.  A change that is negative for an individual (such a being diabetic, overweight or short-sighted) – might be beneficial in the greater context.  A genetic change (or predisposition) that has a negative effect on an individual might also bring a change that introduces a positive effect.

The problem is we just don’t know.  Which is where a human failing comes into effect – we tend to think in very short time-spans, the “here and now”, without considering more the broader implications.  Our frame of reference is also very narrow – a fraction of the overall picture.  We’ve had to – considering too many elements can cloud the issues and impede progress and discovery. 

But Evolutionary and Environmental Genetics doesn’t have that excuse and shouldn’t exclude the larger implications of genetics over time.  They need to consider what the impact is over hundreds and thousands of generations.  Which is no mean feat.  It’s difficult because while we can examine the DNA of somebody – we can’t as yet talk about how their DNA was expressed.  How their DNA was expressed would affect their physical make-up.  Were they short-sighted, chubby, fast, slow, quick-witted and so on.  Not to mention the problem of social evolution – and the effect of our genes on how humans have evolved socially.  Social evolution itself is a 2 part problem – there are genes that can affect our ability to interact (references: here and here) ; in addition the overall effect of our genes (that make us an individual) affect the way we interact.

Take for instance the study about the lasting genetic legacy from the BBC website.  While it’s interesting that we are finally seeing short term adaptation to environmental, physical and social pressures – we need also to consider the larger implications of such a change to the overall pool of humanity.  Humanity in the fuller context of the physiological, social and environmental.

The human genome project is an important milestone in achieving this understanding – but it is only part of the journey. 


Entry filed under: evolution, genetics. Tags: , , , .

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2 Comments Add your own

  • 1. Dmitri  |  Tuesday 1st January, 2008 at 12:11 am

    There was a time when there was a lot of hype regarding genetic algorithms in software. There was a whole subclass of problems (mainly chemical ones) that genetic algorithms in software could solve and get patentable solutions. Unfortunately, after this, things kind of have died down because in my industry it’s not practical to have ‘perfect induction’ solutions to problems. Sad, really…

  • 2. Dasher  |  Tuesday 1st January, 2008 at 1:33 pm

    Genetic algorithms in software development can solve some really interesting problems – especially in the current “Social Network” environment. The problem before was that it was over-hyped and used in the wrong problem space.

    I think in the next 18 months you’ll see more use of genetic approaches to solve ranking, reputation and connected-network problems.

    I’d love to be involved in a project that wanted to use them in this environment.


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