Picture an old record shop. How do you tell a classic from a dud? With no way of telling the value of each record, you could just as easily end up with B*Witched as with the Beatles.
Now imagine all of the records in that shop laid out in front of you in the form of a landscape. Each record forms part of the floor, with different releases of the same album clustered tightly around each other, different albums from the same artist a little further away, artists of the same genre further away still… etc. Starting from A Hard Day’s Night, you move through Rubber Soul, Help! and Revolver, before finally ending up at Magical Mystery Tour. But the landscape formed by these records is not flat; some records are higher than others, making an undulating series of peaks, troughs and plains. And the higher you go, the higher the value of whatever record you are standing on. Looking back behind you, you see a signed copy of Sgt. Pepper’s Lonely Hearts Club Band at the peak of the Beatles’ hill.
Now imagine we could do the same for genetic sequences. Starting with a simple case, we will consider the four variants (A, C, G and T) at one position in a sequence. Given that the rest of the sequence is identical, and the sequences are therefore very similar, this is equivalent to considering just one artist with four different albums. On the x axis we plot the variant, and on the y we show fitness (how well the variant performs):
Imagine that we are placed on the left end of the line – this is the part of space occupied by A. One step to the right and we tread on C, another step and we’re on G, and a final step brings us to T on the right. And as we move from one base to another, we go up and down as the fitness of each sequence increases and decreases. In this example, G has the highest fitness (it’s the Sgt. Pepper of bases), C and T have intermediate fitness, and A has the lowest fitness.
However, this example only encompasses the fitness of the variants at one base. If we consider two bases, the landscape changes from a simple 2D line to a more complex 3D surface. And if we consider more bases (or all the records in the shop), we step into a world with dozens or hundreds of dimensions: the landscape pictured (taken from Hayashi et al, 2006) summarises this high dimensionality into three large peaks, with smaller peaks and troughs at the top of each one.
Starting at one position we can journey across the landscape, treading the paths along which the sequence can evolve, with every step representing one change in the sequence. On this journey, we have to remember only one rule: we can never go downhill. If we go up, it means the sequence is increasing in fitness, and so will survive; however, if we go down it means the sequence is becoming less fit, and will be selected against and removed from the population.
However, this rule soon brings up a problem. Imagine we walk up a small hill: from here, every direction leads downhill. If we can never go down, where do we go? Are we stuck forever on this small hill, at a relatively low fitness, unable to continue evolving?
Luckily, there is a solution: neutral variants. These variants have different sequences but are as fit as each other, and therefore form ridges in the fitness landscape. It is along these ridges that we can escape from low hills, into regions of the landscape that have higher peaks. As the sequence gets longer, the chances of encountering a ridge increase, because each base adds another dimension in which fitness can vary. As Manfred Eigen puts it in his book Steps Towards Life:
‘raising the number of dimensions increases the number of possible routes’
Using the fitness landscape, we can visualise paths from one sequence to another, and judge whether these paths will be evolutionarily viable. By distilling the complexity of genotype and phenotype into one space, the fitness landscape gives an intuitive illustration of the process of molecular evolution.
Hayashi Y., Aita T., Toyota H., Husimi Y., Urabe I., et al (2006) Experimental Rugged Fitness Landscape in Protein Sequence Space. PLoS ONE 1(1): e96
Eigen, M. (1996) Steps Towards Life. Oxford University Press