Hot Spot Paradox
When diploids organisms product gametes, a cell reproducts and divides
itself into 4 haploids gametes.
During this division the aparried chromosomes can recombine their
genes: it happens that the structure of one of the chromosomes breaks
in some specific points called ``hotspots''. The structure is then
repaired by copying the corresponding allele from the aparried
chromosome, which results eventually in the exchange of whole sections
of the chromosomes, called ``crossover''.
The analysis of this phenomenon reveals a paradox of evolution: the
hotspots can be inactivated by mutation, which change them so that
they don't break anymore, and don't initiate a crossover any more.
If a recombination is still initiated by the aparried chromosome, its
hotspot is repaired using the mutated allele: the inactivated hotspots
can propagate to the entire population. Such a mechanism should lead
to the disappearance of the active hotspots, but in nature they didn't
and each diploid species have several of them on each chromosome: this
is the paradox.
The recombination of the chromosomes helps the migration of those
during the cell division, which permits an equilibrated distribution
of chromosomes in the gametes. Without recombination, two aparried
chromosomes could end in the same gamete, sterilizing it and the
gamete missing this chromosome. Boulton, Myers et Redfield tried to
solve the hotspot paradox by taking into account this negative effect,
but it doesn't seem to be strong enough to explain the proportion of
active hotspots measured in practice [Boulton Myers Redfield].
The other contribution of hotspots is to the evolution of the
species. This factor is only feebly taken into account in the
simulations of Boulton et al., where the population is initially
optimal. In collaboration with Mario Pineda, a post-doctoral student
from the department of Zoology, I defined a simple model neglecting
the effect of hotspots on the chromosome separation, but taking into
account the effect on the evolution of the species. I try to analyze
this model with techniques similar to the ones used for genetic
algorithms, on which I have been working for my master's thesis, and I
implemented this model for simulations.
(This is a sequel to Mario's talk in october)