Simulation of mitochondrial DNAs (mtDNAs) *

Mitochondrial DNAs resides in human mitochondrion. A zygote inherits its organelles from the cytoplasm of the egg, and thus organelle inheritance is generally maternal. Whereas there is only one copy of a nuclear chromosome per gamete, there are man copies of an organellar chromosome, forming a population of identical organelle chromosomes that is transmitted to the offspring through the egg. Because these organellar chromosomes are identical, they are modelled in simuPOP as a single chromosome with type MITOCHONDRIAL. In order to simulate mitochondrial DNAs, it is important to remember:

• MendelianGenoTransmitter and Recombinator do not handle mitochondrial DNAs so you will have to explicitly use MitochondrialGenoTransmitter to transmit the mitochondrial DNAs from mother to offspring. Note that CloneGenoTransmitter is a special transmitter that will copy everything including sex, information fields to offspring.
• The Stat operator recognizes this chromosome type and will report allele, haplotype, and genotype counts, and other statistics correctly, although some diploid-specific statistics are not applicable.
• Natural selections on mtDNAs is usually performed using operator MapSelector where single alleles are assigned a fitness value. Operator MaSelector assumes two alleles and is not applicable.

Example mitochondrial demonstrates the use of a Recombinator to recombine an autosome and two sex chromosomes, and a MitochondrialGenoTransmitter to transmit mitochondrial chromosomes. Natural selection is applied to allele 3 at the 3rd locus on the mitochondrial DNA, whose frequency in the population decreases as a result.

Example: Transmission of mitochondrial chromosomes

>>> import simuPOP as sim
>>> pop = sim.Population(1000, loci=[5]*4,
...     # one autosome, two sex chromosomes, and one mitochondrial chromosomes
...     chromTypes=[sim.AUTOSOME, sim.CHROMOSOME_X, sim.CHROMOSOME_Y, sim.MITOCHONDRIAL],
...     infoFields=['fitness'])
>>> pop.evolve(
...     initOps=[
...         sim.InitSex(),
...         sim.InitGenotype(freq=[0.25]*4)
...     ],
...     preOps=[
...         sim.MapSelector(loci=17, fitness={(0,): 1, (1,): 1, (2,): 1, (3,): 0.4})
...     ],
...     matingScheme=sim.RandomMating(ops= [
...         sim.Recombinator(rates=0.1),
...         sim.MitochondrialGenoTransmitter(),
...     ]),
...     postOps=[
...         sim.Stat(alleleFreq=17, step=10),
...         sim.PyEval(r'"%.2f %.2f %.2f %.2f\n" % (alleleNum[17][0],'
...             'alleleNum[17][1], alleleNum[17][2], alleleNum[17][3])', step=10),
...     ],
...     gen = 100
... )
1288.00 273.00 325.00 114.00
1384.00 245.00 371.00 0.00
1492.00 138.00 370.00 0.00
1461.00 69.00 470.00 0.00
1449.00 65.00 486.00 0.00
1536.00 17.00 447.00 0.00
1624.00 7.00 369.00 0.00
1538.00 0.00 462.00 0.00
1619.00 0.00 381.00 0.00
1623.00 0.00 377.00 0.00
100

now exiting runScriptInteractively...

Download mitochondrial.py

You might wonder how a mutation can change the allele of all organelles in the mitochondrion. This is generally believed to be done through natural drift during cytoplasmic segreagation, which is not a mitotic process because it takes place in dividing asexual cells. Because only one mitochondrial chromosome is allowed in simuPOP, you will have to use customized chromosome types if you would like to simulate this process. Fortunately, operator MitochondrialGenoTransmitter can select random organelles from multiple customized chromosomes, if no chromosome of type MITOCHONDRIAL is present.

Example mtDNA_evolve demonstrates the fixation of mutant in cells with multiple organelles. Althogh mutations are introduced to only one of the organelles, after a number of cell divisions, the majority of the cells now have only one type of allele. This example uses a RandomSelection mating scheme to select cells randomly from the parental population. Because no sexual reproduction is involved, MitochondrialGenoTransmitter passes the parental genotype to offspring regardless of sex of parent. This example also demonstrates a disadvantage of using customized chromosomes in that you will have to calculate statistics by yourself because only you know the meaning of these chromosomes. In this example, a function is written to count the number of mutants in each cell (individual), and summarize the number of cells with 0, 1, 2, 3, 4, and 5 copies of the mutant.

Example: Evolution of multiple organelles in mitochondrion

>>> import simuPOP as sim
>>>
>>> def alleleCount(pop):
...     summary = [0]* 6
...     for ind in pop.individuals():
...         geno = ind.genotype(ploidy=0)
...         summary[geno[0] + geno[2] + geno[4] + geno[6] + geno[8]] += 1
...     print('%d %s' % (pop.dvars().gen, summary))
...     return True
...
>>> pop = sim.Population(1000, loci=[2]*5, chromTypes=[sim.CUSTOMIZED]*5)
>>> pop.evolve(
...     # every one has miDNAs 10, 00, 00, 00, 00
...     initOps=[
...         sim.InitGenotype(haplotypes=[[1]+[0]*9]),
...     ],
...     # random select cells for cytoplasmic segregation
...     matingScheme=sim.RandomSelection(ops= [
...         sim.MitochondrialGenoTransmitter(),
...     ]),
...     postOps=sim.PyOperator(func=alleleCount, step=10),
...     gen = 51
... )
0 [333, 408, 219, 38, 2, 0]
10 [806, 16, 14, 16, 11, 137]
20 [816, 1, 1, 3, 0, 179]
30 [833, 0, 0, 0, 0, 167]
40 [805, 0, 0, 0, 0, 195]
50 [849, 0, 0, 0, 0, 151]
51

now exiting runScriptInteractively...

Download mtDNA_evolve.py