the_genome Module Reference

Definition the genetic architecture of the agent. More...

Data Types
type	gene
	This describes an individual gene object. See the genome structure for as general description and gene for details. More...
type	chromosome
	This type describes the chromosome object. Chromosome consists of an array of alleles and a descriptive string label. See "the genome structure" for as general description and "chromosome" for details. More...
type	individual_genome
	This type describes parameters of the individual agent's genome The genome is an array of allocatable the_genome::chromosome objects, different kinds of agents may have different genomes with different number of chromosomes. See "the genome structure" for as general description and "genome" for details. More...

Functions/Subroutines
elemental subroutine	chromosome_sort_rank_id (this)
	Sort GENE objects within the CHROMOSOME by their rank_id. The two subroutines qsort and qs_partition_rank_id are a variant of the recursive quick-sort algorithm adapted for sorting integer components of the the CHROMOSOME object. More...
subroutine	allele_init_random (this)
	Initialises allele with a random integer. Note that we do not set the labels for the alleles here during the random initialisation. More...
elemental subroutine	allele_create_zero (this)
	Create allele with zero value. We don't set labels for alleles here. More...
subroutine	allele_label_init_random (this)
	The (pair of) alleles here are assigned random string labels Not sure if that is necessary for any application though. More...
elemental subroutine	allele_label_set (this, label)
	Set labels for the alleles. The subroutine parameter is array of labels. More...
elemental character(len=label_length) function	allele_label_get (this)
	Get the i-th allele label. More...
elemental subroutine	allele_value_set (this, set_value, nr)
	Set a single value of the allele additive component. More...
pure subroutine	alleles_value_vector_set (this, values)
	Set values of the alleles as a vector, i.e. sets the whole gene values. More...
elemental integer function	allele_value_get (this, nr)
	Get the value of the allele. More...
pure subroutine	allele_values_vector_get (this, values)
	Get the vector of all values of the alleles, i.e. gets the gene values. More...
elemental subroutine	allele_rank_id_set (this, value_id)
subroutine	allele_mutate_random (this, prob)
	Introduce a random point mutation to a random allele component. More...
subroutine	allele_mutate_random_batch (this, prob)
	Introduce a random mutation of the whole set of additive allele components. More...
subroutine	chromosome_init_allocate_random (this, length, label)
	This subroutine initialises the chromosome with, and allocates, random alleles, sets one of them randomly dominant and optionally defines the chromosome label. More...
subroutine	chromosome_create_allocate_zero (this, length, label)
	Init a new chromosome, zero, non-random. More...
elemental subroutine	chromosome_recalculate_rank_ids (this)
	This subroutine recalculates rank_id indices for consecutive gene objects within the chromosome. This may be necessary after reordering by random relocation mutation. More...
subroutine	chromosome_mutate_relocate_swap_random (this, prob)
	Mutate within the same chromosome, relocate a gene (unit of alleles) to a different random position within the same chromosome, the misplaced gene moves to the relocated gene position, so they are just swap. More...
subroutine	chromosome_mutate_relocate_shift_random (this, prob)
	Mutate within the same chromosome, relocate a gene (unit of alleles) to a different random position within the same chromosome, shifting all other genes within the chromosome down one position. This works as follows: first, we randomly determine the gene to relocate, assign it a new random rank_id. Then re-sort the chromosome according to the new ranks with qsort with the qs_partition_rank_id backend. More...
subroutine	genome_init_random (this, label)
	Initialise the genome at random, and set sex as determined by the sex determination locus. More...
subroutine	genome_create_zero (this)
	Create a new empty genome, and set sex as determined by the sex determination locus. Genome values are from parents using inherit functions. More...
subroutine	genome_label_set (this, label)
	Label genome. If label is not provided, make a random string. More...
elemental character(len=label_length) function	genome_label_get (this)
	Accessor function to get the genome label. The label is a kind of a (random) text string name of the genome and the individual agent. More...
subroutine	genome_sex_determine_init (this)
	Sex determination initialisation subroutine. More...
elemental logical function	genome_get_sex_is_male (this)
	Get the logical sex ID of the genome object component. More...
elemental logical function	genome_get_sex_is_female (this)
	Get the logical sex ID of the genome object component. More...
elemental character(len=label_length) function	genome_get_sex_label (this)
	Get the descriptive sex label: male or female. More...
elemental subroutine	genome_individual_set_alive (this)
	Set the individual to be alive, normally this function is used after init or birth. More...
elemental subroutine	genome_individual_set_dead (this)
	Set the individual to be dead. Note that this function does not deallocate the individual agent object, this may be a separate destructor function. More...
elemental logical function	genome_individual_check_alive (this)
	Check if the individual is alive. More...
elemental logical function	genome_individual_check_dead (this)
	Check if the individual is dead (the opposite of is_alive). More...
subroutine	genome_individual_recombine_homol_full_rand_alleles (this, mother, father, exchange_ratio)
	Internal genetic recombination backend, exchange individual alleles between homologous chromosomes in mother and father genomes to form the this (offspring) genome. Fully random recombination. More...
subroutine	genome_individual_recombine_homol_part_rand_alleles (this, mother, father, exchange_ratio)
	Internal genetic recombination backend, exchange individual alleles between homologous chromosomes in mother and father genomes to form the this (offspring) genome. Partially random recombination, identical across the homologous chromosomes. More...
subroutine	genome_individual_crossover_homol_fix (this, mother, father, pattern_matrix)
	Internal fixed genetic crossover backend, exchange blocks of alleles between homologous chromosomes in mother and father genomes to form the this (offspring) genome. More...
subroutine	genome_mutate_wrapper (this, p_point, p_set, p_swap, p_shift)
	Perform a probabilistic random mutation(s) on the individual genome. This is a high level wrapper to build mutations from various components. More...
Neuronal response functions
There are two separate functions that produce a trait value from the genotype. The procedure trait_init_genotype_gamma2gene does modify the agent (this, intent[inout]) as it sets the label. On the other hand, a similar procedure the_genome::trait_set_genotype_gamma2gene() does not affect the agent, it has the intent [in].
subroutine	trait_init_genotype_gamma2gene (this, this_trait, g_p_matrix, init_val, gerror_cv, label)
	Initialise an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy. More...
subroutine	trait_set_genotype_gamma2gene (this, this_trait, g_p_matrix, init_val, gerror_cv)
	Set an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy. More...
The genotype to phenotype functions based on fixed linear
transformation.
subroutine	trait_init_linear_sum_additive_comps_2genes_r (this, this_trait, g_p_matrix, phenotype_min, phenotype_max, label)
	Initialise an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy. More...
subroutine	trait_set_linear_sum_additive_comps_2genes_r (this, this_trait, g_p_matrix, phenotype_min, phenotype_max)
	Set an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy. More...

Variables
character(len= *), parameter, private	modname = "(THE_GENOME)"

Detailed Description

Definition the genetic architecture of the agent.

THE_BODY module

This module defines the genetic architecture objects of the agent. See The genome structure for an overview.

Function/Subroutine Documentation

chromosome_sort_rank_id()

elemental subroutine the_genome::chromosome_sort_rank_id

(

class(chromosome), intent(inout)

this

)

Sort GENE objects within the CHROMOSOME by their rank_id. The two subroutines qsort and qs_partition_rank_id are a variant of the recursive quick-sort algorithm adapted for sorting integer components of the the CHROMOSOME object.

Definition at line 323 of file m_genome.f90.

Here is the call graph for this function:

allele_init_random()

subroutine the_genome::allele_init_random

(

class(gene), intent(inout)

this

)

Initialises allele with a random integer. Note that we do not set the labels for the alleles here during the random initialisation.

The allele components are initialised by a random integers within the range ALLELERANGE_MIN and ALLELERANGE_MAX parameetr values.

Definition at line 410 of file m_genome.f90.

allele_create_zero()

elemental subroutine the_genome::allele_create_zero

(

class(gene), intent(inout)

this

)

Create allele with zero value. We don't set labels for alleles here.

Note

Note that there is no need to allocate allele_value array as it has fixed shape.

Definition at line 424 of file m_genome.f90.

allele_label_init_random()

subroutine the_genome::allele_label_init_random

(

class(gene), intent(inout)

this

)

The (pair of) alleles here are assigned random string labels Not sure if that is necessary for any application though.

Definition at line 436 of file m_genome.f90.

allele_label_set()

elemental subroutine the_genome::allele_label_set	(	class(gene), intent(inout)	this,
		character(len=*), intent(in)	label
	)

Set labels for the alleles. The subroutine parameter is array of labels.

Parameters

[in]

label

label, provides an array of labels to set for the allele.

Definition at line 445 of file m_genome.f90.

allele_label_get()

elemental character(len=label_length) function the_genome::allele_label_get

(

class(gene), intent(in)

this

)

Get the i-th allele label.

Returns

Returns the label of the allele.

Definition at line 456 of file m_genome.f90.

allele_value_set()

elemental subroutine the_genome::allele_value_set	(	class(gene), intent(inout)	this,
		integer, intent(in)	set_value,
		integer, intent(in)	nr
	)

Set a single value of the allele additive component.

Parameters

[in]	set_value	value, provides the value to set for the allele and the allele number.
[in]	nr	number, provides the number of the allele component to set.

Definition at line 467 of file m_genome.f90.

alleles_value_vector_set()

pure subroutine the_genome::alleles_value_vector_set	(	class(gene), intent(inout)	this,
		integer, dimension(additive_comps), intent(in)	values
	)

Set values of the alleles as a vector, i.e. sets the whole gene values.

Parameters

[in]

values

values, provides vector of values to set for the alleles.

Definition at line 482 of file m_genome.f90.

allele_value_get()

elemental integer function the_genome::allele_value_get	(	class(gene), intent(in)	this,
		integer, intent(in)	nr
	)

Get the value of the allele.

Parameters

[in]

nr

number, provides the number of the allele component to set.

Returns

Returns the value of the nr's allele.

Definition at line 493 of file m_genome.f90.

allele_values_vector_get()

pure subroutine the_genome::allele_values_vector_get	(	class(gene), intent(in)	this,
		integer, dimension(additive_comps), intent(out)	values
	)

Get the vector of all values of the alleles, i.e. gets the gene values.

Parameters

[out]

values

values, Gets the vector of the values for the alleles.

Definition at line 508 of file m_genome.f90.

allele_rank_id_set()

elemental subroutine the_genome::allele_rank_id_set	(	class(gene), intent(inout)	this,
		integer, intent(in)	value_id
	)

Parameters

[in]

value_id

rank_id, set this value to the allele rank_id.

Definition at line 518 of file m_genome.f90.

allele_mutate_random()

subroutine the_genome::allele_mutate_random	(	class(gene), intent(inout)	this,
		real(srp), intent(in), optional	prob
	)

Introduce a random point mutation to a random allele component.

Parameters

[in]

prob

prob optional probability of mutation, if absent, the default value commondata::mutationrate_point is used.

Implementation details

Do mutate if a random value is smaller than the commondata::mutationrate parameter constant.

First, determine which of the alleles components gets mutation.

Second, change the value of this allele component to an random integer.

Definition at line 530 of file m_genome.f90.

allele_mutate_random_batch()

subroutine the_genome::allele_mutate_random_batch	(	class(gene), intent(inout)	this,
		real(srp), intent(in), optional	prob
	)

Introduce a random mutation of the whole set of additive allele components.

Parameters

[in]

prob

prob optional probability of mutation, if absent, the default value commondata::mutationrate_batch is used.

Implementation details

This mutation just re-init the whole allele set as random.

Definition at line 562 of file m_genome.f90.

chromosome_init_allocate_random()

subroutine the_genome::chromosome_init_allocate_random	(	class(chromosome), intent(inout)	this,
		integer, intent(in)	length,
		character(len=*), intent(in), optional	label
	)

This subroutine initialises the chromosome with, and allocates, random alleles, sets one of them randomly dominant and optionally defines the chromosome label.

Parameters

[in]	length,sets	the length of the chromosome object, N of alleles.
[in]	label,sets	the label for the chromosome object, optional.

Implementation details

We set the chromosome label if such a parameter is provided, or a random string if not.

First, set the chromosome length using the procedure parameter length.

Then, allocate the array of the allele objects with this length.

Initialise all the alleles within this chromosome.

Specifically, initialise the allele.

Set initial rank_id ID of the allele.

Finally, set the label for the alleles within this chromosome. Labels can be set random using this function (disabled):

call this%allele(i)%label_random()

But in this implementation we construct the label for the allele from the chromosome label and the allele number;

Long chromosome labels are trimmed at right to fit the allele number.

Definition at line 596 of file m_genome.f90.

chromosome_create_allocate_zero()

subroutine the_genome::chromosome_create_allocate_zero	(	class(chromosome), intent(inout)	this,
		integer, intent(in)	length,
		character(len=*), intent(in), optional	label
	)

Init a new chromosome, zero, non-random.

Implementation details

Set the chromosome label if provided as parameter, or random string if not.

First, set the chromosome length using the procedure parameter length.

Then, we allocate the array of the allele objects with this length.

Initialise all the alleles within this chromosome.

Note

Parallel do concurrent construct is used here.

Definition at line 647 of file m_genome.f90.

chromosome_recalculate_rank_ids()

elemental subroutine the_genome::chromosome_recalculate_rank_ids

(

class(chromosome), intent(inout)

this

)

This subroutine recalculates rank_id indices for consecutive gene objects within the chromosome. This may be necessary after reordering by random relocation mutation.

Definition at line 697 of file m_genome.f90.

chromosome_mutate_relocate_swap_random()

subroutine the_genome::chromosome_mutate_relocate_swap_random	(	class(chromosome), intent(inout)	this,
		real(srp), intent(in), optional	prob
	)

Mutate within the same chromosome, relocate a gene (unit of alleles) to a different random position within the same chromosome, the misplaced gene moves to the relocated gene position, so they are just swap.

Parameters

[in]

prob

prob optional probability of mutation, if absent, the default value commondata::relocation_swap_rate is used.

Implementation details

Do mutate if a random value is smaller than the commondata::relocation_swap_rate parameter constant value.

If yes, randomly determine the gene (gene_move) that initiates the mutation move within the chromosome.

Randomly determine the gene that will be swapped with the gene_move.

Then, cycle through the alleles and select new random allele if it happens to coincide with gene_move.

After this, do the gene swap, and gene rank_id ID swap.

Definition at line 714 of file m_genome.f90.

chromosome_mutate_relocate_shift_random()

subroutine the_genome::chromosome_mutate_relocate_shift_random	(	class(chromosome), intent(inout)	this,
		real(srp), intent(in), optional	prob
	)

Mutate within the same chromosome, relocate a gene (unit of alleles) to a different random position within the same chromosome, shifting all other genes within the chromosome down one position. This works as follows: first, we randomly determine the gene to relocate, assign it a new random rank_id. Then re-sort the chromosome according to the new ranks with qsort with the qs_partition_rank_id backend.

Parameters

[in]

prob

prob optional probability of mutation, if absent, the default value commondata::relocation_shift_rate is used.

Implementation details

Do mutate if a random value is smaller than the commondata::relocation_shift_rate parameter constant value.

If yes, randomly determine the gene that initiates move within the chromosome.

Randomly determine the new position of this gene.

Then, cycle through alleles and select new random allele if it happens to coincide with gene_move.

After the cycle, adjust rank_id's of the alleles.

Then re-sort the allele objects by their updated rank_id's.

Finally, recalculate rank_id's so they are again ordered.

Definition at line 772 of file m_genome.f90.

genome_init_random()

subroutine the_genome::genome_init_random	(	class(individual_genome), intent(inout)	this,
		character(len=*), intent(in), optional	label
	)

Initialise the genome at random, and set sex as determined by the sex determination locus.

Implementation details

First, create spatial moving object component of the individual genome. But we do not yet position the genome object.

Allocate the genome object, it must have genome_size chromosomes and CHROMOSOME_PLOIDY homologs.

Now cycle over all the chromosomes and homologs and initialise each of them.

On exit from the cycle, set the genome label if provided, or random string if not.

Then, determine the sex of the genome by the genome sex determination locus taking into account the sex ratio.

Definition at line 827 of file m_genome.f90.

genome_create_zero()

subroutine the_genome::genome_create_zero

(

class(individual_genome), intent(inout)

this

)

Create a new empty genome, and set sex as determined by the sex determination locus. Genome values are from parents using inherit functions.

Implementation details

First of all, Create spatial moving object component of the individual genome.

Allocate the genome object, it must have genome_size chromosomes and CHROMOSOME_PLOIDY homologs

Now cycle over all the chromosomes and homologs and initialise each of them with empty genes (zero).

Initialise the label the genome with a random string.

Determine the sex of the genome by the genome sex determination locus taking into account the sex ratio.

Definition at line 874 of file m_genome.f90.

genome_label_set()

subroutine the_genome::genome_label_set	(	class(individual_genome), intent(inout)	this,
		character(len=*), intent(in), optional	label
	)

Label genome. If label is not provided, make a random string.

Implementation details

Set the genome label if provided, or random string if not.

Definition at line 912 of file m_genome.f90.

genome_label_get()

elemental character(len=label_length) function the_genome::genome_label_get

(

class(individual_genome), intent(in)

this

)

Accessor function to get the genome label. The label is a kind of a (random) text string name of the genome and the individual agent.

Note

We especially need this accessor function because the genome (and individual) name is used in other modules for file names ids etc.

Returns

String label.

Definition at line 935 of file m_genome.f90.

genome_sex_determine_init()

subroutine the_genome::genome_sex_determine_init

(

class(individual_genome), intent(inout)

this

)

Sex determination initialisation subroutine.

Determine the genome's sex, sex is set by a logical identifier, sex_is_male TRUE is male. Sex is calculated from the genome and based on the average values of the sex determination alleles in homologous chromosomes, rescaled to 0:1. This rescaled value is then compared with the sex ratio parameter.

Implementation details

The implementation is based on genotype x phenotype matrix (logical type): commondata::sex_genotype_phenotype.

First, initialise the average sex locus sum across the homologous chromosomes.

Loops

Then loop across homologs, chromosomes and alleles until the value of SEX_GENOTYPE_PHENOTYPE gets TRUE. This means it is the sex locus.

• If this condition is met, set label to the sex locus allele ("SEX_LOCUS").
• Sex is determined by an average of the sex loci of the homologous chromosomes. Therefore, first get the vector of additive allele components.
• And sum it up to finally get the total sum for all chromosomes.
• Finally, also update the counter of the totals.

Upon exit from the loop, check if the average sex locus across all homologous chromosomes and additive allele components, scaled to 0:1 is less than the SEX_RATIO, the subject becomes the male genotype.

Otherwise, the subject becomes the female.

Definition at line 955 of file m_genome.f90.

genome_get_sex_is_male()

elemental logical function the_genome::genome_get_sex_is_male

(

class(individual_genome), intent(in)

this

)

Get the logical sex ID of the genome object component.

Returns

Returns logical value for sex: is it male?

Definition at line 1016 of file m_genome.f90.

genome_get_sex_is_female()

elemental logical function the_genome::genome_get_sex_is_female

(

class(individual_genome), intent(in)

this

)

Get the logical sex ID of the genome object component.

Returns

Returns logical value for sex: is it a female?

Definition at line 1028 of file m_genome.f90.

genome_get_sex_label()

elemental character(len=label_length) function the_genome::genome_get_sex_label

(

class(individual_genome), intent(in)

this

)

Get the descriptive sex label: male or female.

Returns

Returns the the descriptive sex label

Definition at line 1044 of file m_genome.f90.

genome_individual_set_alive()

elemental subroutine the_genome::genome_individual_set_alive

(

class(individual_genome), intent(inout)

this

)

Set the individual to be alive, normally this function is used after init or birth.

Definition at line 1057 of file m_genome.f90.

genome_individual_set_dead()

elemental subroutine the_genome::genome_individual_set_dead

(

class(individual_genome), intent(inout)

this

)

Set the individual to be dead. Note that this function does not deallocate the individual agent object, this may be a separate destructor function.

The dies method is implemented at the following levels of the agent object hierarchy (upper overrides the lower level):

• the_genome::individual_genome::dies();
• the_neurobio::appraisal::dies();
• the_neurobio::gos_global::dies();
• the_individual::individual_agent::dies().

Definition at line 1076 of file m_genome.f90.

genome_individual_check_alive()

elemental logical function the_genome::genome_individual_check_alive

(

class(individual_genome), intent(in)

this

)

Check if the individual is alive.

Definition at line 1085 of file m_genome.f90.

genome_individual_check_dead()

elemental logical function the_genome::genome_individual_check_dead

(

class(individual_genome), intent(in)

this

)

Check if the individual is dead (the opposite of is_alive).

Definition at line 1095 of file m_genome.f90.

genome_individual_recombine_homol_full_rand_alleles()

subroutine the_genome::genome_individual_recombine_homol_full_rand_alleles	(	class(individual_genome), intent(inout)	this,
		class(individual_genome), intent(in)	mother,
		class(individual_genome), intent(in)	father,
		real(srp), intent(in), optional	exchange_ratio
	)

Internal genetic recombination backend, exchange individual alleles between homologous chromosomes in mother and father genomes to form the this (offspring) genome. Fully random recombination.

Note

Note that in this procedure, each of the individual alleles is copied from the mother or from the farther independently and randomly. This means that genetic distances are equal and there is no linkage disequilibrium.

Note also that recombinations across the homologs of the same chromosome are also randomised, i.e. different in all homologs.

Parameters

[in]	mother	mother The mother object the_genome::individual_genome class.
[in]	father	father The father object the_genome::individual_genome class.

Notable local variables

• n_alleles is the number of alleles for each of the chromosomes, dynamically updated within the loops.

acomp_vect_mother and acomp_vect_father are the vectors of additive allele components that are obtained from the mother and the father.

Implementation details

Check optional exchange_ratio parameter that defines the ratio of the alleles that are inherited from the mother. If absent, get the default value from the commondata::genome_recombination_ratio_mother parameter.

Nested loops: HOMOLOGS, CHROMOSOMES, ALLELES

Loop through the chromosomes (CHROMOSOMES block over i) and their homologues (HOMOLOGS block over j) and set the genetic make up of the this object from the genes of the mother and the father objects.

The outline of the main loop is:

• homologues
  • chromosomes
    • alleles

Warning

Note that it is not possible to use parallel do concurrent loops here due to non-pure random call of the RAND_R4() function in the innermost loop (ALLELES block over k).

First, get the number of alleles for each of the chromosomes: n_alleles.

Then, loop over the n_alleles alleles (ALLELES block over k):

Randomly select if this specific allele (k) is copied from the mother or is a subject to (random) recombination and gets the values from the father. This is determined stochastically using the exchange_ratio ratio dummy argument or the commondata::genome_recombination_ratio_mother parameter as the probability to get the allele from the mother.

• Get the vectors of additive allele components for the mother.

Finally, set the vector of additive allele components of the this agent from the mother's vector.

• Get the vectors of additive allele components for the father.

Finally, set the vector of additive allele components of the this agent from the father's vector.

Definition at line 1116 of file m_genome.f90.

genome_individual_recombine_homol_part_rand_alleles()

subroutine the_genome::genome_individual_recombine_homol_part_rand_alleles	(	class(individual_genome), intent(inout)	this,
		class(individual_genome), intent(in)	mother,
		class(individual_genome), intent(in)	father,
		real(srp), intent(in), optional	exchange_ratio
	)

Internal genetic recombination backend, exchange individual alleles between homologous chromosomes in mother and father genomes to form the this (offspring) genome. Partially random recombination, identical across the homologous chromosomes.

Note

Note that in this procedure, each of the individual alleles is copied from the mother or from the farther independently and partially randomly. This means that genetic distances are equal and there is no linkage disequilibrium.

Note also that recombination is identical across all homologs of the same chromosome.

Parameters

[in]	mother	mother The mother object the_genome::individual_genome class.
[in]	father	father The father object the_genome::individual_genome class.

Notable local variables

• n_alleles is the number of alleles for each of the chromosomes, dynamically updated within the loops.

acomp_vect_mother and acomp_vect_father are the vectors of additive allele components that are obtained from the mother and the father.

Implementation details

Check optional exchange_ratio parameter that defines the ratio of the alleles that are inherited from the mother. If absent, get the default value from the commondata::genome_recombination_ratio_mother parameter.

Nested loops: CHROMOSOMES, ALLELES, HOMOLOGS

Loop through the chromosomes (CHROMOSOMES block over i) and their and set the genetic make up of the this object from the genes of the mother and the father objects.

The outline of the main loop is:

• chromosomes
  • alleles
    • homologues (within if block)

Warning

Note that it is not possible to use parallel do concurrent loops here due to non-pure random call of the RAND_R4() function in the innermost loop (ALLELES block over k).

First, get the number of alleles for each of the chromosomes: n_alleles.

Then, loop over the n_alleles alleles (ALLELES block over k):

Randomly select if this specific allele (k) is copied from the mother or is a subject to (random) recombination and gets the values from the father. This is determined stochastically using the exchange_ratio ratio dummy argument or the commondata::genome_recombination_ratio_mother parameter as the probability to get the allele from the mother.

Finally, loop through the homologues of the ith chromosome (HOMOLOGS block over j) and set the same allele across all homologues the same way (transferred from mother or the father). Thus, all homologues have identical non-random recombination pattern, either from the mother or from the father.

• Get the vectors of additive allele components for the mother.

Finally, set the vector of additive allele components of the this agent from the mother's vector.

• Get the vectors of additive allele components for the father.

Finally, set the vector of additive allele components of the this agent from the father's vector.

Definition at line 1227 of file m_genome.f90.

genome_individual_crossover_homol_fix()

subroutine the_genome::genome_individual_crossover_homol_fix	(	class(individual_genome), intent(inout)	this,
		class(individual_genome), intent(in)	mother,
		class(individual_genome), intent(in)	father,
		logical, dimension(max_nalleles,n_chromosomes), intent(in), optional	pattern_matrix
	)

Internal fixed genetic crossover backend, exchange blocks of alleles between homologous chromosomes in mother and father genomes to form the this (offspring) genome.

Note

Note that in this procedure, blocks of alleles (i.e. sections of the chromosomes) are copied from the mother or from the farther. The blocks are fixed and defined by the boolean parameter matrix commondata::genome_crossover_fixed_mother. This means that genetic recombination is non-random, genetic distances are small within the blocks that are inherited from the same parent and relatively large between the alleles that belong to different blocks (i.e. the genetic distances depend on the proximity of the alleles). This makes linkage disequilibrium possible.

Parameters

[in]	mother	mother The mother object the_genome::individual_genome class.
[in]	father	father The father object the_genome::individual_genome class.
[in]	pattern_matrix	pattern_matrix is an optional boolean pattern matrix that determines the pattern of fixed chromosome crossover. For each chromosome, the alleles that are marked with the TRUE (YES) values are inherited from the mother whereas those marked FALSE (NO) are inherited from the father. see commondata::genome_crossover_fixed_mother.

Notable local variables

• n_alleles is the number of alleles for each of the chromosomes, dynamically updated within the loops.

acomp_vect_mother and acomp_vect_father are the vectors of additive allele components that are obtained from the mother and the father.

Implementation details

Check optional pattern_matrix parameter matrix. If it is not provided, use the default commondata::genome_crossover_fixed_mother.

Nested parallel loops: HOMOLOGS, CHROMOSOMES, ALLELES

Loop through the chromosomes (CHROMOSOMES block over i) and their homologues (HOMOLOGSblock over j) and set the genetic make up of the this object from the genes of the mother and the father objects.

First, get the number of alleles for each of the chromosomes: n_alleles.

Then, loop over the n_alleles alleles (ALLELES block over k):

Check the boolean pattern matrix pattern_matrix_locvalue, if it is TRUE, the allele value is copied from the mother, otherwise from the father.

If block check

• Get the vectors of additive allele components for the mother.

Finally, set the vector of additive allele components of the this agent from the mother's vector.

• Get the vectors of additive allele components for the father.

Finally, set the vector of additive allele components of the this agent from the father's vector.

Definition at line 1347 of file m_genome.f90.

trait_init_genotype_gamma2gene()

subroutine the_genome::trait_init_genotype_gamma2gene	(	class(individual_genome), intent(inout)	this,
		real(srp), intent(out)	this_trait,
		logical, dimension(:,:), intent(in)	g_p_matrix,
		real(srp), intent(in)	init_val,
		real(srp), intent(in), optional	gerror_cv,
		character(len=*), intent(in)	label
	)

Initialise an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy.

Create a new trait object from the genotype according to the boolean genotype x phenotype matrix g_p_matrix by commondata::gamma2gene() function and the init_val baseline value. It also introduces an initial Gaussian variance with the coefficient of variation=gerror_cv. Normally, g_p_matrix, init_val and gerror_cv are parameters set in commondata for each specific trait. For example, for thyroid hormone (the_hormones::hormones class level up) they are:

• commondata::thyroid_genotype_phenotype,
• commondata::thyroid_init and
• commondata::thyroid_gerror_cv.

Note

This procedure has the intent [inout] for this, the agent as it modifies the label. Therefore, it should be mainly used for initialisation of agent traits.

Warning

This code does not (yet) work with haploid genotype commondata::chromosome_ploidy = 1, in such a case there is no need to select random homologous chromosome and it is impossible to set the two parameters of the commondata::gamma2gene function (there is only a single chromosome).

Parameters

[out]	this_trait	this_trait the component of the individual agent object hierarchy that we are initialising in.
[in]	g_p_matrix	g_p_matrix matrix genotype x phenotype, the matrix setting the correspondence between the genome and the phenotype.
[in]	init_val	init_val start value, trait initialisation baseline value parameter, e.g. for thyroid it is commondata::thyroid_init.
[in]	gerror_cv	gerror_cv error value, trait Gaussian error value, e.g. for thyroid it is commondata::thyroid_gerror_cv, if absent, we don't introduce random error and the initial trait values are deterministic by the genome.
[in]	label	label a label for the allele locus that sets the phenotypic object.

Implementation details

Set trait values from the genome using the g_p_matrix matrix. We first need to select two chromosomes from the available set (normally two, but possibly more) for input into commondata::gamma2gene() function. We do it random.

Then, cycle through and select a different chromosomes (i.e. cycle if happens to coincide with the first). (Use do while chromosome1 = chromosome2 construct.)

As a result there are two distinct chromosomes k1 and k2. This unique chromosomes selection part of the code precludes the use of haploid genome.

Loop over chromosomes and alleles

• set label to the trait locus allele
• The initial trait value is determined using the commondata::gamma2gene() function using additive allele components. So, we first get the two vectors of additive allele components.
• And get the value of the trait from commondata::gamma2gene().

  Note

  commondata::gamma2gene() now accepts vectors of additive allele components.

Definition at line 1471 of file m_genome.f90.

trait_set_genotype_gamma2gene()

subroutine the_genome::trait_set_genotype_gamma2gene	(	class(individual_genome), intent(in)	this,
		real(srp), intent(out)	this_trait,
		logical, dimension(:,:), intent(in)	g_p_matrix,
		real(srp), intent(in)	init_val,
		real(srp), intent(in), optional	gerror_cv
	)

Set an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy.

This is almost the same as the_genome::trait_init_genotype_gamma2gene(), but does not modify the this object (it has intent [in]). Therefore it should be used for setting such traits as behavioural expectancies.

Note

This procedure has the intent [in] for this, the agent as it does not modify the object (e.g. does not set label). Therefore, it can be used in assessing the subjective expectancies.

Warning

This code does not (yet) work with haploid genotype commondata::chromosome_ploidy = 1, in such a case there is no need to select random homologous chromosome and it is impossible to set the two parameters of the commondata::gamma2gene() function (there is only a single chromosome).

Parameters

[out]	this_trait	this_trait the component of the individual agent object hierarchy that we are initialising.
[in]	g_p_matrix	matrix genotype x phenotype, the matrix setting the correspondence between the genome and the phenotype.
[in]	init_val	init_val start value, trait initialisation baseline value parameter, e.g. for thyroid it is commondata::thyroid_init.
[in]	gerror_cv	gerror_cv error value, trait Gaussian error value, e.g. for thyroid it is commondata::thyroid_gerror_cv, if absent, we don't introduce random error and the initial trait values are deterministic by the genome.

Implementation details

Set trait values from the genome using the g_p_matrix matrix. We first need to select two chromosomes from the available set (normally two, but possibly more) for input into commondata::gamma2gene() function. We do it random.

Then, we cycle through and select a different chromosomes (i.e. cycle if happens to coincide with the first.

As a result there are two distinct chromosomes k1 and k2. This unique chromosomes selection part of the code precludes the use of haploid genome.

Loop over chromosomes and alleles

• The initial trait value is determined using the commondata::gamma2gene() function using additive allele components. So, we first get the two vectors of additive allele components.
• And get the value of the trait from commondata::gamma2gene().

  Note

  commondata::gamma2gene() now accepts vectors of additive allele components.

Definition at line 1561 of file m_genome.f90.

trait_init_linear_sum_additive_comps_2genes_r()

subroutine the_genome::trait_init_linear_sum_additive_comps_2genes_r	(	class(individual_genome), intent(inout)	this,
		real(srp), intent(out)	this_trait,
		logical, dimension(:,:), intent(in)	g_p_matrix,
		real(srp), intent(in)	phenotype_min,
		real(srp), intent(in)	phenotype_max,
		character(len=*), intent(in)	label
	)

Initialise an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy.

Create a new trait object from the genotype according to the boolean genotype x phenotype matrix g_p_matrix by a simple linear scaling transformation function. Normally, g_p_matrix, is a parameters set in commondata for each specific trait.

Note

This version uses only two chromosomes for compatibility with the counterpart the_genome::trait_init_genotype_gamma2gene() procedure; the code logic is almost the same as in that based on the neuronal response function.

Parameters

[out]	this_trait	this_trait the component of the individual agent object hierarchy that we are initialising in.
[in]	g_p_matrix	g_p_matrix matrix genotype x phenotype, the matrix setting the correspondence between the genome and the phenotype.
[in]	phenotype_min	init_val start value, trait initialisation baseline value parameter, e.g. for thyroid it is commondata::thyroid_init.
[in]	label	label a label for the allele locus that sets the phenotypic object.

Implementation details

Set trait values from the genome using the g_p_matrix matrix. We first need to select two chromosomes from the available set (normally two, but possibly more) for input into gamma2gene. We do it random. Assign the first chromosome.

Also assign the second chromosome.

Then, cycle through and select a different chromosomes (i.e. cycle if happens to coincide with the first). (Use do while chromosome1 = chromosome2` construct.)

As a result there are two distinct chromosomes k1 and k2. This unique chromosomes selection part of the code precludes the use of haploid genome.

Loop over chromosomes and alleles

• set label to the trait locus allele
• The initial trait value is determined using the gamma2gene function using additive allele components. So, we first get the two vectors of additive allele components.
• Unlike trait_init_genotype_gamma2gene, the output phenotypic value is obtained by simple linear commondata::rescale from the minimum genotype range to the phenotypic range set by the phenotype_min and phenotype_max parameters.

Definition at line 1646 of file m_genome.f90.

trait_set_linear_sum_additive_comps_2genes_r()

subroutine the_genome::trait_set_linear_sum_additive_comps_2genes_r	(	class(individual_genome), intent(in)	this,
		real(srp), intent(out)	this_trait,
		logical, dimension(:,:), intent(in)	g_p_matrix,
		real(srp), intent(in)	phenotype_min,
		real(srp), intent(in)	phenotype_max
	)

Set an individual trait of the agent that depends on the genotype. This can be any trait upwards in the class hierarchy.

This is almost the same as the_genome::trait_init_linear_sum_additive_comps_2genes_r(), but does not modify the this object (it has intent [in]). Therefore it should be used for setting such traits as behavioural expectancies. Create a new trait object from the genotype according to the boolean genotype x phenotype matrix g_p_matrix by a simple linear scaling transformation function. Normally, g_p_matrix, is a parameters set in commondata for each specific trait.

Note

This version uses only two chromosomes for compatibility with the counterpart the_genome::trait_init_genotype_gamma2gene() procedure; the code logic is almost the same as in that based on the neuronal response function.

Parameters

[out]	this_trait	this_trait the component of the individual agent object hierarchy that we are initialising in.
[in]	g_p_matrix	g_p_matrix matrix genotype x phenotype, the matrix setting the correspondence between the genome and the phenotype.
[in]	phenotype_min	init_val start value, trait initialisation baseline value parameter, e.g. for thyroid it is commondata::thyroid_init.

Implementation details

Set trait values from the genome using the g_p_matrix matrix. We first need to select two chromosomes from the available set (normally two, but possibly more) for input into gamma2gene. We do it random. Assign the first chromosome.

Also assign the second chromosome.

Then, cycle through and select a different chromosomes (i.e. cycle if happens to coincide with the first). (Use do while chromosome1 = chromosome2` construct.)

As a result there are two distinct chromosomes k1 and k2. This unique chromosomes selection part of the code precludes the use of haploid genome.

Loop over chromosomes and alleles

• The initial trait value is determined using the gamma2gene function using additive allele components. So, we first get the two vectors of additive allele components.
• Unlike trait_init_genotype_gamma2gene, the output phenotypic value is obtained by simple linear commondata::rescale from the minimum genotype range to the phenotypic range set by the phenotype_min and phenotype_max parameters.

Definition at line 1742 of file m_genome.f90.

genome_mutate_wrapper()

subroutine the_genome::genome_mutate_wrapper	(	class(individual_genome), intent(inout)	this,
		real(srp), intent(in), optional	p_point,
		real(srp), intent(in), optional	p_set,
		real(srp), intent(in), optional	p_swap,
		real(srp), intent(in), optional	p_shift
	)

Perform a probabilistic random mutation(s) on the individual genome. This is a high level wrapper to build mutations from various components.

Parameters

[in]	p_point	p_point optional probability of mutation, if absent, the default value commondata::mutationrate_point is used.
[in]	p_set	p_set optional probability of mutation, if absent, the default value commondata::mutationrate_batch is used.
[in]	p_swap	p_swap optional probability of mutation, if absent, the default value commondata::mutationrate_batch is used.
[in]	p_shift	p_shift optional probability of mutation, if absent, the default value commondata::mutationrate_batch is used.

Implementation notes

Each of the mutations below is a random process that occurs with a specific probability that is set by its respective mutation rate parameter.

• Call for a point mutation on a single random allele component of a randomly chosen chromosome using the the_genome::gene::mutate_point() method.

Call for a point mutation on a whole random allele (batch of allele components) of a randomly chosen chromosome using the the_genome::gene::mutate_set() method.

Definition at line 1820 of file m_genome.f90.

Variable Documentation

modname

character (len=*), parameter, private the_genome::modname = "(THE_GENOME)"

private

Definition at line 27 of file m_genome.f90.