ffsshhttiikk Evolution
What I Do
Evolutionary biology studies the origin and descent of species, natural selection, and the processes that generate biological diversity. I cover natural selection, genetic drift, gene flow, mutation, speciation, phylogenetics, and evolutionary genetics. I help analyze phylogenetic relationships, calculate selection pressures, and understand adaptation.
When to Use Me
- Building and interpreting phylogenetic trees
- Calculating natural selection and dN/dS ratios
- Understanding mechanisms of speciation
- Analyzing population genetic variation
- Studying adaptive evolution and convergence
- Dating evolutionary events (molecular clocks)
- Understanding coevolution and host-pathogen dynamics
Core Concepts
- Natural Selection: Directional, stabilizing, disruptive selection
- Genetic Drift: Founder effect, bottleneck, random sampling
- Gene Flow: Migration, admixture, population structure
- Mutation: Source of genetic variation, mutation rates
- Speciation: Allopatric, sympatric, parapatric mechanisms
- Phylogenetics: Clades, monophyly, parsimony, likelihood methods
- Molecular Evolution: dN/dS, positive selection, purifying selection
- Adaptive Radiation: Ecological opportunity, character displacement
- Coevolution: Predator-prey, host-parasite, mutualistic arms races
- Molecular Clock: Mutation rate calibration, divergence dating
Code Examples
import numpy as np
from typing import List, Dict, Tuple
from collections import Counter
class NaturalSelection:
def __init__(self, species: str):
self.species = species
def calculate_fitness(self, phenotype_value: float,
optimum: float,
omega: float) -> float:
return np.exp(-0.5 * ((phenotype_value - optimum) / omega)**2)
def directional_selection_gradient(self, mean_phenotype: float,
optimum: float,
heritability: float,
selection_strength: float) -> float:
return heritability * selection_strength * (optimum - mean_phenotype)
def calculate_response_to_selection(self, s: float,
h2: float) -> float:
R = s * h2 # Breeder's equation
return R
def equilibrium_allele_frequency(self, s: float,
h: float) -> float:
return s / (s + h * s) # Approximate for recessive deleterious allele
def fixation_probability(self, initial_freq: float,
selection_coefficient: float,
population_size: int) -> float:
if selection_coefficient == 0:
return initial_freq
s = selection_coefficient
N = population_size
return (1 - np.exp(-2 * s * N * initial_freq)) / \
(1 - np.exp(-4 * s * N))
class Phylogenetics:
def __init__(self, alignment_file: str):
self.alignment_file = alignment_file
def calculate_pairwise_distance(self, seq1: str, seq2: str) -> float:
mismatches = sum(1 for a, b in zip(seq1, seq2) if a != b)
return mismatches / len(seq1)
def calculate_kimura_distance(self, transitions: int,
transversions: int,
sites: int) -> float:
K = -0.5 * np.log(1 - 2*P - Q) - 0.25 * np.log(1 - 2*Q)
P = transitions / sites
Q = transversions / sites
return K
def upgma_clustering(self, distances: Dict[Tuple, float]) -> Dict:
clusters = {i: [i] for i in range(len(distances))}
current_distances = distances.copy()
tree = {}
while len(clusters) > 1:
min_pair = min(current_distances.keys(),
key=lambda x: current_distances[x])
cluster1, cluster2 = min_pair
distance = current_distances[min_pair]
new_cluster = f"node_{len(clusters)}"
tree[new_cluster] = {
'children': [cluster1, cluster2],
'distance': distance / 2
}
clusters[new_cluster] = clusters[cluster1] + clusters[cluster2]
del clusters[cluster1]
del clusters[cluster2]
return tree
def calculate_divergence_time(self, d: float,
mutation_rate: float) -> float:
return d / (2 * mutation_rate) # MYA (millions years ago)
def likelihood_ratio_test(self, null_likelihood: float,
alt_likelihood: float) -> float:
lr = 2 * (alt_likelihood - null_likelihood)
df = 1 # Typically df=1 for nested models
p_value = 1 - stats.chi2.cdf(lr, df)
return {'test_statistic': lr, 'p_value': p_value}
class MolecularEvolution:
def __init__(self, gene_name: str):
self.gene_name = gene_name
def calculate_dn_ds(self, dn: float, ds: float) -> float:
return dn / ds if ds > 0 else float('inf')
def calculate_synonymous_sites(self, sequence: str) -> int:
codon_table = {
'TTT': 1, 'TTC': 1, 'TTA': 0, 'TTG': 0,
'TCT': 1, 'TCC': 1, 'TCA': 1, 'TCG': 1,
'TAT': 0, 'TAC': 0, 'TAA': 0, 'TAG': 0,
'TGT': 0, 'TGC': 0, 'TGA': 0, 'TGG': 0,
'CTT': 1, 'CTC': 1, 'CTA': 1, 'CTG': 1,
'CCT': 1, 'CCC': 1, 'CCA': 1, 'CCG': 1,
'CAT': 0, 'CAC': 0, 'CAA': 0, 'CAG': 0,
'CGT': 1, 'CGC': 1, 'CGA': 1, 'CGG': 1,
'ATT': 1, 'ATC': 1, 'ATA': 1, 'ATG': 0,
'ACT': 1, 'ACC': 1, 'ACA': 1, 'ACG': 1,
'AAT': 0, 'AAC': 0, 'AAA': 0, 'AAG': 0,
'AGT': 1, 'AGC': 1, 'AGA': 0, 'AGG': 0,
'GTT': 1, 'GTC': 1, 'GTA': 1, 'GTG': 1,
'GCT': 1, 'GCC': 1, 'GCA': 1, 'GCG': 1,
'GAT': 0, 'GAC': 0, 'GAA': 0, 'GAG': 0,
'GGT': 1, 'GGC': 1, 'GGA': 1, 'GGG': 1
}
syn_sites = 0
for i in range(0, len(sequence), 3):
codon = sequence[i:i+3]
if len(codon) == 3:
syn_sites += codon_table.get(codon, 0)
return syn_sites
def site_positive_selection(self, omega: float) -> str:
if omega > 1:
return "positive_selection"
elif omega < 1:
return "purifying_selection"
return "neutral"
selection = NaturalSelection("DarwinFinches")
fitness = selection.calculate_fitness(12.5, optimum=12.0, omega=2.0)
print(f"Fitness: {fitness:.3f}")
phylo = Phylogenetics("mitochondrial.fasta")
dist = phylo.calculate_pairwise_distance("ATCGATCG", "ATCGATTA")
print(f"Distance: {dist:.4f}")
Best Practices
- Use appropriate molecular clock calibration points
- Account for rate heterogeneity among sites and lineages
- Validate phylogenetic methods with known relationships
- Consider substitution model complexity vs. data support
- Use proper outgroups for root placement
- Apply bootstrap resampling for clade support
- Consider population structure in evolutionary analysis
- Distinguish between orthologs and paralogs in gene trees
- Use multiple methods to test for selection
- Consider fossil evidence for divergence dating