Evolution

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Mechanisms of Evolution

Five Forces of Evolution

# Evolution mechanisms
evolution_forces = {
    'mutation': {
        'description': 'Random changes in DNA sequence',
        'type': 'Ultimate source of genetic variation',
        'rate': 'Varies by gene and organism',
        'examples': ['Point mutations', 'Insertions', 'Deletions', 'Duplications']
    },
    'natural_selection': {
        'description': 'Differential reproductive success',
        'result': 'Adaptation to environment',
        'types': ['Directional', 'Stabilizing', 'Disruptive', 'Sexual']
    },
    'genetic_drift': {
        'description': 'Random change in allele frequencies',
        'effect': 'Stronger in small populations',
        'types': ['Founder effect', 'Bottleneck']
    },
    'gene_flow': {
        'description': 'Movement of genes between populations',
        'effect': 'Reduces genetic differentiation',
        'examples': ['Migration', 'Hybridization']
    },
    'non_random_mating': {
        'description': 'Assortative or disassortative mating',
        'effect': 'Changes genotype frequencies',
        'examples': ['Inbreeding', 'Sexual selection']
    }
}

Natural Selection

# Selection coefficient calculation
def selection_coefficient(w1, w2):
    """
    Calculate selection coefficient.
    w1, w2: Fitness values (0-1)
    """
    if w1 == 0:
        return 1.0
    return 1 - (w2 / w1)

def allele_frequency_change(p, q, s, t):
    """
    Calculate allele frequency change under selection.
    p: Frequency of dominant allele
    q: Frequency of recessive allele  
    s: Selection coefficient against dominant
    t: Selection coefficient against recessive
    """
    delta_p = (p * q * (p * s - q * t)) / (1 - s * p**2 - t * q**2)
    return delta_p

# Types of selection
selection_types = {
    'directional': {
        'description': 'One extreme favored over other',
        'result': 'Shift in mean phenotype',
        'example': 'Antibiotic resistance in bacteria'
    },
    'stabilizing': {
        'description': 'Intermediate phenotype favored',
        'result': 'Reduced variance',
        'example': 'Human birth weight'
    },
    'disruptive': {
        'description': 'Both extremes favored',
        'result': 'Increased variance, possible speciation',
        'example': 'African finch beak size'
    },
    'balancing': {
        'description': 'Multiple alleles maintained',
        'result': 'Polymorphism',
        'example': 'Sickle cell and malaria resistance'
    },
    'sexual': {
        'description': 'Traits affecting mating success',
        'result': 'Sexual dimorphism',
        'example': 'Peacock tail, deer antlers'
    }
}

Genetic Drift

# Wright-Fisher model
def wright_fisher_model(N, p, generations):
    """
    Simulate genetic drift using Wright-Fisher model.
    N: Population size (diploid)
    p: Initial allele frequency
    generations: Number of generations
    """
    p_history = [p]
    current_p = p
    
    for gen in range(generations):
        # Number of alleles
        2N = 2 * N
        
        # Binomial sampling
        n_A = np.random.binomial(2N, current_p)
        current_p = n_A / 2N
        
        p_history.append(current_p)
    
    return p_history

# Effective population size
def effective_population_size(Nm, Nf):
    """
    Calculate effective population size.
    Nm: Number of males
    Nf: Number of females
    """
    if Nm + Nf == 0:
        return 0
    return (4 * Nm * Nf) / (Nm + Nf)

# Founder effect and bottleneck
founder_bottleneck = {
    'founder_effect': {
        'description': 'New population started by small group',
        'result': 'Reduced genetic diversity',
        'examples': ['Galapagos finches', 'Hawaiian islands species']
    },
    'bottleneck': {
        'description': 'Population temporarily reduced',
        'result': 'Loss of rare alleles',
        'examples': ['Northern elephant seal', 'Cheetah']
    }
}

Population Genetics

Hardy-Weinberg Equilibrium

# Hardy-Weinberg proportions
def hardy_weinberg_proportions(p, q=None):
    """
    Calculate genotype frequencies under HWE.
    p: Frequency of dominant allele
    q: Frequency of recessive allele (computed if not given)
    """
    if q is None:
        q = 1 - p
    
    return {
        'AA': p**2,      # Homozygous dominant
        'Aa': 2 * p * q, # Heterozygous
        'aa': q**2       # Homozygous recessive
    }

def test_hardy_weinberg(observed_AA, observed_Aa, observed_aa, N):
    """
    Test for Hardy-Weinberg equilibrium using chi-square test.
    """
    # Total number of alleles
    total_alleles = 2 * N
    
    # Observed allele frequencies
    p = (2 * observed_AA + observed_Aa) / total_alleles
    q = 1 - p
    
    # Expected under HWE
    expected_AA = p**2 * N
    expected_Aa = 2 * p * q * N
    expected_aa = q**2 * N
    
    # Chi-square test
    chi_square = ((observed_AA - expected_AA)**2 / expected_AA +
                  (observed_Aa - expected_Aa)**2 / expected_Aa +
                  (observed_aa - expected_aa)**2 / expected_aa)
    
    # Degrees of freedom = 1 (one allele frequency estimated)
    # Critical value at p=0.05 with df=1 is 3.84
    return chi_square, chi_square < 3.84

# Fixation index (Fst)
def calculate_fst(populations):
    """
    Calculate Wright's Fst to measure population differentiation.
    """
    # Heterozygosity within populations (Hs)
    # Total heterozygosity (Ht)
    # Fst = (Ht - Hs) / Ht
    pass

Molecular Population Genetics

# Tajima's D
def tajima_d(seq1, seq2, theta_estimates):
    """
    Calculate Tajima's D test statistic.
    Tests for selection using frequency spectrum.
    """
    # Based on differences between theta estimates
    # Positive D: balancing selection or bottleneck
    # Negative D: directional selection or population expansion
    pass

# McDonald-Kreitman test
def mckreitman_test(aligned_sequences):
    """
    Test for positive selection using MK test.
    Compare substitution rates in:
    - Fixed differences between species (divergence)
    - Polymorphism within species
    """
    # Count fixed substitutions
    # Count polymorphic sites
    # Use G-test for independence
    pass

Phylogenetics

Phylogenetic Methods

# Distance-based methods
distance_methods = {
    'UPGMA': {
        'name': 'Unweighted Pair Group Method with Arithmetic Mean',
        'assumptions': 'Molecular clock',
        'strength': 'Simple, fast',
        'weakness': 'Assumes clock-like evolution'
    },
    'neighbor_joining': {
        'name': 'Neighbor-Joining',
        'assumptions': 'None (correct tree for additive distances)',
        'strength': 'Does not assume clock',
        'weakness': 'Can produce negative branch lengths'
    }
}

# Character-based methods
character_methods = {
    'maximum_parsimony': {
        'description': 'Find tree requiring fewest changes',
        'strength': 'Fast, simple',
        'weakness': 'May not find most likely tree'
    },
    'maximum_likelihood': {
        'description': 'Find tree maximizing likelihood given model',
        'strength': 'Uses explicit evolutionary model',
        'weakness': 'Computationally intensive'
    },
    'bayesian': {
        'description': 'Sample from posterior distribution of trees',
        'strength': 'Provides uncertainty estimates',
        'weakness': 'Computationally intensive'
    }
}

Tree Building

# Simple neighbor-joining implementation
def neighbor_joining(distance_matrix, labels):
    """
    Build phylogenetic tree using NJ algorithm.
    """
    n = len(distance_matrix)
    active = set(range(n))
    tree = {}
    
    while len(active) > 2:
        # Calculate Q matrix
        Q = np.zeros((n, n))
        for i in active:
            for j in active:
                if i != j:
                    r_i = sum(distance_matrix[i, k] for k in active if k != i)
                    r_j = sum(distance_matrix[j, k] for k in active if k != j)
                    Q[i, j] = (n - 2) * distance_matrix[i, j] - r_i - r_j
        
        # Find minimum Q
        min_q = float('inf')
        min_pair = None
        for i in active:
            for j in active:
                if i < j and Q[i, j] < min_q:
                    min_q = Q[i, j]
                    min_pair = (i, j)
        
        i, j = min_pair
        
        # Calculate branch lengths
        r_i = sum(distance_matrix[i, k] for k in active if k != i)
        r_j = sum(distance_matrix[j, k] for k in active if k != j)
        
        dist_ij = distance_matrix[i, j]
        branch_i = (dist_ij + (r_i - r_j) / (n - 2)) / 2
        branch_j = dist_ij - branch_i
        
        # Record in tree
        # ... (implementation details)
        
        # Update distance matrix
        # ... (implementation details)
    
    # Connect final two nodes
    # Return tree

Molecular Clock

# Molecular clock hypothesis
molecular_clock = {
    'hypothesis': 'Mutations accumulate at constant rate',
    'neutral_theory': 'Most mutations are neutral',
    'applications': [
        'Dating divergence events',
        'Estimating species ages',
        'Molecular phylogenetics'
    ],
    'caveats': [
        'Rates vary across lineages',
        'Rate heterogeneity possible',
        'Generation time effects'
    ]
}

def calculate_divergence_time(distance, rate):
    """
    Calculate divergence time from genetic distance.
    """
    return distance / (2 * rate)

# Calibration points
calibration_types = {
    'paleontological': 'Fossil ages',
    'geological': 'Vicariance events',
    'biological': 'Known hybridization/crossing',
    'historical': 'Documented events'
}

Molecular Evolution

dN/dS Ratio

# dN/dS (Ka/Ks) analysis
def calculate_ka_ks(sequence1, sequence2, genetic_code='standard'):
    """
    Calculate synonymous (dS) and nonsynonymous (dN) substitutions.
    """
    # Identify synonymous and nonsynonymous sites
    # Count substitutions in each category
    # Apply appropriate substitution model
    pass

def interpret_ka_ks(ratio):
    """
    Interpret dN/dS ratio.
    """
    if ratio < 1:
        return 'Purifying (negative) selection'
    elif ratio == 1:
        return 'Neutral evolution'
    else:
        return 'Positive selection'

# Example interpretation
ka_ks_interpretation = {
    'ratio_less_1': {
        'interpretation': 'Purifying selection',
        'meaning': 'Deleterious mutations removed'
    },
    'ratio_1': {
        'interpretation': 'Neutral',
        'meaning': 'No selective pressure'
    },
    'ratio_greater_1': {
        'interpretation': 'Positive selection',
        'meaning': 'Adaptive evolution at site'
    }
}

Positive Selection

# Detecting positive selection
selection_detection = {
    'site_models': {
        'M1a': 'Nearly neutral (two categories)',
        'M2a': 'Adds positive selection category',
        'M7': 'Beta distribution (10 categories)',
        'M8': 'Beta + ω > 1 category'
    },
    'branch_models': {
        'one_ratio': 'Single ω for all branches',
        'foreground': 'Different ω for foreground branch'
    },
    'branch_site_models': {
        'A': 'Allow ω > 1 on foreground branches at some sites',
        'null': 'ω ≤ 1 everywhere'
    }
}

Speciation

Modes of Speciation

speciation_modes = {
    'allopatric': {
        'description': 'Geographic isolation',
        'barriers': ['Mountains', 'Rivers', 'Oceans', 'Distance'],
        'prevalence': 'Most common in animals'
    },
    'parapatric': {
        'description': 'Adjacent ranges with some overlap',
        'mechanism': 'Differential selection across gradient',
        'example': 'Ring species'
    },
    'sympatric': {
        'description': 'Reproductive isolation without geography',
        'mechanism': 'Ecological differentiation, sexual selection',
        'prevalence': 'Common in plants, some insects'
    },
    'peripatric': {
        'description': 'Small population at edge of range',
        'mechanism': 'Founder effect + selection'
    }
}

# Reproductive isolation
reproductive_isolation = {
    'prezygotic': {
        'habitat': 'Different habitats',
        'temporal': 'Different timing',
        'behavioral': 'Sexual isolation',
        'mechanical': 'Incompatible genitalia',
        'gametic': 'Gamete incompatibility'
    },
    'postzygotic': {
        'hybrid_inviability': 'Hybrid dies',
        'hybrid_sterility': 'Hybrid sterile',
        'hybrid_breakdown': 'F2 hybrids have problems'
    }
}

Adaptive Radiation

adaptive_radiation = {
    'definition': 'Rapid diversification of single lineage',
    'requirements': [
        'Ecological opportunity',
        'Available niches',
        'Key innovation',
        'Few competitors'
    ],
    'classic_examples': [
        'Darwin's finches (Galapagos)',
        'Hawaiian honeycreepers',
        'Cichlid fish (African lakes)',
        'Marsupials (Australia)'
    ],
    'stages': [
        '1. Colonization of new area',
        '2. Ecological release',
        '3. Divergence and adaptation',
        '4. Coexistence through niche partitioning'
    ]
}

Human Evolution

Hominin Evolution

hominin_lineage = {
    'sahelanthropus_tchadensis': {
        'date': '7-6 million years ago',
        'location': 'Chad',
        'features': ['Bipedal', 'Small brain']
    },
    'ardipithecus_ramidus': {
        'date': '4.4 million years ago',
        'location': 'Ethiopia',
        'features': ['Arborial', 'Bipedal']
    },
    'australopithecus_afarensis': {
        'date': '3.9-2.9 million years ago',
        'location': 'East Africa',
        'features': ['Lucy', 'Fully bipedal', 'Small brain']
    },
    'homo_habilis': {
        'date': '2.4-1.4 million years ago',
        'location': 'Africa',
        'features': ['Stone tools', 'Larger brain']
    },
    'homo_erectus': {
        'date': '1.9-0.1 million years ago',
        'location': 'Africa, Asia',
        'features': ['Fire', 'Acheulean tools', 'Out of Africa'
    },
    'homo_neanderthalensis': {
        'date': '400,000-40,000 years ago',
        'location': 'Europe, Asia',
        'features': ['Complex tools', 'Burial', 'Large brain']
    },
    'homo_sapiens': {
        'date': '300,000 years ago to present',
        'location': 'Worldwide',
        'features': ['Modern behavior', 'Language', 'Art']
    }
}

Evidence for Human Evolution

human_evolution_evidence = {
    'fossil_record': [
        'Skull shape changes',
        'Pelvic structure',
        'Dental changes',
        'Bipedal adaptations'
    ],
    'molecular': [
        'DNA similarity to great apes',
        'Mitochondrial Eve',
        'Y-chromosome Adam',
        'Neanderthal admixture'
    ],
    'comparative': [
        'Embryonic development',
        'Vestigial structures',
        'Atavisms',
        'Molecular homology'
    ],
    'behavioral': [
        'Tool use',
        'Art and symbolism',
        'Language',
        'Social organization'
    ]
}

Evidence for Evolution

Types of Evidence

# Molecular clock evidence
molecular_evidence = {
    'cytochrome_c': {
        'organisms_compared': 'Humans and chimpanzees',
        'differences': '0 differences',
        'interpretation': 'Recent common ancestor'
    },
    'hemoglobin': {
        'similarity': 'Humans and mice have ~85% similarity',
        'interpretation': 'Shared ancestry'
    },
    'endogenous_retroviruses': {
        'description': 'Viral DNA integrated into genome',
        'evidence': 'Same ERV loci in related species',
        'interpretation': 'Common ancestry'
    }
}

Common Errors to Avoid

• Confusing evolution with progress: Evolution has no direction
• Thinking evolution is random: Selection is not random, mutation is
• Ignoring genetic drift: Especially important in small populations
• Conflating correlation with causation: Environment influences traits
• Misunderstanding "survival of the fittest": Not about strongest
• Ignoring the role of chance: Genetic drift, founder effects
• Assuming humans are "more evolved": All lineages evolve equally
• Confusing species concepts: Biological, phylogenetic, morphological
• Not understanding "junk DNA": Much has regulatory functions
• Ignoring gene regulation: Most evolution is regulatory changes