Cunning Strategy#

Synthesis#

There is a fascinating relationship between tryptamine and noradrenaline in terms of their synthetic processes, as they are both biogenic amines derived from amino acids, but they belong to different biosynthetic pathways and serve distinct physiological roles.

DMT can produce vivid “projections” of mystical experiences involving euphoria and dynamic pseudohallucinations of geometric forms
– Wikipedia

Tryptamine Synthesis#

Tryptamine is synthesized from the amino acid tryptophan via the following steps:

  1. Decarboxylation: Tryptophan is decarboxylated by the enzyme aromatic L-amino acid decarboxylase (AADC), producing tryptamine.

  2. Tryptamine can act as a neuromodulator or serve as a precursor for other biologically active molecules like serotonin (5-HT) and melatonin.

Noradrenaline Synthesis#

Noradrenaline (norepinephrine) is synthesized from the amino acid tyrosine through the catecholamine biosynthesis pathway:

  1. Hydroxylation: Tyrosine is converted to L-DOPA by tyrosine hydroxylase.

  2. Decarboxylation: L-DOPA is decarboxylated by AADC to produce dopamine.

  3. Hydroxylation: Dopamine is converted to noradrenaline by dopamine β-hydroxylase.

Shared Enzymatic Features#

  • Both pathways involve aromatic L-amino acid decarboxylase (AADC) as a key enzyme. AADC catalyzes the decarboxylation of L-tryptophan to tryptamine and L-DOPA to dopamine.

  • This shared enzymatic step underscores a biochemical link between the pathways, even though tryptamine and noradrenaline are functionally and structurally different.

Interactions in Neurotransmission#

  • While tryptamine and noradrenaline operate in different neurochemical systems, they can indirectly influence each other:

    • Tryptamine’s Neuromodulation: Tryptamine can modulate the release of other neurotransmitters, including noradrenaline, by acting on trace amine-associated receptors (TAARs).

    • Catecholamine-Tryptamine Crosstalk: Changes in noradrenaline levels, such as through stress or pharmacological interventions, can influence serotoninergic and tryptaminergic systems due to overlapping neural networks.

Evolutionary Perspective#

The fact that both tryptamine and noradrenaline pathways utilize AADC hints at an evolutionary economy in neurotransmitter biosynthesis. It highlights a conserved mechanism that allows the generation of diverse signaling molecules from amino acids, ensuring broad functionality in the nervous system.

Simulation#

Ayahuasca is a traditional psychoactive brew used for centuries by indigenous tribes in the Amazon basin, primarily for spiritual and healing ceremonies. It holds a sacred status in many shamanic traditions, functioning as a bridge to deeper understanding, healing, and connection to the spiritual world. The word “ayahuasca” comes from Quechua, combining aya (spirit, soul) and huasca (vine, rope), translating roughly to “vine of the soul.”

Composition#

Ayahuasca is typically made by combining two main plant components:

  1. Banisteriopsis caapi (the vine): Contains harmala alkaloids (harmine, harmaline, and tetrahydroharmine), which act as reversible monoamine oxidase inhibitors (MAOIs). These inhibitors allow the psychoactive compound DMT to bypass breakdown in the digestive system and enter the bloodstream.

  2. Psychotria viridis (the leaf): A plant rich in N,N-Dimethyltryptamine (DMT), a potent psychedelic compound.

The brew may also include other plants for specific effects, depending on the tradition.

Effects#

Ayahuasca induces an intense, prolonged psychedelic experience lasting 4–6 hours, characterized by:

  • Visual and auditory hallucinations: Often vivid and symbolic, described as encountering otherworldly realms, spirits, or ancestors.

  • Emotional catharsis: Feelings of deep introspection, reconciliation with past traumas, and emotional release.

  • Physical purging: Many users experience nausea, vomiting, or diarrhea, viewed as a cleansing or purging process.

Mechanism of Action#

The effects of ayahuasca are due to the interaction between the harmala alkaloids and DMT.

  • The harmala alkaloids inhibit monoamine oxidase (MAO), allowing DMT to remain active when ingested orally.

  • DMT then binds to serotonin receptors (5-HT2A, 5-HT2C, etc.), producing the profound psychedelic effects.

Cultural and Modern Context#

  • Traditional Use: Indigenous shamans (curanderos) use ayahuasca in ceremonial settings to diagnose and heal physical, emotional, or spiritual ailments. The experience is often guided with icaros (sacred songs) and rituals.

  • Global Spread: In recent decades, ayahuasca has gained popularity in the West as a tool for spiritual growth and psychotherapy. Many seek it for its potential to address issues like depression, PTSD, addiction, and existential crises.

  • Scientific Interest: Studies suggest potential benefits in reducing depression and addiction symptoms. Ayahuasca’s ability to induce neuroplasticity and enhance emotional processing is a growing area of research.

Risks and Considerations#

  • Not for Everyone: People with certain medical conditions (e.g., heart problems, mental health disorders like schizophrenia) or those on medications (e.g., SSRIs) should avoid ayahuasca due to the risk of severe interactions.

  • Psychological Impact: The intense introspection can lead to challenging or even distressing experiences, often referred to as “ego death.”

  • Unregulated Retreats: The global ayahuasca tourism industry has raised concerns about exploitation, unsafe practices, and cultural appropriation.

Spiritual Symbolism#

The brew often represents death and rebirth, acting as a metaphorical gateway to the “other side.” Users frequently describe their experiences as profound encounters with their shadow selves, universal truths, or cosmic connections.

Hide code cell source
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network structure
def define_layers():
    return {
        'World': ['Cosmos', 'Earth', 'Life', 'Cost', 'Parallel', 'Time'], # Divine: Cosmos-Earth; Red Queen: Life-Cost; Machine: Parallel-Time
        'Perception': ['Perspectivism'],
        'Agency': ['Surprise', 'Optimism'],
        'Generativity': ['Anarchy', 'Oligarchy', 'Monarchy'],
        'Physicality': ['Dynamic', 'Partisan', 'Common Wealth', 'Non-Partisan', 'Static']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Perspectivism':
        return 'yellow'
    if layer == 'World' and node in [ 'Time']:
        return 'paleturquoise'
    if layer == 'World' and node in [ 'Parallel']:
        return 'lightgreen'
    if layer == 'World' and node in [ 'Cosmos', 'Earth']:
        return 'lightgray'
    elif layer == 'Agency' and node == 'Optimism':
        return 'paleturquoise'
    elif layer == 'Generativity':
        if node == 'Monarchy':
            return 'paleturquoise'
        elif node == 'Oligarchy':
            return 'lightgreen'
        elif node == 'Anarchy':
            return 'lightsalmon'
    elif layer == 'Physicality':
        if node == 'Static':
            return 'paleturquoise'
        elif node in ['Non-Partisan', 'Common Wealth', 'Partisan']:
            return 'lightgreen'
        elif node == 'Dynamic':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    # Add edges (without weights)
    for layer_pair in [
        ('World', 'Perception'), ('Perception', 'Agency'), ('Agency', 'Generativity'), ('Generativity', 'Physicality')
    ]:
        source_layer, target_layer = layer_pair
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Snails Pace vs. Compressed Time", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()
../_images/b8238d80ec1e4d6147a86d3e87a244bbe54b24c4ee102f00fe0e0ccfc2e0292c.png
../_images/blanche.png

Fig. 7 Nostalgia & Romanticism. When monumental ends (victory), antiquarian means (war), and critical justification (bloodshed) were all compressed into one figure-head: hero#