cell-biology

name	Cell Biology
description	Cell biology fundamentals including cell structure, membrane transport, cell signaling, cell cycle, apoptosis, and microscopy techniques for life science applications.
license	MIT
compatibility	python>=3.8
audience	cell-biologists, biochemists, researchers, students
category	biology

Cell Biology

What I Do

I provide comprehensive cell biology tools including cell structure analysis, membrane transport calculations, cell signaling pathways, cell cycle modeling, apoptosis analysis, and microscopy quantification for life science applications.

When to Use Me

• Cell counting and viability
• Membrane transport analysis
• Signaling pathway modeling
• Cell cycle analysis
• Apoptosis detection
• Microscopy image analysis

Core Concepts

• Cell Structure: Organelles, cytoskeleton, membranes
• Membrane Transport: Diffusion, osmosis, active transport
• Cell Signaling: Receptors, second messengers
• Cell Cycle: G1, S, G2, M phases
• Apoptosis: Intrinsic and extrinsic pathways
• Cell Adhesion: Integrins, cadherins
• Cytoskeleton: Actin, microtubules, intermediate filaments
• Microscopy: Fluorescence, confocal, electron

Code Examples

Cell Counting and Viability

def trypan_blue_exclusion(live_count, total_count):
    return live_count / total_count * 100

def hemocytometer_calculation(count, squares, dilution_factor, depth=0.1):
    cells_per_ml = count / squares * dilution_factor / depth * 10**4
    return cells_per_ml

def doubling_time(N0, Nt, t):
    return t * np.log(2) / np.log(Nt / N0)

def confluence_estimation(area_fraction, total_area):
    return area_fraction / total_area * 100

live_count, total_count = 85, 100
viability = trypan_blue_exclusion(live_count, total_count)
print(f"Cell viability: {viability:.1f}%")

N0, Nt, t = 1000, 8000, 24
td = doubling_time(N0, Nt, t)
print(f"Doubling time: {td:.1f} hours")

Membrane Transport

def ficks_first_law(J, D, dC, dx):
    return -D * dC / dx

def ghk_voltage(V, P_K, P_Na, P_Cl, K_out, K_in, Na_out, Na_in, Cl_out, Cl_in):
    RT_F = 0.0267  # V at 37C
    
    P_total = P_K + P_Na + P_Cl
    num = P_K * K_out + P_Na * Na_out + P_Cl * Cl_in
    den = P_K * K_in + P_Na * Na_in + P_Cl * Cl_out
    
    return RT_F * np.log(num / den)

def osmotic_pressure(pi, C, R=0.0821, T=310):
    return pi * C * R * T

def pump_rate(ATP_consumed, efficiency=0.5):
    return ATP_consumed / efficiency

def diffusion_time(dx, D):
    return dx**2 / (2 * D)

D = 1e-6  # cm²/s
dx = 100e-4  # 100 microns
t = diffusion_time(dx, D)
print(f"Diffusion time: {t:.2f} seconds")

Cell Signaling

def receptor_ligand_binding(Kd, L):
    return L / (Kd + L)

def hill_equation(response, L, Kd, n):
    return L**n / (Kd**n + L**n)

def second_messenger_cascade(Receptor, amplification):
    return Receptor * amplification

def mapk_cascade(MKKK, MKK, MK, transcription_factor):
    return MKKK * MKK * MK * transcription_factor

def calcium_spark_frequency(Fura2_ratio, baseline):
    return Fura2_ratio / baseline

Kd = 1e-9  # nM
L = 1e-8   # M
occupancy = receptor_ligand_binding(Kd, L)
print(f"Receptor occupancy: {occupancy:.2%}")

Cell Cycle Analysis

def cell_cycle_phases(G1, S, G2, M, total=100):
    return {
        'G1': G1 / total * 100,
        'S': S / total * 100,
        'G2': G2 / total * 100,
        'M': M / total * 100
    }

def brdu_incorporation(BrdU_label, control):
    return BrdU_label / control * 100

def mitotic_index(mitotic_cells, total_cells):
    return mitotic_cells / total_cells * 100

def g2_m_checkpoint_activity(ATM_phosphorylation, Chk1_phosphorylation):
    return (ATM_phosphorylation + Chk1_phosphorylation) / 2

def senescence_beta_galactosidase(SA_beta_gal_positive, total):
    return SA_beta_gal_positive / total * 100

mitotic = 15
total = 1000
MI = mitotic_index(mitotic, total)
print(f"Mitotic index: {MI:.2f}%")

Microscopy Analysis

def fluorescence_intensity(fluorescence_background, area):
    return fluorescence_background / area

def colocalization_coefficient(ch1, ch2, threshold_ch1, threshold_ch2):
    overlap = np.sum((ch1 > threshold_ch1) & (ch2 > threshold_ch2))
    coef1 = overlap / np.sum(ch1 > threshold_ch1)
    coef2 = overlap / np.sum(ch2 > threshold_ch2)
    return coef1, coef2

def fRET_efficiency(donor_emission, acceptor_emission, FRET):
    return FRET / (donor_emission + acceptor_emission)

def frap_recovery(t, t_half, plateau, mobile_fraction):
    return plateau * (1 - np.exp(-np.log(2) / t_half * t))

def calculate_fluorescence_lifetime(tau, tau0):
    return tau / tau0

def particle_tracking_displacement(x, y, t):
    return np.sqrt((x[-1] - x[0])**2 + (y[-1] - y[0])**2)

Best Practices

1. Controls: Include appropriate controls
2. Blinding: Blind samples when possible
3. Replication: Technical and biological replicates
4. Quantification: Use appropriate metrics
5. Calibration: Calibrate instruments

Common Patterns

# Flow cytometry analysis
def flow_cytometry_gate(single_cells, debris):
    return single_cells / debris

# Western blot quantification
def western_blot_band_intensity(band, background):
    return band - background

Core Competencies

1. Cell culture and counting
2. Membrane transport
3. Cell signaling pathways
4. Cell cycle analysis
5. Microscopy techniques