NeuralBlitz 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
- Controls: Include appropriate controls
- Blinding: Blind samples when possible
- Replication: Technical and biological replicates
- Quantification: Use appropriate metrics
- 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
- Cell culture and counting
- Membrane transport
- Cell signaling pathways
- Cell cycle analysis
- Microscopy techniques