cell-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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python # 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