القائمة
Cell biology fundamentals including cell structure, membrane transport, cell signaling, cell cycle, apoptosis, and microscopy techniques for life science applications.
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استنادا إلى تصنيف SOC المهني
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Techniques and frameworks for generating new data instances that match the distribution of training data
Advanced techniques for training and fine-tuning transformer-based language models at scale
Foundational understanding and practical implementation of transformer-based language models
Integrating and reasoning across multiple data modalities including text, images, audio, and video
| 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 |
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.
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")
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")
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%}")
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}%")
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)
# 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