How To: Fit Logistic Growth Models#
Fit logistic growth curves to time-series colony measurements and extract kinetic parameters (growth rate, carrying capacity, lag time) using LogGrowthModel.
[1]:
from phenotypic.data import load_meas
from phenotypic.analysis import LogGrowthModel
[2]:
df = load_meas()
df.head()
[2]:
| Metadata_Condition | Metadata_Media | Metadata_Set | Metadata_Replicate | Metadata_Time | Metadata_Strain | Metadata_FileName | Object_Label | Bbox_CenterRR | Bbox_CenterCC | ... | TextureGray_InfoCorrelation1-deg135-scale04 | TextureGray_InfoCorrelation2-deg000-scale04 | TextureGray_InfoCorrelation2-deg045-scale04 | TextureGray_InfoCorrelation2-deg090-scale04 | TextureGray_InfoCorrelation2-deg135-scale04 | Metadata_StrainID | TextureGray_HaralickVariance-avg-scale04 | CorrectedCarryingCapacity_Intensity | CorrectedCarryingCapacity_Area | Metadata_Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 30C | S | 3 | 4 | 24 | CBS11445 | 30C_2_3S_4 | 14 | 263.025755 | 327.103517 | ... | -0.033962 | 0.523065 | 0.263252 | 0.538240 | 0.318621 | 220 | 0.662881 | 1629.588639 | 4183.333333 | S 30C |
| 1 | 30C | S | 3 | 5 | 24 | CBS11445 | 30C_2_3S_5 | 14 | 309.120466 | 291.332736 | ... | -0.035568 | 0.605276 | 0.243759 | 0.563349 | 0.376115 | 220 | 1.373371 | 1433.869174 | 3225.333333 | S 30C |
| 2 | 30C | S | 3 | 6 | 24 | CBS11445 | 30C_2_3S_6 | 13 | 312.525658 | 262.907288 | ... | -0.068525 | 0.733840 | 0.519368 | 0.658842 | 0.497267 | 220 | 1.298390 | 1206.170181 | 2984.666667 | S 30C |
| 3 | 30C | S | 3 | 12 | 24 | CBS11445 | 30C_2_3S_12 | 16 | 296.079598 | 305.151655 | ... | -0.057264 | 0.715456 | 0.470510 | 0.672158 | 0.475346 | 220 | 1.870195 | 1279.117711 | 3074.666667 | S 30C |
| 4 | 30C | S | 3 | 8 | 24 | CBS11445 | 30C_2_3S_8 | 14 | 318.208931 | 260.158426 | ... | -0.034110 | 0.589887 | 0.358457 | 0.596423 | 0.342946 | 220 | 1.067527 | 1244.967859 | 2988.333333 | S 30C |
5 rows × 245 columns
Fit the Model#
Specify the target measurement column, grouping structure, and time label. The model fits a logistic curve to each group independently.
[3]:
model = LogGrowthModel(
on="Shape_Area",
groupby=["Metadata_Strain"],
time_label="Metadata_Time",
agg_func="mean",
)
[4]:
results = model.analyze(df)
results
[4]:
| Metadata_Strain | LogGrowthModel_r | LogGrowthModel_K | LogGrowthModel_N0 | LogGrowthModel_µmax | LogGrowthModel_Kmax | ModelMetrics_MAE | ModelMetrics_MSE | ModelMetrics_RMSE | ModelMetrics_R2 | ModelMetrics_OptimizerLoss | ModelMetrics_OptimizerStatus | ModelMetrics_NumSamples | LogGrowthModel_lambda | LogGrowthModel_beta | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CBS11445 | 0.128296 | 2774.486146 | 361.146883 | 88.989209 | 3018.454545 | 149.156592 | 37500.693073 | 193.650957 | 0.628794 | 1235.513459 | 3 | 5 | 1.2 | 2 |
| 1 | CBS1553 | 0.096670 | 2461.589556 | 268.039039 | 59.490509 | 2472.666667 | 111.983445 | 41337.150766 | 203.315397 | 0.348866 | 915.756366 | 2 | 5 | 1.2 | 2 |
| 2 | CBS1554 | 0.049705 | 2549.056960 | 1027.927953 | 31.675310 | 2552.125000 | 50.849750 | 4101.485366 | 64.042840 | 0.953202 | 1411.496844 | 3 | 5 | 1.2 | 2 |
The results DataFrame contains fitted parameters for each group: growth rate, carrying capacity (K), and lag time. Use these to compare strain fitness across experimental conditions.