HotshotGoku/Simulation_templated_pattern_prediction

The files in the dataset are organized as follows:

1. seed_to_sim_determinisitic
2. sim_to_exp_diffusion
3. information_encoding_decoding

These three folders represent the 3 major pipelines in the manuscript: Mechanistically conditioned generative modelling of biologically realistic bacterial patterns (Insert link later). First folder is for the determinisitic ResNet model, second folder is for the diffusion model, and third is for testing the inverse problem prediction with generated patterns.

1. seed_to_sim_determinisitic

• Sim_050924_seed.tar is the input seed dataset, Sim_050924_intermediate_Tp3.tar is the output, default end-point patterns and Sim_050924_complex_Tp3.tar is the end-point patterns with different parameters- thinner but denser branching.

• Sim_050924_ModelTesting_seed.tar is the input seed test dataset, Sim_050924_ModelTesting_intermediate.tar is the default patterns for the test set, and Sim_050924_ModelTesting_complex.tar is the thinner but denser branches test set. saved_models.tar contains all saved trained models that are used in the manuscript.

  i)Pixel_32x32x3to32x32x4_dilRESNET_30k_graypatterns_seedtointermediate_v101_4-1759366230_best.pt is the model used in Fig 2 for mapping between seed to simulation.

  ii)Pixel_32x32x3to32x32x4_dilRESNET_graypatterns_intermediatetocomplex_Model_30000_v101_Cluster_GPU_tfData-1759363890_best.pt is the model used in Fig 3 to map between one simulation to another.

  iii) models_Fig4 contain all models that were used in Fig4a and b- testing the model performance as a function of training data size. The number following intermediatetocomplex and preceeding _v1015 represents the training data size.

  iv) models_dataagumentation_Fig4 contains all models that were used in Fig4c and d- testing the model performance as a function of unique training data size. The number following intermediatetocomplex and preceeding _v1015 represents the unique training data size(Total training size used for all images was 40k, different images represents different amounts of augmentation accordingly)

2. sim_to_exp_diffusion

• Exp.tar contains the raw experimental images that are used in the model training.

• Exp_SimcorrtoExp_seed.tar contains the seeding configurations of the experimental images in the training set.

• SimcorrtoExp.tar contains the paired simulation images corresponding to the experimental dataset.

• Exp_testset.tar contains the experimental images that are used in the model inference as ground truths

• Exp_SimcorrtoExp_testset_seed.tar contains the seeding configurations corresponding to the experimental and simulation images in the test set.

• SimcorrtoExp_testset.tar contain the paired simulatoin images that are used in the model inference as spatial inputs.

• checkpoint_simtoexp.tar is the trained ControlNet model checkpoint used in Fig 5 to map from simulation to experiments.

• checkpoint_seedtoexp.tar is the trained ControlNet model checkpoint used in Supplementary Fig 17 to map from seed to experiments.

• Dissimilarity_scoring.tar contains the images used in Supplementary Figures 17 and 18, and the trained contrastive learning model.

• inference_folders.tar contains various results from the trained ControlNet model on the test set.

  i) v2025926_1251_simtoexp_v3 contains the results of the base ControlNet model used in Fig 5.

  ii) v20251011_841_seedtoexp_swapped_v3 contains the results of the ControlNet trained on seeding configurations as spatial input in Supp Fig 17. The rest of the images are from the ablation study shown in Supp Fig 18.

  iii) v20251023_1458_no_guess : Guess mode= True

  iv) v20251023_1753_no_negative: Blank negative prompt

  v) v20251023_1756_plus_positive: Added positive prompt

  vi) v20251023_1758_low_strength_point85: Lower conditioning control

  vii) v20251023_1758_high_strength_1point25: Higher conditioning control

  viii) v20251023_1759_higher_DDIM_steps_100: Higher DDIM steps(100)

  ix) v20251023_181_lower_guidance_9point0: Lower guidance scale of 9.0 used in model training

3. information_encoding_decoding

• Exp_testset_seed.tar contains the experimental seed images(random seeding locations) that are used in the model testset.
• Exp_testset_fixed.tar contains the experimental image with defined seeding conditions that are used in the testset.
• Exp_testset_fixed_seed.tar contains the defined seeding conditions that are used in the testset.
• Generated_patterns_selected.tar contains a select few synthetic generated patterns for display in Fig 6.
• checkpoint_exptoseed.tar is the trained UNet model checkpoint used in Fig 6 to map from experiment to seeds
• checkpoint_simtoseed.tar is the trained UNet model checkpoint used in Supplementary Fig 20 to map from simulations/experiments to seeds.

Note:

1. The datasets in the manuscript are augmented using rotations to increase the training size for model training. All the datasets here are non-augmented. Instructions on how to augment the dataset are outlined in the github repo.

2. Supplementary Figure 13 in the manuscipt involves the use of experimental images. To run this model, the appropriate images can be downloaded from the sim_to_exp_diffusion dataset.

3. For synthetic data generation used in Figure 6, entire library is quite big (30GB), can be generated using script in Github repo. Selected images for display are attached in Generated_patterns_selected.tar