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PromoterModeling

Constraint-based mathematical models of gene regulation. This package predicts effector concentration and promoter-specific biological constants by fitting machine learning derived regulatory metrics to a biophysical mathematical promoter model. See biorxiv for additional details - https://doi.org/10.1101/2024.05.30.596718.

How to use this package

  1. Install necessary packages - requirements.txt contains all packages used in a functioning environment.
conda create --name PromoterModeling --file ./requirements.txt --channel bioconda

Note

All files are already included for utilizing P1K (E. coli iModulons). If wanting to use these, skip to step 3. I suggest first using the small test set to make sure the code functions properly on your setup. The default settings are for this small test set, which can be started at step 4.

  1. Download external data files, move to data/external/. These include:

    1. data/imodulon_info/ - Needs to contain A.csv, M.csv, iM_table.csv, log_tpm.csv. Optionally can include sample_table.csv and gene_info.csv for use in some troubleshooting. Necessary files can most easily be found on https://imodulondb.org.
    2. data/regulonDB_files - Needs to contain network_tf_operon.txt, NetWorkTFGene.txt, and OperonSet.txt. These can be downloaded from https://regulondb.ccg.unam.mx/datasets.
    3. data/validation_data_sets/ - Various data sets used to validate models. Only heinemann_protein_conc.xlsx is necessary, which provides limitations on model parameters and is available in the supplement of 10.1038/nbt.3418. Optionally can include variuos other validation datasets, most importantly metabolite data which by default is in the data/validation_data_sets/stationary_phase folder sourced from 10.1038/nmeth.3584.
    4. (optional) data/external/KEGG/ - Includes pathway_to_gene.txt and pathways.txt, whichare used in some troubleshooting steps.

  2. Set up the option files in data/options/

    • data/options contains two files:
      • gene_flags.csv contains options for how the model handles each of the individual genes.
      • TF_flags.csv contains options for how the model handles each individual regulator group.
    1. Generate template options files by running 1_model_setup/1_setup_data_options.
      1. This will generate two files in data/options titled template_gene_flags.csv and template_TF_flags.csv
    2. Set the effector and effector type for each regulator in template_TF_flags.csv that you intend to include:
      • (note) - Both a small test case and more complete case of regulators for P1K (E. coli iModulons) is included already, so no need to complete this manual step if you plan on using P1K.
      1. Set the effector, make sure the naming matches that of any included metabolites data. By default, these names are in validation_data_sets/stationary_phase/metaboltie_data.xlsx.
      2. The following options are currently available, for each regutor modify one column that best fits the specific promoter type to 1:
        1. cAct_no_effector_form - TF is a promoter which activates without needing to bind an effector
        2. cAct_multi_effector_binding - TF is a promoter which activates with the binding of one or more copies of the same effector
        3. cAct_multi_co_effector_binding - TF is a promoter which activates with the binding of two different effectors
        4. cInh_no_effector_form - TF is an inhibitor which activates without needing to bind an effector
        5. cInh_multi_effector_binding - TF is an inhibitor which activates with the binding of one or more copies of the same effector
        6. cInh_multi_co_effector_binding - TF is an inhibitor which activates with the binding of two different effectors
    3. Rename the template files to gene_flags.csv and TF_flags.csv and place them in the data/options folder.

  3. Modify data/options/settings.csv to point to desired settings flags, if necessary add path to the GAMS executable.

  4. Remove outlier samples to avoid fitting on them by running 1_model_setup/2_find_outliers.ipynb

  5. Set basal conditions for the individual genes

    1. Run 1_model_setup/3_select_basal_conditions.ipynb
      1. This will set the same basal condition for all genes of a regulatory case. The basal condition should correspond to zero regulator activity. Gene expression is standardized for each gene across all samples. The basal conditions is selected based on if the case's regulator is a promoter, inhibitor, or dual-regulator:
        1. Regulator is a promoter - The sample with the lowest expression is picked
        2. Regulator is an inhibitor - The sample with the highest expression is picked
        3. Regulator is a dual-regulator - The sample with the most average expression is picked
      2. You may want to manually set some basal conditions, if you have samples that are better approximations of zero activity, such as knockouts. Basal conditions need to be the same across all genes of the same regulatory case.

  6. Generate various data files used for the model and next steps by running 1_model_setup/4_generate_various_interim_data_files.ipynb

  7. Generate initial values for GAMS inputs by running 1_model_setup/5_generate_GAMS_input_files.ipynb

  8. Select grid values for each gene.

    1. Run 1_model_setup/6_pick_grid_for_gene.ipynb
      1. Each gene needs a set of associated constants (KdRNAP, KeqOpening, KdRNAPActivator) to solve the model. This notebook generates these constants for each.
      2. 9 different sets of solutions are generated ranging from low to high KdRNAP values. Typically grid #7 works best and is the default.
      3. KdRNAPActivator is needed for any case with an activator. The model can pick an ideal KdRNAPActivator value if one exists, but this process often has no solution and occasionally fails. This requires some manual work, so for each gene check the following:
        1. The output for each gene will include the gene name followed by its activating and inhibiting iModulon.
        2. Check if the range for mRNA ratio makes sense.
          • mRNA ratio should be almost entirely below 1 if inhibitor only, almost entirely above 1 if promoter only, mean should be near 1 if dual-regulator.
        3. If inhibitor - Check for negative correlation between cInhibitor and actual mRNA ratio, normal range for cInhibitor values (0-1000 is ideal, 0-10000 is okay, 0-100000 may cause overfitting on outliers in later steps).
        4. If activator - Need to verify the KdRNAPActivator value:
          1. 1st Objecitive Function and 2nd Objective function are used to pick a KdRNAPActivator value which balances between a KdRNAPActivator value which causes the activator increase recruitment of RNAP to the promoter site while not making it a completely binary switch.
          2. Ideally, the vertical dotted line will be at the minimum point in both plots. If it is not, there are a few options:
            1. Look at the same plots for all 9 grid values, if it works for one of these, select this grid value.
            2. If the dotted line is not picking the minimum point but there appears to be one for one of the grid values - modify the initial_guess_ratio column in data/options/gene_flags.csv to somewhere between 0 and 1 and rerun the code to regenerate the results. This sets an initial different guess for KdRNAPActivator which often allows the optimization code to work properly in this case.
        5. If activator and valid KdRNAPActivator value - Check for positive correlation between cActivator and actual mRNA ratio, normal range for cActivator values (0-1000 is ideal, 0-10000 is okay, 0-100000 may cause overfitting on outliers in later steps).
        6. If previous steps worked, change the "checked" and "include" columns in the gene_flags.csv to TRUE. Set the "grid_use" column to the preferred grid value.
      4. Generally, for the above steps, it is easier to iterate over everything once and then reiterate through the genes which failed the checks. If a gene is unable to work, you may simply change the "checked" column to TRUE and the "include" column to FALSE. This will exclude the gene from the model.

  9. If grid counts were modified, rerun GAMS input generation by running 1_model_setup/5_generate_GAMS_input_files.ipynb.

  10. Generate the GAMS model by running 2_GAMS/1_run_model.ipynb, which will run GAMS to optimize the model for the input data.

  11. Visualize results.

    • Run various notebooks in 3_visualize_results to look at the GAMS results.

  12. Optimize parameters if necessary by running 2_GAMS/1_optimize_GAMS_parameters.ipynb to select better constraints on the parameters for GAMS to create more accurate models.

    • This process is very slow and is best run either over a weekend or on an independent server. It involves running GAMS on a wide variety of parameter constraints, taking the best result, creating a new set of parameter results closer to the best result, and repeating this process a set number of times.

Notes

  • Test flags exist in the options/ folders and are the default to run, they will not generate good models but will validate that the code is working.
  • More complete flags exist in the options/ folder to create models for the all of the best cases of P1K.
  • A full GAMS license is likely required for any models of notable scale (more than ~100 samples).
  • 4_various_tools contains a few niche use conversion functions, primarily for interfacing between mathematica, GAMS, and python.
  • This code and pipeline were developed for analysis of the P1K dataset and E. coli. Additional modifications may be necessary to adapt this model to other species.

Terminology

  1. Regulatory case - A set of genes which all have the same promoter and inhibitor. For example: All genes inhibited by argR and not promoted by any other genes are a regulatory case.
  2. cInhibitor - A proxy value for the activity of an inhibitor, the GAMS model optimizes on these values.
  3. cActivator - A proxy value for the activity of an activator, the GAMS model optimizes on these values.

Contact

Please feel free to contact [email protected] with any questions.