Kinetic tRNA charging port

configs/default.json

@@ -48,7 +48,8 @@
         "save_intermediates": false,
         "intermediates_directory": "",
         "variable_elongation_transcription": true,
-        "variable_elongation_translation": false
+        "variable_elongation_translation": false,
+        "optimize_trna_charging_kinetics": false
     },
 
     "analysis_options": {

configs/no_grc.json

@@ -0,0 +1,33 @@
+{
+    "experiment_id": "no_grc",
+    "suffix_time": false,
+    "parca_options": {
+        "cpus": 16
+    },
+    "fail_at_max_duration": true,
+    "sim_data_path": null,
+    "generations": 32,
+    "n_init_sims": 16,
+    "single_daughters": true,
+    "emitter": "parquet",
+    "emitter_arg": {
+        "out_dir": "out"
+    },
+    "variants": {
+        "condition": {
+            "condition": {"value": ["with_aa"]}
+        }
+    },
+    "analysis_options": {
+        "single": {"mass_fraction_summary": {}},
+        "multivariant": {"trna_synthetase_concs": {"skip_gens": 4}}
+    },
+    "steady_state_trna_charging": false,
+    "kinetic_trna_charging": false,
+    "coarse_kinetic_elongation": false,
+    "ppgpp_regulation": false,
+    "mechanistic_replisome": false,
+    "mechanistic_translation_supply": false,
+    "mechanistic_aa_transport": false,
+    "aa_supply_in_charging": false
+}

ecoli/analysis/multivariant/dummy.py

@@ -112,6 +112,7 @@
     "listeners__mass__volume",
     "listeners__mass__water_mass",
     "listeners__monomer_counts",
+    "listeners__monomer_degradation__monomers_degraded",
     "listeners__replication_data__critical_initiation_mass",
     "listeners__replication_data__critical_mass_per_oriC",
     "listeners__replication_data__fork_coordinates",
@@ -122,20 +123,23 @@
     "listeners__replication_data__total_DnaA_boxes",
     "listeners__ribosome_data__aa_count_in_sequence",
     "listeners__ribosome_data__aa_counts",
+    "listeners__ribosome_data__activation_prob",
     "listeners__ribosome_data__actual_elongation_hist",
     "listeners__ribosome_data__actual_elongations",
     "listeners__ribosome_data__actual_prob_translation_per_transcript",
     "listeners__ribosome_data__did_initialize",
     "listeners__ribosome_data__did_terminate",
     "listeners__ribosome_data__effective_elongation_rate",
     "listeners__ribosome_data__elongations_non_terminating_hist",
+    "listeners__ribosome_data__is_n_ribosomes_to_activate_reduced",
     "listeners__ribosome_data__mRNA_TU_index",
     "listeners__ribosome_data__mRNA_is_overcrowded",
     "listeners__ribosome_data__max_p",
     "listeners__ribosome_data__max_p_per_protein",
     "listeners__ribosome_data__n_ribosomes_on_each_mRNA",
     "listeners__ribosome_data__n_ribosomes_on_partial_mRNA_per_transcript",
     "listeners__ribosome_data__n_ribosomes_per_transcript",
+    "listeners__ribosome_data__n_ribosomes_to_activate",
     "listeners__ribosome_data__num_trpA_terminated",
     "listeners__ribosome_data__process_elongation_rate",
     "listeners__ribosome_data__protein_mass_on_polysomes",
@@ -218,6 +222,12 @@
     "listeners__transcript_elongation_listener__count_NTPs_used",
     "listeners__transcript_elongation_listener__count_rna_synthesized",
     "listeners__transcript_elongation_listener__counts_attenuated",
+    "listeners__trna_charging__codons_read",
+    "listeners__trna_charging__initial_disagreements",
+    "listeners__trna_charging__initiated",
+    "listeners__trna_charging__ribosome_initiation_argA",
+    "listeners__trna_charging__ribosome_positions_argA",
+    "listeners__trna_charging__saturation_trna",
     "listeners__unique_molecule_counts__DnaA_box",
     "listeners__unique_molecule_counts__RNA",
     "listeners__unique_molecule_counts__active_RNAP",

ecoli/analysis/multivariant/trna_synthetase_concs.py

@@ -0,0 +1,241 @@
+import pickle
+import warnings
+from datetime import datetime, timezone
+from io import StringIO
+from typing import Any
+
+# noinspection PyUnresolvedReferences
+from duckdb import DuckDBPyConnection
+from scipy.constants import N_A
+import polars as pl
+
+from ecoli.library.parquet_emitter import (
+    read_stacked_columns,
+    field_metadata,
+    skip_n_gens,
+)
+from reconstruction.ecoli.fit_sim_data_1 import SimulationDataEcoli
+
+
+def plot(
+    params: dict[str, Any],
+    conn: DuckDBPyConnection,
+    history_sql: str,
+    config_sql: str,
+    success_sql: str,
+    sim_data_dict: dict[str, dict[int, str]],
+    validation_data_paths: list[str],
+    outdir: str,
+    variant_metadata: dict[str, dict[int, Any]],
+    variant_names: dict[str, str],
+):
+    """
+    Analysis script for calculating the mean, standard deviation, and minimum values
+    of tRNA synthetase concentrations for a set of baseline simulations (no growth
+    rate control or kinetic tRNA charging) in minimal and rich media. The minimal
+    workflow required to generate this data is ``configs/no_grc.json``. The output
+    of this analysis should be used to replace
+    ``reconstruction/ecoli/flat/optimization/trna_synthetase_dynamic_range.tsv``
+    every time changes are made that could affect tRNA synthetase concentrations.
+
+    Args:
+        params: Dictionary of parameters that can include the following keys::
+
+            {
+                "skip_gens": int  # Number of initial generations to skip
+            }
+
+    """
+    # Skip n generations if provided
+    skip_gens = params.get("skip_gens")
+    if skip_gens is not None:
+        history_sql = skip_n_gens(history_sql, skip_gens)
+    # Figure out which variant(s) are minimal and which are rich media
+    experiment_ids = list(variant_names.keys())
+    assert len(experiment_ids) == 1, (
+        "This analysis script is only intended to be run with one experiment ID "
+        "at a time."
+    )
+    experiment_id = experiment_ids[0]
+    variant_name = variant_names[experiment_id]
+    assert variant_name == "condition", (
+        "This analysis script is only intended to be run with the 'condition' variant."
+    )
+    variant_id_data: dict[str, Any] = {
+        "basal": {
+            "variant_ids": [],
+            "bulk_ids": [],
+            "synthetase_ids": [],
+            "synthetase_idx": [],
+            "summary_stats": [],
+        },
+        "with_aa": {
+            "variant_ids": [],
+            "bulk_ids": [],
+            "synthetase_ids": [],
+            "synthetase_idx": [],
+            "summary_stats": [],
+        },
+    }
+    for variant_id, variant_params in variant_metadata[experiment_id].items():
+        condition_label: str = ""
+        if isinstance(variant_params, str):
+            if variant_params != "baseline":
+                warnings.warn(
+                    f"Ignoring variant {variant_id} with unexpected label: {variant_params}"
+                )
+                continue
+            condition_label = "basal"
+        elif isinstance(variant_params, dict):
+            condition_label = variant_params.get("condition", "")
+        else:
+            warnings.warn(
+                f"Ignoring variant {variant_id} with unsupported metadata type: {type(variant_params)}"
+            )
+            continue
+
+        media_info = variant_id_data[condition_label]
+        if variant_id not in media_info["variant_ids"]:
+            media_info["variant_ids"].append(variant_id)
+
+    for condition_label, media_info in variant_id_data.items():
+        if len(media_info["variant_ids"]) == 0:
+            raise ValueError(f"No variants found for media type: {condition_label}")
+        ordered_bulk_ids: list[str] | None = None
+        trna_synthetase_ids: list[str] | None = None
+        for variant_id in media_info["variant_ids"]:
+            var_subquery = f"FROM ({config_sql}) WHERE variant = {variant_id}"
+            bulk_ids = field_metadata(conn, var_subquery, "bulk")
+            if ordered_bulk_ids is None:
+                ordered_bulk_ids = bulk_ids
+            elif bulk_ids != ordered_bulk_ids:
+                raise ValueError(
+                    f"Bulk ID order mismatch for media type '{condition_label}'. Ensure all "
+                    "variants list bulk IDs in the same order."
+                )
+
+            sim_data_path = sim_data_dict[experiment_id].get(variant_id)
+            if sim_data_path is None:
+                raise ValueError(
+                    f"Missing sim_data path for experiment {experiment_id}, variant {variant_id}."
+                )
+            with open(sim_data_path, "rb") as f:
+                sim_data: SimulationDataEcoli = pickle.load(f)
+            variant_trna_synthetase_ids = list(
+                sim_data.process.transcription.synthetase_names
+            )
+            if trna_synthetase_ids is None:
+                trna_synthetase_ids = variant_trna_synthetase_ids
+            elif variant_trna_synthetase_ids != trna_synthetase_ids:
+                raise ValueError(
+                    "tRNA synthetase ID mismatch between variants. Ensure all variants "
+                    "have the same set of tRNA synthetases."
+                )
+
+        if ordered_bulk_ids is None or trna_synthetase_ids is None:
+            raise ValueError(
+                f"Unable to determine metadata for media type '{condition_label}'."
+            )
+        media_info["synthetase_ids"] = trna_synthetase_ids
+        media_info["bulk_ids"] = ordered_bulk_ids
+
+    # Get indices of tRNA synthetases in bulk IDs
+    # and read concentration summary stats
+    for condition_label, media_info in variant_id_data.items():
+        synthetase_ids = media_info.get("synthetase_ids")
+        bulk_ids = media_info.get("bulk_ids")
+        if synthetase_ids is None or bulk_ids is None:
+            raise ValueError(
+                f"Missing bulk or synthetase metadata for media type '{condition_label}'."
+            )
+        media_info["synthetase_idx"] = []
+        for synthetase_id in synthetase_ids:
+            try:
+                synthetase_idx = bulk_ids.index(synthetase_id)
+            except ValueError:
+                raise ValueError(
+                    f"tRNA synthetase ID '{synthetase_id}' not found in bulk IDs for "
+                    f"media type '{condition_label}'."
+                )
+            media_info["synthetase_idx"].append(synthetase_idx + 1)  # 1-based indexing
+        subquery = read_stacked_columns(
+            history_sql,
+            ["bulk", "listeners__mass__volume AS volume"],
+            order_results=False,
+        )
+        # Scale concs from counts / fL to umol / L
+        # 1e15 fL / L * 1 mol / N_A counts * 1e6 umol / mol
+        scale_factor = 1e15 / N_A * 1e6
+        # Read each variant ID individually as DuckDB does not support filter
+        # pushdown for IN statements (e.g. WHERE variant IN (...))
+        for variant_id in media_info["variant_ids"]:
+            variant_subquery = f"FROM ({subquery}) WHERE variant = {variant_id}"
+            trna_synthetase_stats = conn.sql(f"""
+                WITH synthetase_counts AS (
+                    SELECT list_select(bulk, {media_info["synthetase_idx"]}) AS counts,
+                    volume, variant
+                    FROM ({variant_subquery})
+                ),
+                unnested_counts AS (
+                    SELECT unnest(counts) AS counts, volume,
+                        generate_subscripts(counts, 1) AS count_idx, variant
+                    FROM synthetase_counts
+                ),
+                concs AS (
+                    SELECT counts / volume * {scale_factor} AS bulk_concs, count_idx, variant
+                    FROM unnested_counts
+                )
+                SELECT avg(bulk_concs) AS avg_concs, stddev(bulk_concs) AS std_concs,
+                    min(bulk_concs) AS min_concs, count_idx, variant
+                FROM concs
+                GROUP BY variant, count_idx
+                ORDER BY variant, count_idx
+                """).pl()
+            stats_column_names = {
+                "avg_concs": "mean (units.umol / units.L)",
+                "std_concs": "std (units.umol / units.L)",
+                "min_concs": "min (units.umol / units.L)",
+            }
+            synthetase_condition = [
+                f"{synth_id}__{condition_label}"
+                for synth_id in media_info["synthetase_ids"]
+            ]
+            trna_synthetase_stats = (
+                trna_synthetase_stats.rename(stats_column_names)
+                .with_columns(
+                    pl.col("count_idx")
+                    .map_elements(
+                        lambda idx: synthetase_condition[idx - 1],
+                        return_dtype=pl.Utf8,
+                    )
+                    .alias("synthetase_condition")
+                )
+                .select(["synthetase_condition"] + list(stats_column_names.values()))
+            )
+            media_info["summary_stats"].append(trna_synthetase_stats)
+
+    # Combine and save data
+    final_df = pl.concat(
+        [
+            pl.concat(condition_data["summary_stats"])
+            for condition_data in variant_id_data.values()
+        ]
+    ).sort("synthetase_condition")
+
+    # Build metadata comments for reproducibility
+    timestamp_utc = datetime.now(timezone.utc).isoformat()
+    metadata_lines = [
+        "# Generated by ecoli.analysis.multivariant.trna_synthetase_concs",
+        f"# Run timestamp (UTC): {timestamp_utc}",
+        f"# Experiment ID: {experiment_id}",
+        f"# Skip n gens: {skip_gens if skip_gens is not None else '0'}",
+    ]
+
+    output_path = f"{outdir}/trna_synthetase_dynamic_range.tsv"
+    buffer = StringIO()
+    final_df.write_csv(buffer, separator="\t", quote_style="non_numeric")
+    with open(output_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(metadata_lines))
+        f.write("\n")
+        f.write(buffer.getvalue())
+    print(f"Saved tRNA synthetase dynamic range data to: {output_path}")