Merge pull request #446 from biosustain/hybrid_solver

Hybrid algebra/ode solver

docs/bibliography.bib

@@ -136,3 +136,14 @@ @article{kacser1973
   langid = {english},
   keywords = {Arginine,Enzyme Repression,Enzymes,Feedback,Kinetics,Melanins,Metabolism,{Models, Chemical},Neurospora crassa,Tyrosine 3-Monooxygenase}
 }
+@inproceedings{margossianComputingSteadyStates2018,
+  title = {Computing Steady States with {{Stan}}'s Nonlinear Algebraic Solver},
+  booktitle = {Stan {{Conference}} 2018},
+  author = {Margossian, Charles},
+  year = {2018},
+  month = jan,
+  doi = {10.5281/zenodo.1284375},
+  url = {10.5281/zenodo.1284375},
+  abstract = {Stan's numerical algebraic solver can be used to solve systems of nonlinear algebraic equations with no closed form solutions. One of its key applications in scientific and engineering fields is the computation of equilibrium states (equivalently steady states). This case study illustrates the use of the algebraic solver by applying it to a problem in pharmacometrics. In particular, I show the algebraic system we solve can be quite complex and embed, for instance, numerical solutions to ordinary differential equations. The code in R and Stan are provided, and a Bayesian model is fitted to simulated data},
+  file = {/Users/tedgro/Zotero/storage/5XCZ7KVA/Margossian - 2018 - Computing steady states with Stan's nonlinear alge.pdf}
+}

docs/inputting.md

@@ -33,7 +33,8 @@ The following optional fields can also be specified:
 
 * `reject_non_steady` Boolean saying whether to reject draws that enter non-steady states
 * `penalize_non_steady` Boolean saying whether to penalize steady state deviations in the likelihood. It cannot be `True` when `reject_non_steady` is `True`.
-* `ode_config` Table of configuration options for Stan's ode solver
+* `ode_solver_config` Table of configuration options for Stan's ode solver
+* `algebra_solver_config` Table of configuration options for Stan's algebra solver
 * `cmdstanpy_config` Table of keyword arguments to the cmdstanpy method [`CmdStanModel.sample](https://cmdstanpy.readthedocs.io/en/v1.1.0/api.html#cmdstanpy.CmdStanModel.sample)
 * `cmdstanpy_config_predict` Table of overriding sample keyword argments for predictions
 * `stanc_options` Table of valid choices for [CmdStanModel](https://cmdstanpy.readthedocs.io/en/v1.1.0/api.html#cmdstanpy.CmdStanModel) argument `stanc_options`
@@ -68,11 +69,15 @@ Here is an example configuration file:
     chains = 4
     save_warmup = true
 
-    [ode_config]
+    [ode_solver_config]
+    abs_tol = 1e-4
+    rel_tol = 1e-4
+    max_num_steps = 1e6
+
+    [algebra_solver_config]
     abs_tol = 1e-4
     rel_tol = 1e-4
     max_num_steps = 1e6
-    timepoint = 1e3
 
 This file tells Maud that a file representing a kinetic model can be found at
 the relative path `kinetic_model.toml`, and that priors and experimental

docs/theory.md

@@ -226,9 +226,8 @@ solving [](#eq-steady).
 
 :::{note}
 
-Note that in practice we do not solve [](#eq-steady) directly but instead use
-ODE simulation - see section {ref}`sec-solving-the-steady-state-problem` for
-details
+See section {ref}`sec-solving-the-steady-state-problem` for how we solve
+[](#eq-steady) in practice.
 
 :::
 
@@ -337,16 +336,19 @@ adaptive Hamiltonian Monte Carlo requires gradients of the posterior
 distribution, it is also necessary to calculate sensitivities of the steady
 state solution with respect to all parameters.
 
-Our approach to this problem is to choose a starting concentration vector
-$x_{0}$ and a simulation time $t$, then find $x_t$ using numerical ODE
-integration. To verify whether $x_t$ is a steady state we then evaluate $S\cdot
-f_v(x_t, \theta)$ and check if the result is sufficiently close to zero.
-
-Stan provides an interface to the Sundials ODE solver CVODES, including
-gradient calculations.
-
-For the systems we have investigated, this method works better than solving the
-steady state problem using an algebra solver.
+Our approach to this problem is, before MCMC sampling, to choose a
+concentration vector $x_{guess}$. Every time Maud's sampler evaluates the
+statistical model's log probability and gradients, it numerically solves
+[](#eq-steady), and finds its gradients, using this starting guess and Stan's
+interface to the Sundials Newton solver IDAS.
+
+The system function that is passed to IDAS includes a representation of
+[](#eq-steady) in Stan code. In order to model the instantaneous change of
+metabolite concentrations for a given concentration vector due to
+[](#eq-steady), Maud uses the Stan interface to the Sundials ODE solver CVODES
+to evolve the system. This hybrid approach, using both numerical ODE solving
+and algebra solving, follows the one taken in
+{cite}`margossianComputingSteadyStates2018`.
 
 It is relevant to note a caveat for the Maximum A Posteriori estimation
 (optimization) in opposition to sampling the full distribution. In an

maud/data/example_inputs/example_ode/config.toml

@@ -9,6 +9,5 @@ iter_warmup = 0
 iter_sampling = 1
 chains = 1
 
-[ode_config]
+[ode_solver_config]
 max_num_steps = 1e9
-timepoint = 1e3

maud/data/example_inputs/example_ode/input_data_train.json

@@ -1 +1 @@
-{"N_mic": 4, "N_edge_sub": 5, "N_edge_prod": 5, "N_edge": 5, "N_unbalanced": 2, "N_enzyme": 5, "N_phosphorylation": 0, "N_pme": 0, "N_competitive_inhibition": 1, "N_allostery": 2, "N_allosteric_enzyme": 2, "N_drain": 0, "N_km": 10, "N_sub_km": 5, "N_prod_km": 5, "S": [[-1, -1, -1, 0, 0], [1, 0, 0, -1, 0], [0, 1, 1, 0, -1], [0, 0, 0, 1, 1]], "N_reaction": 4, "N_metabolite": 4, "balanced_mic_ix": [2, 3], "unbalanced_mic_ix": [1, 4], "ci_mic_ix": [4], "edge_type": [1, 1, 1, 1, 1], "edge_to_enzyme": [1, 2, 3, 4, 5], "edge_to_tc": [0, 1, 2, 0, 0], "edge_to_drain": [0, 0, 0, 0, 0], "edge_to_reaction": [1, 2, 2, 3, 4], "water_stoichiometry": [0.0, 0.0, 0.0, 0.0, 0.0], "transported_charge": [0.0, 0.0, 0.0, 0.0, 0.0], "mic_to_met": [1, 2, 3, 4], "subunits": [1, 1, 1, 1, 1], "sub_by_edge_long": [1, 1, 1, 2, 3], "sub_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "prod_by_edge_long": [2, 3, 3, 4, 4], "prod_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "sub_km_ix_by_edge_long": [1, 3, 5, 7, 9], "sub_km_ix_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "prod_km_ix_by_edge_long": [2, 4, 6, 8, 10], "prod_km_ix_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "ci_ix_long": [1], "ci_ix_bounds": [[1, 1], [2, 1], [2, 1], [2, 1], [2, 1]], "allostery_ix_long": [1, 2], "allostery_ix_bounds": [[1, 0], [1, 1], [2, 2], [3, 2], [3, 2]], "allostery_type": [1, 2], "allostery_mic": [3, 3], "phosphorylation_ix_long": [], "phosphorylation_ix_bounds": [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0]], "phosphorylation_type": [], "phosphorylation_pme": [], "priors_km": [[-0.6931471805599453, 0.0, 0.6931471805599453, -0.6931471805599453, 0.0, 0.6931471805599453, -0.6931471805599453, 0.6931471805599453, 0.0, 1.0986122886681098], [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]], "priors_ki": [[0.0], [0.2]], "priors_kcat": [[-0.6931471805599453, 0.6931471805599453, 0.0, 0.6931471805599453, 0.0], [0.2, 0.2, 0.2, 0.2, 0.2]], "priors_dissociation_constant": [[-1.2039728043259361, -0.10536051565782628], [0.2, 0.2]], "priors_transfer_constant": [[0.0, 0.0], [0.2, 0.2]], "priors_kcat_pme": [[], []], "priors_drain_train": [[[]], [[]]], "priors_drain_test": [[[]], [[]]], "priors_conc_enzyme_train": [[[0.0, 0.6931471805599453, 1.0986122886681098, 0.6931471805599453, 1.0986122886681098]], [[0.2, 0.2, 0.2, 0.2, 0.2]]], "priors_conc_enzyme_test": [[[0.0, 0.6931471805599453, 1.0986122886681098, 0.6931471805599453, 1.0986122886681098]], [[0.2, 0.2, 0.2, 0.2, 0.2]]], "priors_conc_unbalanced_train": [[[1.6094379124341003, -0.6931471805599453]], [[0.2, 0.2]]], "priors_conc_unbalanced_test": [[[1.6094379124341003, -0.6931471805599453]], [[0.2, 0.2]]], "priors_conc_pme_train": [[[]], [[]]], "priors_conc_pme_test": [[[]], [[]]], "priors_psi_train": [[0.0], [2.0]], "prior_loc_dgf": [10.0, 2.0, 5.0, 0.0], "prior_cov_dgf": [[0.2, 0.0, 0.0, 0.0], [0.0, 0.2, 0.0, 0.0], [0.0, 0.0, 0.2, 0.0], [0.0, 0.0, 0.0, 0.2]], "N_experiment_train": 1, "N_flux_measurement_train": 2, "N_enzyme_measurement_train": 0, "N_conc_measurement_train": 2, "N_enzyme_knockout_train": 0, "N_pme_knockout_train": 0, "temperature_train": [298.15], "enzyme_knockout_train_long": [], "enzyme_knockout_train_bounds": [[1, 0]], "pme_knockout_train_long": [], "pme_knockout_train_bounds": [[1, 0]], "yconc_train": [0.323117, 3.02187], "sigma_yconc_train": [0.1, 0.1], "experiment_yconc_train": [1, 1], "mic_ix_yconc_train": [2, 3], "yflux_train": [0.421816, 2.11674], "sigma_yflux_train": [0.01, 0.01], "experiment_yflux_train": [1, 1], "reaction_yflux_train": [3, 4], "yenz_train": [], "sigma_yenz_train": [], "experiment_yenz_train": [], "enzyme_yenz_train": [], "likelihood": 1, "drain_small_conc_corrector": 1e-06, "reject_non_steady": 1, "penalize_non_steady": 0, "steady_state_threshold_abs": 1e-08, "steady_state_threshold_rel": 0.001, "steady_state_penalty_rel": 1e-08, "rel_tol": 1e-09, "abs_tol": 1e-09, "timepoint": 1000.0, "max_num_steps": 1000000000, "conc_init": [[0.323117, 3.02187]]}
+{"N_mic": 4, "N_edge_sub": 5, "N_edge_prod": 5, "N_edge": 5, "N_unbalanced": 2, "N_enzyme": 5, "N_phosphorylation": 0, "N_pme": 0, "N_competitive_inhibition": 1, "N_allostery": 2, "N_allosteric_enzyme": 2, "N_drain": 0, "N_km": 10, "N_sub_km": 5, "N_prod_km": 5, "S": [[-1, -1, -1, 0, 0], [1, 0, 0, -1, 0], [0, 1, 1, 0, -1], [0, 0, 0, 1, 1]], "N_reaction": 4, "N_metabolite": 4, "balanced_mic_ix": [2, 3], "unbalanced_mic_ix": [1, 4], "ci_mic_ix": [4], "edge_type": [1, 1, 1, 1, 1], "edge_to_enzyme": [1, 2, 3, 4, 5], "edge_to_tc": [0, 1, 2, 0, 0], "edge_to_drain": [0, 0, 0, 0, 0], "edge_to_reaction": [1, 2, 2, 3, 4], "water_stoichiometry": [0.0, 0.0, 0.0, 0.0, 0.0], "transported_charge": [0.0, 0.0, 0.0, 0.0, 0.0], "mic_to_met": [1, 2, 3, 4], "subunits": [1, 1, 1, 1, 1], "sub_by_edge_long": [1, 1, 1, 2, 3], "sub_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "prod_by_edge_long": [2, 3, 3, 4, 4], "prod_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "sub_km_ix_by_edge_long": [1, 3, 5, 7, 9], "sub_km_ix_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "prod_km_ix_by_edge_long": [2, 4, 6, 8, 10], "prod_km_ix_by_edge_bounds": [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], "ci_ix_long": [1], "ci_ix_bounds": [[1, 1], [2, 1], [2, 1], [2, 1], [2, 1]], "allostery_ix_long": [1, 2], "allostery_ix_bounds": [[1, 0], [1, 1], [2, 2], [3, 2], [3, 2]], "allostery_type": [1, 2], "allostery_mic": [3, 3], "phosphorylation_ix_long": [], "phosphorylation_ix_bounds": [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0]], "phosphorylation_type": [], "phosphorylation_pme": [], "priors_km": [[-0.6931471805599453, 0.0, 0.6931471805599453, -0.6931471805599453, 0.0, 0.6931471805599453, -0.6931471805599453, 0.6931471805599453, 0.0, 1.0986122886681098], [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]], "priors_ki": [[0.0], [0.2]], "priors_kcat": [[-0.6931471805599453, 0.6931471805599453, 0.0, 0.6931471805599453, 0.0], [0.2, 0.2, 0.2, 0.2, 0.2]], "priors_dissociation_constant": [[-1.2039728043259361, -0.10536051565782628], [0.2, 0.2]], "priors_transfer_constant": [[0.0, 0.0], [0.2, 0.2]], "priors_kcat_pme": [[], []], "priors_drain_train": [[[]], [[]]], "priors_conc_enzyme_train": [[[0.0, 0.6931471805599453, 1.0986122886681098, 0.6931471805599453, 1.0986122886681098]], [[0.2, 0.2, 0.2, 0.2, 0.2]]], "priors_conc_unbalanced_train": [[[1.6094379124341003, -0.6931471805599453]], [[0.2, 0.2]]], "priors_conc_pme_train": [[[]], [[]]], "priors_psi_train": [[0.0], [2.0]], "prior_loc_dgf": [10.0, 2.0, 5.0, 0.0], "prior_cov_dgf": [[0.2, 0.0, 0.0, 0.0], [0.0, 0.2, 0.0, 0.0], [0.0, 0.0, 0.2, 0.0], [0.0, 0.0, 0.0, 0.2]], "N_experiment_train": 1, "N_flux_measurement_train": 2, "N_enzyme_measurement_train": 0, "N_conc_measurement_train": 2, "N_enzyme_knockout_train": 0, "N_pme_knockout_train": 0, "temperature_train": [298.15], "enzyme_knockout_train_long": [], "enzyme_knockout_train_bounds": [[1, 0]], "pme_knockout_train_long": [], "pme_knockout_train_bounds": [[1, 0]], "yconc_train": [0.323117, 3.02187], "sigma_yconc_train": [0.1, 0.1], "experiment_yconc_train": [1, 1], "mic_ix_yconc_train": [2, 3], "yflux_train": [0.421816, 2.11674], "sigma_yflux_train": [0.01, 0.01], "experiment_yflux_train": [1, 1], "reaction_yflux_train": [3, 4], "yenz_train": [], "sigma_yenz_train": [], "experiment_yenz_train": [], "enzyme_yenz_train": [], "likelihood": 1, "drain_small_conc_corrector": 1e-06, "reject_non_steady": 1, "penalize_non_steady": 0, "steady_state_threshold_abs": 1e-08, "steady_state_threshold_rel": 0.001, "steady_state_penalty_rel": 1e-08, "rel_tol_ode": 1e-09, "abs_tol_ode": 1e-09, "max_num_steps_ode": 1000000000, "rel_tol_alg": 1e-07, "abs_tol_alg": 1e-07, "max_num_steps_alg": 1000000, "conc_init": [[0.323117, 3.02187]]}

maud/data/example_inputs/linear/config.toml

@@ -13,8 +13,12 @@ chains = 4
 save_warmup = true
 seed = 1234
 
-[ode_config]
+[ode_solver_config]
+abs_tol = 1e-7
+rel_tol = 1e-7
+max_num_steps = 1e6
+
+[algebra_solver_config]
 abs_tol = 1e-5
 rel_tol = 1e-5
 max_num_steps = 1e6
-timepoint = 1e3

maud/data/example_inputs/linear/expected_stan_input.json

@@ -1 +1 @@
-{"N_mic": 4, "N_edge_sub": 3, "N_edge_prod": 3, "N_edge": 3, "N_unbalanced": 2, "N_enzyme": 3, "N_phosphorylation": 0, "N_pme": 0, "N_competitive_inhibition": 1, "N_allostery": 2, "N_allosteric_enzyme": 2, "N_drain": 0, "N_km": 5, "N_sub_km": 3, "N_prod_km": 2, "S": [[-1, 0, 0], [1, -1, 0], [0, 1, -1], [0, 0, 1]], "N_reaction": 3, "N_metabolite": 2, "balanced_mic_ix": [2, 3], "unbalanced_mic_ix": [1, 4], "ci_mic_ix": [2], "edge_type": [1, 2, 1], "edge_to_enzyme": [1, 2, 3], "edge_to_tc": [1, 2, 0], "edge_to_drain": [0, 0, 0], "edge_to_reaction": [1, 2, 3], "water_stoichiometry": [0.0, 0.0, 0.0], "transported_charge": [0.0, 0.0, 1.0], "mic_to_met": [1, 1, 2, 2], "subunits": [1, 1, 1], "sub_by_edge_long": [1, 2, 3], "sub_by_edge_bounds": [[1, 1], [2, 2], [3, 3]], "prod_by_edge_long": [2, 3, 4], "prod_by_edge_bounds": [[1, 1], [2, 2], [3, 3]], "sub_km_ix_by_edge_long": [1, 3, 4], "sub_km_ix_by_edge_bounds": [[1, 1], [2, 2], [3, 3]], "prod_km_ix_by_edge_long": [2, 5], "prod_km_ix_by_edge_bounds": [[1, 1], [2, 1], [2, 2]], "ci_ix_long": [1], "ci_ix_bounds": [[1, 0], [1, 1], [2, 1]], "allostery_ix_long": [1, 2], "allostery_ix_bounds": [[1, 1], [2, 2], [3, 2]], "allostery_type": [1, 2], "allostery_mic": [3, 2], "phosphorylation_ix_long": [], "phosphorylation_ix_bounds": [[1, 0], [1, 0], [1, 0]], "phosphorylation_type": [], "phosphorylation_pme": [], "priors_km": [[0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 0.6, 0.6, 0.6, 0.6]], "priors_ki": [[0.0], [0.6]], "priors_kcat": [[0.0, 0.0, 0.0], [0.6, 0.6, 0.6]], "priors_dissociation_constant": [[0.0, 0.0], [0.6, 0.6]], "priors_transfer_constant": [[0.0, 0.0], [0.6, 0.6]], "priors_kcat_pme": [[], []], "priors_drain_train": [[[]], [[]]], "priors_conc_enzyme_train": [[[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]], "priors_conc_unbalanced_train": [[[-2.3, -2.3], [-2.3, -2.3]], [[2.0, 2.0], [2.0, 2.0]]], "priors_conc_pme_train": [[[]], [[]]], "priors_psi_train": [[-0.95, -0.95], [0.2, 0.2]], "prior_loc_dgf": [-1.0, -2.0], "prior_cov_dgf": [[0.05, 0.0], [0.0, 0.05]], "N_experiment_train": 2, "N_flux_measurement_train": 2, "N_enzyme_measurement_train": 6, "N_conc_measurement_train": 7, "N_enzyme_knockout_train": 0, "N_pme_knockout_train": 0, "temperature_train": [299.0, 298.15], "enzyme_knockout_train_long": [], "enzyme_knockout_train_bounds": [[1, 0], [1, 0]], "pme_knockout_train_long": [], "pme_knockout_train_bounds": [[1, 0], [1, 0]], "yconc_train": [0.59, 1.09, 1.05, 0.54, 0.38, 1.12, 1.14], "sigma_yconc_train": [0.1, 0.05, 0.05, 0.1, 0.1, 0.05, 0.05], "experiment_yconc_train": [1, 1, 1, 2, 2, 2, 2], "mic_ix_yconc_train": [2, 1, 4, 2, 3, 1, 4], "yflux_train": [0.19, 0.39], "sigma_yflux_train": [0.1, 0.1], "experiment_yflux_train": [1, 2], "reaction_yflux_train": [3, 3], "yenz_train": [1.5, 1.5, 1.5, 1.5, 1.5, 1.5], "sigma_yenz_train": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], "experiment_yenz_train": [1, 1, 1, 2, 2, 2], "enzyme_yenz_train": [1, 2, 3, 1, 2, 3], "likelihood": 1, "drain_small_conc_corrector": 1e-06, "reject_non_steady": 1, "penalize_non_steady": 0, "steady_state_threshold_abs": 1e-06, "steady_state_penalty_rel": 1e-08, "steady_state_threshold_rel": 0.001, "rel_tol": 1e-05, "abs_tol": 1e-05, "timepoint": 1000.0, "max_num_steps": 1000000, "conc_init": [[0.59, 0.38], [0.54, 0.38]]}
+{"N_mic": 4, "N_edge_sub": 3, "N_edge_prod": 3, "N_edge": 3, "N_unbalanced": 2, "N_enzyme": 3, "N_phosphorylation": 0, "N_pme": 0, "N_competitive_inhibition": 1, "N_allostery": 2, "N_allosteric_enzyme": 2, "N_drain": 0, "N_km": 5, "N_sub_km": 3, "N_prod_km": 2, "S": [[-1, 0, 0], [1, -1, 0], [0, 1, -1], [0, 0, 1]], "N_reaction": 3, "N_metabolite": 2, "balanced_mic_ix": [2, 3], "unbalanced_mic_ix": [1, 4], "ci_mic_ix": [2], "edge_type": [1, 2, 1], "edge_to_enzyme": [1, 2, 3], "edge_to_tc": [1, 2, 0], "edge_to_drain": [0, 0, 0], "edge_to_reaction": [1, 2, 3], "water_stoichiometry": [0.0, 0.0, 0.0], "transported_charge": [0.0, 0.0, 1.0], "mic_to_met": [1, 1, 2, 2], "subunits": [1, 1, 1], "sub_by_edge_long": [1, 2, 3], "sub_by_edge_bounds": [[1, 1], [2, 2], [3, 3]], "prod_by_edge_long": [2, 3, 4], "prod_by_edge_bounds": [[1, 1], [2, 2], [3, 3]], "sub_km_ix_by_edge_long": [1, 3, 4], "sub_km_ix_by_edge_bounds": [[1, 1], [2, 2], [3, 3]], "prod_km_ix_by_edge_long": [2, 5], "prod_km_ix_by_edge_bounds": [[1, 1], [2, 1], [2, 2]], "ci_ix_long": [1], "ci_ix_bounds": [[1, 0], [1, 1], [2, 1]], "allostery_ix_long": [1, 2], "allostery_ix_bounds": [[1, 1], [2, 2], [3, 2]], "allostery_type": [1, 2], "allostery_mic": [3, 2], "phosphorylation_ix_long": [], "phosphorylation_ix_bounds": [[1, 0], [1, 0], [1, 0]], "phosphorylation_type": [], "phosphorylation_pme": [], "priors_km": [[0.0, 0.0, 0.0, 0.0, 0.0], [0.6, 0.6, 0.6, 0.6, 0.6]], "priors_ki": [[0.0], [0.6]], "priors_kcat": [[0.0, 0.0, 0.0], [0.6, 0.6, 0.6]], "priors_dissociation_constant": [[0.0, 0.0], [0.6, 0.6]], "priors_transfer_constant": [[0.0, 0.0], [0.6, 0.6]], "priors_kcat_pme": [[], []], "priors_drain_train": [[[]], [[]]], "priors_conc_enzyme_train": [[[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]], "priors_conc_unbalanced_train": [[[-2.3, -2.3], [-2.3, -2.3]], [[2.0, 2.0], [2.0, 2.0]]], "priors_conc_pme_train": [[[]], [[]]], "priors_psi_train": [[-0.95, -0.95], [0.2, 0.2]], "prior_loc_dgf": [-1.0, -2.0], "prior_cov_dgf": [[0.05, 0.0], [0.0, 0.05]], "N_experiment_train": 2, "N_flux_measurement_train": 2, "N_enzyme_measurement_train": 6, "N_conc_measurement_train": 7, "N_enzyme_knockout_train": 0, "N_pme_knockout_train": 0, "temperature_train": [299.0, 298.15], "enzyme_knockout_train_long": [], "enzyme_knockout_train_bounds": [[1, 0], [1, 0]], "pme_knockout_train_long": [], "pme_knockout_train_bounds": [[1, 0], [1, 0]], "yconc_train": [0.59, 1.09, 1.05, 0.54, 0.38, 1.12, 1.14], "sigma_yconc_train": [0.1, 0.05, 0.05, 0.1, 0.1, 0.05, 0.05], "experiment_yconc_train": [1, 1, 1, 2, 2, 2, 2], "mic_ix_yconc_train": [2, 1, 4, 2, 3, 1, 4], "yflux_train": [0.19, 0.39], "sigma_yflux_train": [0.1, 0.1], "experiment_yflux_train": [1, 2], "reaction_yflux_train": [3, 3], "yenz_train": [1.5, 1.5, 1.5, 1.5, 1.5, 1.5], "sigma_yenz_train": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], "experiment_yenz_train": [1, 1, 1, 2, 2, 2], "enzyme_yenz_train": [1, 2, 3, 1, 2, 3], "likelihood": 1, "drain_small_conc_corrector": 1e-06, "reject_non_steady": 1, "penalize_non_steady": 0, "steady_state_threshold_abs": 1e-06, "steady_state_threshold_rel": 0.001, "steady_state_penalty_rel": 1e-08, "rel_tol_ode": 1e-07, "abs_tol_ode": 1e-07, "max_num_steps_ode": 1000000, "rel_tol_alg": 1e-05, "abs_tol_alg": 1e-05, "max_num_steps_alg": 1000000, "conc_init": [[0.59, 0.38], [0.54, 0.38]]}

maud/data/example_inputs/methionine/config.toml

@@ -9,15 +9,19 @@ reject_non_steady = true
 [cmdstanpy_config]
 iter_warmup = 1000
 iter_sampling = 1000
-max_treedepth = 11
+max_treedepth = 10
 chains = 4
 save_warmup = true
 refresh = 1
 metric = "dense_e"
 adapt_delta = 0.99
 
-[ode_config]
-rel_tol = 1e-9
-abs_tol = 1e-9
+[ode_solver_config]
+rel_tol = 1e-8
+abs_tol = 1e-8
 max_num_steps = 1000000.0
-timepoint = 1e+20
+
+[algebra_solver_config]
+rel_tol = 1e-6
+abs_tol = 1e-6
+max_num_steps = 1000000