SimBiology

When a MassAction rate parameter (e.g., `ke`) is set by an initial assignment rule, `createSimFunction` does not use the computed value for the MassAction rate calculation. The parameter IS correctly computed (confirmed by observing it as a SimFunction output), but MassAction uses the pre-assignment `.Value` property instead. `sbiosimulate` with the same model produces correct results. Environment - MATLAB R2025b (25.2.0.3123386) - SimBiology toolbox model = sbiomodel("MWE"); cs = getconfigset(model); cs.SolverType = 'sundials'; cs.SolverOptions.AbsoluteTolerance = 1e-8; cs.SolverOptions.RelativeTolerance = 1e-8; cs.CompileOptions.DimensionalAnalysis = true; cs.CompileOptions.UnitConversion = true; addcompartment(model, 'Central', ... 'CapacityUnits', 'milliliter', 'ConstantCapacity', false); addspecies(model.Compartments(1), 'Drug', 0, 'Units', 'milligram/milliliter'); addparameter(model, 'typical_V', 3000, 'Units', 'milliliter'); addparameter(model, 'typical_CL', 200, 'Units', 'milliliter/hour'); addparameter(model, 'BW', 70, 'Units', 'kilogram'); addparameter(model, 'typical_BW', 70, 'Units', 'kilogram'); addparameter(model, 'CL', eps, 'Units', 'milliliter/hour'); addparameter(model, 'ke', eps, 'Units', '1/hour'); % Initial assignments: % Central = 3000 mL, CL = 200 mL/h, ke = 200/3000 = 0.0667 /h addrule(model, 'Central = typical_V * (BW / typical_BW)', 'initialAssignment'); addrule(model, 'CL = typical_CL * (BW / typical_BW)', 'initialAssignment'); addrule(model, 'ke = CL / Central', 'initialAssignment'); % MassAction elimination (first-order) rxn = addreaction(model, 'Central.Drug -> null', 'Name', 'Elimination'); kl = addkineticlaw(rxn, 'MassAction'); kl.ParameterVariableNames = 'ke'; % Dose dose = sbiodose('IV', 'schedule'); dose.TargetName = 'Drug'; dose.Amount = 300; dose.AmountUnits = 'milligram'; dose.TimeUnits = 'hour'; dose.Time = 0; % --- Method 1: createSimFunction (INCORRECT) --- F = createSimFunction(model, {}, {'Drug', 'ke', 'CL', 'Central'}, {'Central.Drug'}, ... 'UseParallel', false, 'AutoAccelerate', false); [~, y] = F([], [], getTable(dose), [0, 10, 100]); fprintf('createSimFunction:\n'); fprintf(' Drug(t=0)=%g, Drug(t=10h)=%g, Drug(t=100h)=%g\n', y{1}(1,1), y{1}(2,1), y{1}(3,1)); fprintf(' ke=%g, CL=%g, Central=%g (all correct!)\n\n', y{1}(1,2), y{1}(1,3), y{1}(1,4)); % --- Method 2: sbiosimulate (CORRECT) --- cs.StopTime = 100; cs.TimeUnits = 'hour'; cs.SolverOptions.OutputTimes = [0, 10, 100]; [~, x, names] = sbiosimulate(model, cs, [], dose); idx = strcmp(names, "Drug"); fprintf('sbiosimulate:\n'); fprintf(' Drug(t=0)=%g, Drug(t=10h)=%g, Drug(t=100h)=%g\n', x(1,idx), x(2,idx), x(3,idx)); Output createSimFunction: Drug(t=0)=0.1, Drug(t=10h)=0.0999815, Drug(t=100h)=0.099815 ke=0.0666667, CL=200, Central=3000 (all correct!) sbiosimulate: Drug(t=0)=0.1, Drug(t=10h)=0.0513417, Drug(t=100h)=0.000127269 Expected Behavior Both methods should produce identical results. Expected half-life = ln(2) / 0.0667 = 10.4 hours. `sbiosimulate` is correct; `createSimFunction` shows ~1000x slower elimination. Key Observation The initial assignments ARE evaluated correctly in `createSimFunction` — `ke=0.0667`, `CL=200`, `Central=3000` are all reported with correct values when observed as SimFunction outputs. However, the MassAction rate calculation does not use the computed `ke` value; it appears to use the pre-assignment `.Value` (eps). Notes - This pattern (`ke = CL/V` as initial assignment, MassAction with `ke`) is the same pattern used by SimBiology's own `PKCompartment.m` source code for "linear-clearance" elimination (lines 132-142 in R2025b). - The issue is not specific to chained initial assignments — even `ke = typical_CL / typical_V` (single-level, depending only on directly-set parameters) exhibits the same behavior. - The `Constant` property of the parameters does not affect the outcome. - `AutoAccelerate = true` does not fix the issue.

SimBiology project file for a published PK-PD model for degrader-antibody conjugates

Hello, When generating SimBiology scenarios, is it possible for a given scenario to have multiple doses or variants, or must each scenario have at most one dose and at most one variant? Thank you, Abed

I’m interested in performing a non-linear mixed effect estimation of a compartmental model. Besides the four parameters to be estimated, the model has two parameters which need to be fixed and which assume a different value for each subject in the dataset. Is there any way to change the value of the fixed parameter for each subject in the dataset? Thank you!

I am using simbiology to run my code and its been working fine. However, periodically I would have errors with sbiosaveproject. When I get errors, I can dive into the sbiosaveproject and see that its doing a try and catch on the sbiogate function (this code is taken from line 130-160 of sbiosaveproject: errorOccurred = false; try filesToZip = {matFileName, manifestFileName}; sbiogate('privateadddesktopfilestoproject', projfilename_new, filesToZip); catch errorOccurred = true; end If I debug and stop it at that function and run it in the command line, i get this error: K>> sbiogate('privateadddesktopfilestoproject', projfilename_new, filesToZip) Unrecognized function or variable 'cleanupDirectories'. Error in privateadddesktopfilestoproject Error in sbiogate (line 22) feval(varargin{:}); ^^^^^^^^^^^^^^^^^^^ If I do a which on cleanupDirectories, I get this: K>> which cleanupDirectories 'cleanupDirectories' not found. I have tried to delete all forms of MatLab and redownload. I have read and write access to all the temp folders. I have tried to specifically manually delete and reinstall simbiology. Nothing works to get the sbiosaveproject code to run again.

I fit some data and create a variant with the results. Then I duplicate the program of the fitting, and change the configuration into a group simulation, and added the new variant. The simulation is different in the group simulation, than the result of the fitting. I do not know why this is happening. If I create another program with only simulation, without data, the plot is the same as in the fitting. Is this a bug?

Automating Parameter Identifiability Analysis in SimBiology Is it possible to develop a MATLAB Live Script that automates a series of SimBiology model fits to obtain likelihood profiles? The goal is to fit a kinetic model to experimental data while systematically fixing the value of one kinetic constant (e.g., k1) and leaving the others unrestricted. The script would perform the following: Use a pre-configured SimBiology project where the best fit to the experimental data has already been established (including dependent/independent variables, covariates, the error model, and optimization settings). Iterate over a defined sequence of fixed values for a chosen parameter. For each fixed value, run the estimation to optimize the remaining parameters. Record the resulting Sum of Squared Errors (SSE) for each run. The final output would be a likelihood profile—a plot of SSE versus the fixed parameter value (e.g., k1)—to assess the practical identifiability of each model parameter. Automating Parameter Identifiability Analysis in SimBiology Hi @Luis B. Walter, what you are describing is already implemented in sbioparameterci, which is also the method used when you add a confidence interval step to a fit program in the Model Analyzer app. Best regards, Jérémy Thank you very much, Jérémy: Certainly, Simbiology has implemented this operation when calculating parameter confidence intervals if the Profile Likelihood Confidence Interval Calculation option is used. Now, it seems to be implemented to obtain confidence intervals rather than to establish the identifiability of a model's parameters—despite being the same calculation. I imagine that the color of the bar graphs indicating the statuses (success, constrained, or estimable) are related to the identifiability of the parameters, but this is not clear in the Simbiology User's Manual (where can I find out more about this?). I believe—and it is widely accepted in new enzymology—that obtaining confidence intervals to assess the quality of fit results is important only after establishing the identifiability of a model's parameters. Identifiability is a structural property of the model and the data that allows us to understand the information contained in the experimental data. Unfortunately, Simbiology does not save or print (or at least I couldn't find a way) the pairs of SSE values vs. parameters (pi) that it calculates when sweeping the values of each parameter, nor is it possible to define and/or change the sweep intervals. Access to this data would enable us to construct the 1D confidence contours, like those shown in attached sheet (A), which are important for assigning kinetic models to experimental data. However, this would not be enough to construct the 2D confidence contours (B) in the attached sheet, which would complement the characterization of parameter behavior. My question was aimed at finding an automated procedure to obtain the data to build the graphs (A) and (B) (in the attach), having carried out the fitting of the model to experimental data from the Simbiology GUI. I have begun this task but am encountering significant difficulties. I estimate that if I could trigger the adjustments in Simbiology from a live script and define which parameters and at what intervals they should be set in each optimization and retrieve the SSE values, the objective would be solved. What do you think?. Did I present any interpretation or observation above that you consider erroneous about this proposal? What would you recommend me to do? best regards Luis B. Hi @Luis B. Walter, SimBiology’s “Profile Likelihood Confidence Interval Calculation” computes profile likelihoods (PLL) by sweeping each parameter and re-optimizing others. It retrieves the Loglikelihood values (Likelihood profiles) instead of SSE but the curves will be the same. In your case the SSE are also normalized but again it won't change the Confidence intervals, and thus the results of the practical identifiability analysis. Status colors: “Estimable” ≈ locally identifiable (practical identifiability). If the confidence interval of the parameter estimate is unbounded, then it is set to be not estimable. The box plot you see as Confidence Interval plot is the default for all calculation method. However, PLL has a dedicated plot that looks exactly like the 1D confidence contour in your PDF: Built-in plot of profile-likelihood curves: https://www.mathworks.com/help/simbio/ref/simbiology.fit.parameterconfidenceinterval.plot.html You can switch from a box plot to a PLL plot in the Model Analyzer App in the Plot Settings: You can also define a MaxStepSize for the parameter sweep. But the exact value is determined by SimBiology depending on the current Loglikelihood change. Regarding the 2D confidence contour, assuming that the results are obtained by fixing 2 parameters and estimating the remaining ones, you are right that this is not built-in. However, your suggestion seems reasonable. You can indeed write a script to loop over the fixed parameter values and estimate the remaining ones. I recommend generating the fitting code from your fit program (right-click on program in the Model Analyzer Browser and 'View program code') and adapt it for this task. I hope this helps. Best regards, Jérémy Excellent answer, Jérémy. Very, very useful and descriptive. I'll try the live script as you recommended, and I'll contact you again if I need it. Thank you very much. best Luis B. simbiology likelihood profiles

When doing parameter estimation with sbiofit and with categorical covariates, is it possible to specify separate initial estimates for the various covariates?

I built a simple drug and dose model, I try to use GSA and mpgsa method, but when I finished setting up and pressed run, it says that "unable to plot gsa empty result"

I am trying to run a sensitivity analysis where I vary one parameter for a data set/variants activated by orders of magnitude (say -5 to 5), and obtain the peak value for a species in order to compare the % change from the original (order of magnitude of 0). In principle, it makes sense in my head how to do this, but I don't know how to go about writing a custom code in symbiology.. Any help would strongly be appreciated. I have a program in symbiology for running multiple data set and single data set simulations, but not sure how to define a custom one like this.

I want to observe the time (Tmax) to reach maximum drug concentration (Cmax) in my model. I have set up the OBSERVABLES as follows (figure1): Cmax = max(Blood.lL15); Tmax_LT = time(Conc_lL15_LT_nm == max(Conc_lL15_LT_nm)); Tmax_Tm = time(Conc_lL15_Tumor_nm == max(Conc_lL15_Tumor_nm)); After running the Sobol indices program for global sensitivity analysis, with inputs being some parameters and their ranges, the output for Cmax works, but there are some prompts, as shown in figure2. Additionally, when outputting Tmax, the program does not run successfully and reports some errors, as shown in figure2. How can I resolve the errors when outputting Tmax? How to set a Tmax and successfully implement it in Simbiology? Hello, the error message indicates that model simulations failed. The problem is likely caused by SimBiology’s validation of observables. Observable expressions must always either return a scalar value or a vector of the same length as the time value. Expressions like Tmax = time(conc == max(conc)) can return a scalar, but they can also return a vector, i.e., if the concentration is constant. The case of conc being constant is probably not occurring during your analysis, but it is detected during the validation of the observable before model simulation. To work around this validation issue, you can create a function function tMax = calculateTmax(time, conc) tMax = time(conc == max(conc)); % This assumes that only the concentration assumes its max. at a % unique time tMax. tMax = tMax(1); end that calculates Tmax and ensures a single time is returned. Please safe this function in a script calculateTmax.m in a folder that is on the MATLAB path. You can either save the m-file in your current folder, or add the folder containing this file to the path. You will then be able to calculate the observable by specifying the expressions as follows: Tmax_LT = calculateTmax(time, LT.IL15) Tmax_Tm = calculateTmax(time, Tumor.IL15) I hope this helps. -Florian

Hi All, I need to use IDA solver in MATLAB but I am not able to use it. I have downloaded the installation folder which contains the SUNDIALS package but I do not know the further steps in order to be able to use IDA on MATLAB. Do you know how can I do? PS: I need to run it on my Mac, are there any problems of compatibility? Thank you so much in advance! Gabri

I am encountering a "MATLAB:UndefinedFunction" error with the message, "Undefined function handle" when running a MATLAB script in conjunction with a SimBiology program in MATLAB R2024b. The program performs a set of model fits to experimental data by submitting each model fit to a cluster using "parfeval". The cluster consists of the host computer (running a MATLAB SimBiology Model Analyzer interface and runs the program) and five additional compute nodes. This issue only occurs after the SimBiology program has been run at least once with the parallel pool generated by the cluster. How can I resolve this error? kA0UU0000003wNd0AI 000575480

Dear staff, I'm learning how to implement a TGI model in Simbiology using a scripting approach. For that purpose I started to implement my model (based on the example tumor_growth_fitPKPD_completed.sbproj) just with a modified growth component and I'm trying to fit a dataset. Attached you can find the dataset (synth_data.mat) and the TGI script (test_code.m). As you can see the initial tumor mass of different patients varies quite a lot so I can't use a unique initialization for the variable w0. I would like to understand if (and how) it is possible to pass to the model the initial tumor mass for each ID, in order to avoid to fit this parameter. Thank you very much for your help.

Hello, I have a simbiology PBPK model, to which I successfully fit several organ data (liver, kidneys, spleen, blood, ...) simulatenously using the proportional error model. I would like now to switch to numeric weights (1/SD²) for the error model to account for the experimental variability in the model, as I have concentrations and SDs data for each compartment/organ. My data is now formatted in a "wide" format: time points in rows, organ concentrations as columns. I've done some tests on a two-compartment model (GI + Central), based on the example bioavailabilty.sbproj file to remove complexity and enable quick testing of the approach: I managed to implement the numeric weights when fitting only one compartment, by adding a column SD (and even Weights) to the dataset (see files beginning with "fit1comp_"). However when fitting data of two compartments and keeping the wide format of the data, with then Conc_Central, Conc_GI, SD_Central and SD_GI columns, this approach doesn't seem to work because of the multi-column format of the Weights (see files beginning with "fit2comp_wide_"). I also tried to have data in a "long" format, with Time, Conc, SD and Group as columns, where the groups represent my compartment (central, GI). However when mapping the responses, I didn't manage to assign properly the Blood_Plasma.Drug_Central and GI_Compartment.Drug_Oral to the groups in the model analyzer application. The group-specific properties seem only to be variants and doses and not response. I understood from the documentation this groupping is mostly foreseen to map different individuals for the same type of response, so I am not sure how this can be implemented. Do you have any advice on how to implement the mutli-compartment fit with numeric weights? Thanks in advance! Best regards

Sometimes when I try to create and compile a SimFunction, the following error happens: Unable to create SimFunction: --> Error reported from ODE Compilation: Not enough input arguments. Caused by: Error using SimBiology.internal.compile --> Error reported from ODE Compilation: Not enough input arguments. The code file will run in other versions of MATLAB (e.g. I just got this error in R2023a, but it runs fine on R2022b).

I cannot seem to find ribbon buttons for undo/redo operations in SimBiology. In Matlab I find them in the "Quick Access Toolbar" - in fact I can change the toolbar layout through Matlab preferences. In the SimBiology desktop quick access toolbar there are no undo/redo buttons nor is there any option in the does not show undo/redo buttons nor does the SimBiology preferences allow for the customization of the quick access toolbar.

i doesnt respond to any commands related to simbiology and it gives a massage of missing contents.m file

Dear all, I am trying to build a PBPK model of a radioactive probe, and the proposed drug dose unit is Bq (becquerel), but the becquerel unit in current simbiology is not a drug dose category. The error is as follows：Quantity named Dose_Central references a unit that dose not exist or is not a valid unit. In addition, do you have any experience in building PBPK models of radioactive probes? If so, can you share it? Thanks！ Miya

I tried to open the SimBiology file via SimBiology MATLAB 2024b, which was created on 21 Mar by the version 2024b, but failed to open Model Analyzer while successfully opened Model Builder. Alert was popped up as attached in this inquery. This phenomenon has happened only recently. Also, it is reproduced several times. An old file I saved on 12 Mar can be opened without any issues.

I have some .SBPROJ files I created using MATLAB R2020a SimBiology App. When I attempt to open these saved project files using MATLAB R2019b SimBiology, the GUI freezes, and MATLAB has to be killed manually through Task Manager. How can I open SimBiology SBPROJ files using older releases of MATLAB? kA03q000001HpUZCA0 000150493

I am using the simbiology QSP model from H Wang et al., "Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer", iScience, 2022. The model includes a drug treatment that works fine with both the SUNDIALS and ODE15s solvers when the dose start time is set to day 0. However, when the dose start time is set to a value later than 0, I encounter the 'Integration Tolerance Not Met' error. The error appears earlier with SUNDIALS; for example, it occurs when the dose start time is set to 7 days. In contrast, the simulation runs without issues using ODE15s until the dose start time is set to 15 days. Surprisingly, with ODE15s, when I reduce the dose amount from 100 units to 20 units, the simulation runs fine with the delayed dose start time set to 40 days. However, if I further delay the dose start time to 50 days, the simulation fails again. Any help will be higly appreciated.

When I run the Simbiology debugger, it runs for a bit and then stops. The simulation is not run. I get the following output in the command line. >> startSimBiologyDebugger(model,cs,model_variant,doses_sim) The debugger is currently running. Stop debugging to unblock MATLAB command line. ans = 'Dimensions of arrays being concatenated are not consistent.' ans = struct with fields: file: '[insert my directory here]' name: 'Debugger.simulationCallback' line: 0 The debugger has stopped. MATLAB command line is unblocked.

Hello all, First, I used SimBiology Model Builder to restore the PPK model that had been established in NONMEM. Since SimBiology Model Builder cannot add parameters for inter-individual variation and error models, I added them using code. But I found that the exponential error model has not been added to the model. I would like to ask how to add the exponential error model to the following code and how to optimize the simulation process code? Thank you for your help! opts = spreadsheetImportOptions("NumVariables", 3); opts.Sheet = "Sheet1"; opts.DataRange = "A2:C237"; opts.VariableNames = ["ID", "TIME", "DV"]; opts.VariableTypes = ["double", "double", "double"]; Dose = readtable("Dose.xls", opts, "UseExcel", false) data = groupedData(Dose); sbiotrellis(data,@semilogy,"ID","TIME","DV",Marker="+",LineStyle="--");%%@semilogy % Extract the model overall model file s = sbioloadproject("Version-2-20241218.sbproj"); model = s.m1; covModel = CovariateModel; covModel.Expression = {'V1 = exp(theta1 + eta1)',' V2 = exp(theta2 + eta2)','F1 = exp(theta3 + eta3)','CL = exp(theta4 + eta4)'}; covModel.FixedEffectNames covModel.FixedEffectDescription options.ErrorModel = 'exponential'; options.ErrorModelParameters = struct('sigma', 1.0928) thetas = table(4.0056,-20.3634,-7.9054,7.0873,'VariableNames',["theta1","theta2","theta3","theta4"]) omega = table([2.4432;0;0;0],[0;0.0534;0;0],[0;0;9.4391;0],[0;0;0;0.0729],'VariableNames',["eta1","eta2","eta3","eta4"]) phi = sbiosampleparameters(covModel.Expression,thetas,omega,Dose) f = createSimFunction(model,covModel.ParameterNames,'Drug_Central',[]) simresults = f(phi,24) t = sbiotrellis(simresults,[],'Time',' Drug_Central'); t.hFig.Position(3:4) = [800 500]; set(t.plots,'XGrid','on','YGrid','on'); set(t.plots,'YScale','log'); lh = findobj(t.plots,'Type','line'); set(lh,'LineWidth',2);

Hello, I am having some trouble fitting a model to data using Simbiology. The error message seem to indicate simulation failure when using initial parameter estimates. But when I use exactly the same model, dose, Configset, and set parameters to be idential to the initial parameter estimate for fitting to run "sbiosimulate", the simulation finished without error. Could you please advise how to deal with this error. Thank you very much in advance! The error I got is attached below (there is a dose delay of 2000): Error using SimBiology.fit.internal.validateSimFunctionWithDoseInputs One or more simulations failed when using the initial values of estimated parameters. Check your model for errors or try different initial values for estimated parameters. Error in SimBiology.fit.internal.FitObject/fit (line 216) obj.SimFunctionDoseInputs = SimBiology.fit.internal.validateSimFunctionWithDoseInputs(obj.SimFunction, values, obj.Dosing, obj.OutputTimesCell, tfEmptyDose); Error in sbiofit (line 318) [varargout{1:nargout}] = fitObject.fit(modelObj, data, responseMap, estimInfo, varargin{:}); Error in fitproblem/fit (line 650) [varargout{1:max(1,nargout)}] = feval(argCell{:}); Caused by: Unable to complete simulation for input parameters [0.5 0.1 1e-09] because: CVODES returned -4 from module CVODES function CVode: At t = 2000 and h = 7.28377e-15, the corrector convergence test failed repeatedly or with |h| = hmin. Empty results have been returned for this simulation.