Variables

Model set-up (see annotations in the driver script)

Select pre-defined model

Model_Example	Choose model example
1	Pipeline example
2	PDGF model
3	CellNOpt example
4	Apoptosis model

Define optmisation options

Variable	Explanation
optRound	Number of optimisation round
MaxFunEvals	Number of maximal function being evaluated (3000=default)
MaxIter	Number of maximal iteration being evaluated (3000=default)
Parallelisation	Use multiple cores for optimisation? (0=no, 1=yes)
HLbound	Qualitative threshold between high and low inputs (0.5=default)
Forced=1	Define whether single inputs and Boolean gates are forced to probability 1 (0=no, 1=yes)
InitIC=2	Initialise parameters' distribution (1=uniform, 2=normal)

Define plotting and saving (0=no, 1=yes)

Variable	Explanation
PlotFitEvolution	Graph of optimised fitting cost over iteration
PlotFitSummary	Graph of state values at steady-state versus measurements (all in 1)
PlotFitIndividual	Graph of state values at steady-state versus measurements (individual)
PlotHeatmapCost	Heatmap of optimal costs for each output for each condition absolute cost
PlotStateSummary	Graph of only state values at steady-sate (all in 1)
PlotStateEvolution	Graph of state values evolution over the course of the simulation (two graphs)
PlotBiograph	Graph of network topology, nodes activities, and optimised parameters
PlotAllBiographs	(Only for machines with strong GPUs) Plot all Biographs above

Additional analyses after the optimisation with the default setting (0=no, 1=yes)

Variable	Explanation
Resampling_Analysis	Resampling of experimental data and re-optimise
NDatasets	Number of artificial datasets to resample
:-------:	:-:
LPSA_Analysis	Local parameter sensitivity analysis
Fast_Option	Performing faster LPSA by stopping if fitting costs go over a set threshold value
LPSA_Increments	Number of increments for LPSA. Increase for finer resolution
:-------:	:-:
KO_Analysis	Parameter knock-out analysis
KONodes_Analysis	Node knock-out analysis

Model & Optimisation results (stored in Matlab workspace)

Model information and inputs

Variable	Explanation
estim	Structure variable to store model information and results
estim.Interactions	List of interactions in the model, 1st col = number of interaction, 2nd col = inputs, 3rd col = type of interaction (-> = activate; -
estim.Input	List of input nodes (columns) for each experiment (rows)
estim.Input_idx	List of indices of input nodes (columns) in the model for each experiment (rows)
estim.Output	Experimental data for output nodes (columns) for each experiment (rows); Note: NaN is used for missing data point(s)
estim.Output_idx	List of indices of Output nodes (columns) in the model for each experiment (rows)
estim.Output	The error of experimental data (e.g. SD or SEM) for output nodes (columns) for each experiment (rows); Note: NaN is used for missing data point(s)
estim.state_names	List of names for all nodes in the model
estim.NrStates	Number of state/node in the model
estim.NrParams	Number of optimising parameters in the model
estim.param_index	Matrix of network information where 1st col = input indices, 2nd col = output indices, 3rd & 4th col = type of interactions (activate or inhibit, respectively), 5th col = type of Boolean gate (1 = AND, 2 = OR), 6th col = running number of Boolean gate in the model, 7th col = parameter range constraints (0 = default, -1 = low, 1 = high)
estim.param_vector	Vector of optimising parameters
estim.ma	Matrix of activation (read indices in estim.state_names)
estim.mi	Matrix of inhibition(read indices in estim.state_names)
estim.Aeq	Constrain equations for fmincon where Aeq*estim.param_vector = beq (constraints for sum of activating probabilities being 1)
estim.beq	Constrain equations for fmincon where Aeq*estim.param_vector = beq (constraints for sum of activating probabilities being 1)
estim.A	Constrain equations for fmincon where A*estim.param_vector = b (constraints for sum of inhibiting probabilities being less than 1)
estim.b	Constrain equations for fmincon where A*estim.param_vector = b (constraints for sum of inhibiting probabilities being less than 1
estim.LB	List of lower bounds for parameters
estim.UB	List of upper bounds for parameters
estim.kInd	Indices of parameters
estim.IdxInAct	Extracted vectors for Input for activating reactions
estim.IdxOutAct	Extracted vectors for Output for activating reactions
estim.IdxInInh	Extracted vectors for Input for inhibiting reactions
estim.IdxOutInh	Extracted vectors for Output for inhibiting reactions
estim.BoolMax	Number of total Boolean gate(s) in the model
estim.BoolIdx	Number of Boolean indices
estim.BoolOuts	Indices of Boolean output node
estim.FixBool	Indices of fixed Boolean variable
estim.option	Default and customized optimisation options for fmincon
estim.SSthresh	Threshold of fitting cost to accept the reach of steady-state

Optimisation outputs

Variable	Explanation
estim.MaxTime	The maximum running time from the optimisation
estim.AllofTheXs	State trajectory of each nodes during the optimisation (better representation in the plots)
estim.MeanStateValueAll	Mean state value from multiple simulations
estim.bestx	The best set of optimised parameter values

Optimisation results as sub-structures (estim.Results)

Optimisation

Variable	Explanation
estim.Results.Optimisation.FittingCost	List all fitting costs
estim.Results.Optimisation.FittingTime	List all optimisation time
estim.Results.Optimisation.ParamNames	List all parameter names
estim.Results.Optimisation.BestParams	List all best parameter values
estim.Results.Optimisation.StateNames	List all state names

Fitting evolution

Variable	Explanation
estim.Results.FitEvol.PlotCosts	List all 3 re-run fitting costs
estim.Results.FitEvol.Cost1/.Cost2/.Cost3	List fitting costs from the 3 re-runs

Resampling

Variable	Explanation
estim.Results.Resampling.Parameters	List all parameters
estim.Results.Resampling.OptimisedParameters	List all optimised parameters with new re-sampled measurements
estim.Results.Resampling.OptimisedSD	List the standard deviation from all optimised parameters with new re-sampled measurements
estim.Results.Resampling.LargeSD	Determine if the SD are larger than the threshold
estim.Results.Resampling.Costs	List all fitting cost during resampling process

LPSA (Local parameter sensitivity analysis)

Variable	Explanation
estim.Results.LPSA.ParamNames	List of all parameter names
estim.Results.LPSA.Identifiability	Vector determining whether each parameter is identifiable
estim.Results.LPSA.LPSA_Increments	The number of parameter interval to estimate identifiability
estim.Results.LPSA.p_SA	The list of parameters to perturb
estim.Results.LPSA.cost_SA	The fitting cost after parameter perturbations
estim.Results.LPSA.CutOff	The cut-off value of fitting cost to assess identifiability
estim.Results.LPSA.Interpretation	Type of identifiability in estim.Results.LPSA.Identifiability ('1=Identifiable','2=Partially identifiable','3=Non-identifiable')

Knockout (interaction)

Variable	Explanation
estim.Results.KnockOut.Parameters	List of parameters to knock-out parameters
estim.Results.KnockOut.AIC_values	List of AIC values after parameter knockout
estim.Results.KnockOut.KO_effect	List of interpretation if knockout has a substantial effect
estim.Results.KnockOut.Interpretation	Interpreter for knockout results ('0 = no KO effect','1 = KO effect')

KnockoutNode

Variable	Explanation
estim.Results.KnockOutNode.Parameters	List of nodes to knock-out
estim.Results.KnockOutNode.AIC_values	List of AIC values after node knockout
estim.Results.KnockOutNode.KO_effect	List of interpretation if knockout has a substantial effect
estim.Results.KnockOutNode.Interpretation	Interpreter for knockout results ('0 = no KO effect','1 = KO effect')

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