KroneckerBio
============
KroneckerBio is a systems biology and QSP modeling toolbox for Matlab. It provides an easy-to-use programming interface for building, simulating, and analyzing ODE models of biological systems. The toolbox incorporates numerous methods developed in the Tidor lab at MIT.
For mass action models, simulations can be run using sparse matrix operations, a fact that KroneckerBio exploits. Because Matlab has fast sparse matrix algorithms, simulating mass action models and running analyses that are dependent on simulations in KroneckerBio is very fast. If mass action is not sufficiently expressive for your model, models can also be defined using arbitrary analytic expressions.
Included in KroneckerBio is a rich set of methods for quantifying the uncertainty in the parameters and topology of a model and doing optimal experimental design to predict which experiments would be best for reducing the remaining uncertainty.
Installation
------------
KroneckerBio is written entirely in Matlab. No compilation is necessary. Simply download the entire source of the [most recent stable release](https://github.com/kroneckerbio/kroneckerbio/releases). KroneckerBio is now installed.
To use the library, run the `InitKronecker.m` Matlab script at the start of any session, which modifies the Matlab path to make the KroneckerBio functions available. Like `import` in Python or `library` in R, this only persists through the current Matlab session. KroneckerBio can be permanently imported by [saving the Matlab path](https://www.mathworks.com/help/matlab/ref/savepath.html) if desired.
Getting Started
---------------
The Motivating Example section below shows how to run a single simulation. In the `Tutorial` folder, the `T0x` scripts cover the basics of model building and import, simulation, and fitting.
The help section of each KroneckerBio function generally contains good information about how to use it.
In addition to the tutorial, we recommend the [mailing list](https://groups.google.com/forum/#!forum/kroneckerbio-users) for help in using KroneckerBio. Bugs can also be reported here or in the issues tab of the [GitHub repository](https://github.com/kroneckerbio/kroneckerbio).
Motivating Example
------------------
Below is an example showing the building, simulating, and plotting of the distributed-kinase distributed-phosphatase MAPK model, a simple mass action model.
```matlab
%% Build model
m = InitializeModelMassActionAmount('MAPK-DKDP');
% Compartments organize the states of a model. This model uses a dummy
% compartment "v" into which all states will go, which is fine for small
% models.
m = AddCompartment(m, 'v', 3, 1);
% Seed parameters are used by the initial conditions.
m = AddSeed(m, 'S', 2);
m = AddSeed(m, 'P', 1);
% Input species are externally defined and their amounts are not affected
% by the reactions. Here, the species "E" always has an amount of 1. The
% experimental conditions can override this with an arbitrary function of
% time.
m = AddInput(m, 'E', 'v', 1);
% State species are controlled by the reactions. Each one exists in a
% particular compartment and has a particular initial condition. In mass
% action models, the initial conditions are restricted to a linear
% combination of seed parameters.
m = AddState(m, 'S', 'v', 'S');
m = AddState(m, 'E:S', 'v');
m = AddState(m, 'M', 'v');
m = AddState(m, 'E:M', 'v');
m = AddState(m, 'D', 'v');
m = AddState(m, 'P', 'v', 'P');
m = AddState(m, 'P:D', 'v');
m = AddState(m, 'P:M', 'v');
% Outputs are the observable states of the model. In mass action models,
% they are restricted to being linear combinations of species.
m = AddOutput(m, 'S', {'S', 'E:S'});
m = AddOutput(m, 'M', {'M', 'E:M', 'P:M'});
m = AddOutput(m, 'D', {'D', 'P:D'});
% Kinetic parameters are used by the reactions.
m = AddParameter(m, 'k1on', 0.02);
m = AddParameter(m, 'k1off', 1);
m = AddParameter(m, 'k1cat', 0.01);
m = AddParameter(m, 'k2on', 0.032);
m = AddParameter(m, 'k2off', 1);
m = AddParameter(m, 'k2cat', 15);
m = AddParameter(m, 'k3on', 0.045);
m = AddParameter(m, 'k3off', 1);
m = AddParameter(m, 'k3cat', 0.092);
m = AddParameter(m, 'k4on', 0.01);
m = AddParameter(m, 'k4off', 1);
m = AddParameter(m, 'k4cat', 0.5);
% Reactions are defined by reactants, products, a forward rate constant,
% and reverse rate constant.
m = AddReaction(m, '', {'E', 'S'}, 'E:S', 'k1on', 'k1off');
m = AddReaction(m, '', 'E:S', {'E', 'M'}, 'k1cat');
m = AddReaction(m, '', {'E', 'M'}, 'E:M', 'k2on', 'k2off');
m = AddReaction(m, '', 'E:M', {'E', 'D'}, 'k2cat');
m = AddReaction(m, '', {'P', 'D'}, 'P:D', 'k3on', 'k3off');
m = AddReaction(m, '', 'P:D', {'P', 'M'}, 'k3cat');
m = AddReaction(m, '', {'P', 'M'}, 'P:M', 'k4on', 'k4off');
m = AddReaction(m, '', 'P:M', {'P', 'S'}, 'k4cat');
% This builds the system of ODEs from the definitions above.
m = FinalizeModel(m);
%% Simulation
% The experimental conditions object defines the initial conditions, the
% inputs, and the doses. Here, the default values on the model are used.
con = experimentInitialValue(m);
% The observation scheme object defines what information is stored during
% a simulation. Here, we store everything, which is convenient for small
% models.
obs = observationAll(10000);
% A simulation is a model run under specific experimental conditions
% recorded under a specific observation scheme.
sim = SimulateSystem(m, con, obs);
%% Plotting
% Simulation objects from observationAll are easy to plot.
plot(sim.t, sim.y(sim.t))
legend({m.Outputs.Name})
```
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毕业设计&课设-KroneckerBio系统生物学建模工具箱.zip (419个子文件)
dsymtemplate.c 4KB
.gitignore 27B
.gitignore 12B
getStructureEnum.m 139KB
finalizeModelMassActionAmount.m 119KB
getStructureFieldnames.m 109KB
getValueType.m 96KB
getDefaultValues.m 92KB
finalizeModelAnalytic.m 48KB
TopologyProbability.m 45KB
ode15sf.m 37KB
FitObjective.m 26KB
BestTopologyExperiment.m 23KB
symbolic2massaction.m 22KB
integrateCurvComp.m 19KB
LoadChenModel.m 18KB
integrateCurvSimp.m 17KB
MapParameterSpace.m 16KB
InitializeModelMassActionAmount.m 16KB
BestParameterExperiment.m 16KB
LoadModelMassAction.m 14KB
fixAbsTol.m 14KB
CheckAndConvert.m 14KB
SampleParameterSpace.m 14KB
computeObjSensAdj.m 14KB
symbolic2sbml.m 12KB
installSBML.m 11KB
UT01b_BuildingModelsAnalytic.m 11KB
observationLogWeightedSumOfSquares.m 11KB
odenumjac.m 10KB
devals.m 10KB
FinalizeModel.m 10KB
objectiveWeightedSumOfSquares.m 10KB
combinator.m 10KB
isSBML_Model.m 10KB
objectiveZero.m 10KB
ConvertFormulaToMathML.m 10KB
tUT00a_ComplexStepFD.m 10KB
UT02_LoadingModels.m 10KB
sbml2symbolic.m 10KB
UT01a_BuildingModelsMassAction.m 10KB
UT14_AnalyticModels.m 10KB
analytic_model_syms.m 9KB
isValidSymbolicModel.m 9KB
observationLinearWeightedSumOfSquares.m 9KB
steadystateCurv.m 9KB
accumulateOdeRevSelect.m 9KB
sbml2analytic.m 8KB
ObjectiveInformation.m 8KB
accumulateOdeRev.m 8KB
ParameterExperimentElimination.m 8KB
simple_model.m 7KB
daeic3.m 7KB
LoadBrownModel.m 7KB
integrateSensComp.m 7KB
UT04_Simulation.m 7KB
simbio2symbolic.m 7KB
UT07_Objective.m 7KB
normbndrnd.m 7KB
massaction2symbolic.m 7KB
SimulateCurvature.m 7KB
simbio2analytic.m 7KB
SaveModel.m 7KB
odearguments.m 7KB
PubDocs.m 6KB
integrateSensSimp.m 6KB
FiniteObjectiveGradient.m 6KB
fixAbsTolLna.m 6KB
T01a_Building_Model_MassAction.m 6KB
computeObjHess.m 6KB
FiniteObjectiveHessian.m 6KB
GoodAbsTol.m 6KB
SimulateSensitivity.m 6KB
integrateMfk.m 6KB
randomExperimentFittingData.m 6KB
integrateLnaComp.m 5KB
observationAll.m 5KB
ObjectiveLogLikelihood.m 5KB
FiniteSimulateSensitivity.m 5KB
ObjectiveProbability.m 5KB
FiniteSimulateCurvature.m 5KB
experimentSteadyState.m 5KB
accumulateOdeFwdSimp.m 5KB
integrateObjSensSelect.m 5KB
integrateObjSens.m 5KB
accumulateOdeFwdComp.m 5KB
odezero.m 5KB
OutputSBML.m 5KB
UT03_CreatingExperiments.m 5KB
UT15_ParseExprs.m 5KB
ObjectiveGradient.m 5KB
objectiveLogNormalPriorOnSeedParameters.m 5KB
objectiveLogNormalPriorOnKineticParameters.m 5KB
SimulateSystem.m 5KB
ObjectiveHessian.m 5KB
odefinalize.m 4KB
daeic12.m 4KB
T01b_Building_Model_Analytic.m 4KB
ObjectiveValue.m 4KB
computeObjGrad.m 4KB
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