Recent changes to optipa

optipa modified by MaartenH

MaartenH — Fri, 29 Jan 2016 00:50:21 -0000

--- v7
+++ v8
@@ -17,7 +17,7 @@

 ####Link:####

-www.optipa.be
+[www.optipa.be](http://www.optipa.be)

 ####Designed for:####
@@ -62,4 +62,4 @@

 ####Description:####
-OptiPa comes as a standalone executable (64 or 32 bit). For a full description [check out the website](www.optipa.be) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).
+OptiPa comes as a standalone executable (64 or 32 bit). For a full description [check out the website](http://www.optipa.be) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).

optipa modified by MaartenH

MaartenH — Fri, 29 Jan 2016 00:48:31 -0000

--- v6
+++ v7
@@ -17,7 +17,7 @@

 ####Link:####

-https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm
+www.optipa.be

 ####Designed for:####
@@ -62,4 +62,4 @@

 ####Description:####
-OptiPa comes as a standalone executable (64 or 32 bit). For a full description [check out the website](https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).
+OptiPa comes as a standalone executable (64 or 32 bit). For a full description [check out the website](www.optipa.be) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).

optipa modified by MaartenH

MaartenH — Sun, 06 Jul 2014 23:09:56 -0000

--- v5
+++ v6
@@ -26,7 +26,7 @@

 ####Applicable to:####
 OptiPa was developed for research purposes in general but with the food-related disciplines as the main focus. The model is to be defined by the user and therefore OptiPa is not limited to any particular application. 
-Within the framework of this __OpenML for Predictive Modelling in Food__ project, one of the demo models contains an implementation of the Baranyi-model for bacterial growth combined with the Ratkowsky square root model to describe temperature dependence. This deom comes with some experimental data on bacterial growth under dynamic temperature conditions. However, the user has the freedom to implement any possible (microbial  growth) model. 
+Within the framework of this __OpenML for Predictive Modelling in Food__ project, one of the demo models contains an implementation of the Baranyi-model for bacterial growth combined with the Ratkowsky square root model to describe temperature dependence. This demo comes with experimental data on bacterial growth under dynamic temperature conditions. However, the user has the freedom to implement any possible (microbial  growth) model. 

 ####Media covered:####
 It is up to the user to provide experimental data and model equations on any media specific aspects. From a modelling point of view there are no limitations. 
@@ -41,9 +41,9 @@

 ####Modeling approach:####
-Models can be implemented using a combination of ODEs and analytical equations.
+Deterministic models can be implemented using a combination of ODEs and analytical equations.
 OptiPa was developed under Matlab (The MathWorks, Inc., Natick, MA, USA) and allows the user to to access the main programming functionalities from Matlab. As OptiPa is designed for ODE based models, it can be applied to non-static conditions such as dynamic temperature scenarios.
-To run OptiPa the user needs to prepare the following input files:
+To run OptiPa the user needs to prepare three input files:

 1. __An experimental data file__ containing measured data on one or more of the dependent model variables (e.g. CFU counts). The experimental data can be organised in numbered experiments that each coincides with a single time span. If replicate measurements were taken they can either be organised in a single experiment with replicate observations, or in as many experiments as there where replicates. The experimental data can be used to estimate any unknown model parameters.

optipa modified by MaartenH

MaartenH — Sun, 06 Jul 2014 23:00:00 -0000

--- v4
+++ v5
@@ -43,18 +43,22 @@
 ####Modeling approach:####
 Models can be implemented using a combination of ODEs and analytical equations.
 OptiPa was developed under Matlab (The MathWorks, Inc., Natick, MA, USA) and allows the user to to access the main programming functionalities from Matlab. As OptiPa is designed for ODE based models, it can be applied to non-static conditions such as dynamic temperature scenarios.
-To run OptiPa the user need to prepare the following input files:
-1 An experimental data file containing measured data on one or more of the dependent model variables (e.g. CFU counts). The experimental data can be organised in numbered experiments that each coincides with a single time span. If replicate measurements were taken they can either be organised in a single experiment with replicate observations, or in as many experiments as there where replicates. 
-* A non-mandatory condition file containing possible model input variables. Besides for defining model input variables (e.g. temperature, pH) this file can be used to define a possible (sub)grouping of the experiments based on characterisations like medium type, organism, or added growth factors. Such grouping variables can be used by OptiPa to estimate separate multiple values for a single parameter depending on such groupings.
-* The OptiPa model file (OMF-file) containing the actual model definition. It can bre prepared starting from a template and typically consists of four distinctive parts: model initialisation, data pre-processing, the actual ODE model definition, and the model post-processing.
+To run OptiPa the user needs to prepare the following input files:
+
+1. __An experimental data file__ containing measured data on one or more of the dependent model variables (e.g. CFU counts). The experimental data can be organised in numbered experiments that each coincides with a single time span. If replicate measurements were taken they can either be organised in a single experiment with replicate observations, or in as many experiments as there where replicates. The experimental data can be used to estimate any unknown model parameters. 
+
++ A non-mandatory __condition file__ containing possible model input variables. Besides for defining model input variables (e.g. temperature, pH) this file can be used to define a possible (sub)grouping of the experiments based on characterisations like medium type, organism, or added growth factors. Such grouping variables can be used by OptiPa to estimate separate multiple values for a single parameter depending on such groupings.
+
++ The OptiPa __model file__ (OMF-file) containing the actual model definition. It can bre prepared starting from a template and typically consists of four distinctive parts: model initialisation, data pre-processing, the actual ODE model definition, and the model post-processing.

 ####Optimisation approach:####
 The following model optimisation methods have been incorporated to assist the user in estimating the optimal model parameters:
+
 * Least square non-linear optimisation
-* Direct pattern search
-* Genetic algorithm
-* Differential evolution algorithm
-* Enhanced Scatter Search
++ Direct pattern search
++ Genetic algorithm
++ Differential evolution algorithm
++ Enhanced Scatter Search

 ####Description:####

optipa modified by MaartenH

MaartenH — Sun, 06 Jul 2014 22:54:11 -0000

--- v3
+++ v4
@@ -58,4 +58,4 @@

 ####Description:####
-OptiPa comes as a standalone executable (64 or 32 bit). For a full description [heck out the website](https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).
+OptiPa comes as a standalone executable (64 or 32 bit). For a full description [check out the website](https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).

optipa modified by MaartenH

MaartenH — Sun, 06 Jul 2014 22:53:43 -0000

--- v2
+++ v3
@@ -1,7 +1,8 @@
-![OptiPa, a Matlab based optimisation interface](https://perswww.kuleuven.be/~u0040603/optipa/images/2.gif)
+![OptiPa, a Matlab based optimisation interface](https://perswww.kuleuven.be/~u0040603/optipa/images/2.gif)

 ####Name:####
 OptiPa
+

 ####Company / Institution:####
 KU Leuven, BIOSYST-MeBioS
@@ -11,33 +12,50 @@
 none

 ####Contact person:####
+Maarten Hertog

-Maarten Hertog

 ####Link:####

 https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm

+
 ####Designed for:####
 OptiPa is a free and generic software tool to __simulate__, __calibrate__ and __validate__ ordinary differential equation (ODE) based models. OptiPa was developed because of an urgent need for a flexible and versatile interface to __estimate model parameters__ on ODE based models leaving only a minimum of programming for the end user. One of the innovative aspects of OptiPa is that it readily allows the user to identify the different __sources of variation__ in a model as it allows, for instance, estimating certain model parameters either in common, per experiment, per treatment condition, per cultivar, per organism or per experimental unit. Analysing data this way enhances the interpretation of experimental data and the subsequent application of the model to different situations.

+
 ####Applicable to:####
-OptiPa was developed for research purposes in general but with the food-related disciplines as the main focus. The model is to be defined by the user and therefore OptiPa is not limited to any particular application. One of the demo models contains an implementation of the Baranyi-model for bacterial growth combined with the Ratkowsky square root model to describe temperature dependence. Some experimental data is included on growth under dynamic temperature conditions. However, the user has the freedom to implement any possible (microbial  growth) model using a combination of ODEs and analytical equations.
-OptiPa was developed under Matlab (The MathWorks, Inc., Natick, MA, USA) and allows the user to to make use of the basic programming functionality from Matlab.
+OptiPa was developed for research purposes in general but with the food-related disciplines as the main focus. The model is to be defined by the user and therefore OptiPa is not limited to any particular application. 
+Within the framework of this __OpenML for Predictive Modelling in Food__ project, one of the demo models contains an implementation of the Baranyi-model for bacterial growth combined with the Ratkowsky square root model to describe temperature dependence. This deom comes with some experimental data on bacterial growth under dynamic temperature conditions. However, the user has the freedom to implement any possible (microbial  growth) model. 

 ####Media covered:####
 It is up to the user to provide experimental data and model equations on any media specific aspects. From a modelling point of view there are no limitations. 

+
 ####Microorganisms covered:####
 It is up to the user to provide experimental data and model equations on any organism specific aspects. From a modelling point of view there are no limitations.   

+
 ####Growth Factors covered:####
-
+It is up to the user to provide experimental data and model equations on any growth factor specific aspects. From a modelling point of view there are no limitations. 

 ####Modeling approach:####
+Models can be implemented using a combination of ODEs and analytical equations.
+OptiPa was developed under Matlab (The MathWorks, Inc., Natick, MA, USA) and allows the user to to access the main programming functionalities from Matlab. As OptiPa is designed for ODE based models, it can be applied to non-static conditions such as dynamic temperature scenarios.
+To run OptiPa the user need to prepare the following input files:
+1 An experimental data file containing measured data on one or more of the dependent model variables (e.g. CFU counts). The experimental data can be organised in numbered experiments that each coincides with a single time span. If replicate measurements were taken they can either be organised in a single experiment with replicate observations, or in as many experiments as there where replicates. 
+* A non-mandatory condition file containing possible model input variables. Besides for defining model input variables (e.g. temperature, pH) this file can be used to define a possible (sub)grouping of the experiments based on characterisations like medium type, organism, or added growth factors. Such grouping variables can be used by OptiPa to estimate separate multiple values for a single parameter depending on such groupings.
+* The OptiPa model file (OMF-file) containing the actual model definition. It can bre prepared starting from a template and typically consists of four distinctive parts: model initialisation, data pre-processing, the actual ODE model definition, and the model post-processing.

+####Optimisation approach:####
+The following model optimisation methods have been incorporated to assist the user in estimating the optimal model parameters:
+* Least square non-linear optimisation
+* Direct pattern search
+* Genetic algorithm
+* Differential evolution algorithm
+* Enhanced Scatter Search

 ####Description:####
-
+OptiPa comes as a standalone executable (64 or 32 bit). For a full description [heck out the website](https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm) and the [online help files](https://perswww.kuleuven.be/maarten_hertog/optipa/OptiPaHelp/OptiPa.html).

optipa modified by MaartenH

MaartenH — Sun, 06 Jul 2014 22:28:24 -0000

--- v1
+++ v2
@@ -1,13 +1,14 @@
+![OptiPa, a Matlab based optimisation interface](https://perswww.kuleuven.be/~u0040603/optipa/images/2.gif)
+
 ####Name:####
 OptiPa

 ####Company / Institution:####
-
+KU Leuven, BIOSYST-MeBioS

 ####Development Partners:####
-
-
+none

 ####Contact person:####

@@ -15,22 +16,20 @@

 ####Link:####

-https://perswww.kuleuven.be/~u0040603/optipa/optipamain.htm
+https://perswww.kuleuven.be/maarten_hertog/optipa/optipamain.htm

 ####Designed for:####
+OptiPa is a free and generic software tool to __simulate__, __calibrate__ and __validate__ ordinary differential equation (ODE) based models. OptiPa was developed because of an urgent need for a flexible and versatile interface to __estimate model parameters__ on ODE based models leaving only a minimum of programming for the end user. One of the innovative aspects of OptiPa is that it readily allows the user to identify the different __sources of variation__ in a model as it allows, for instance, estimating certain model parameters either in common, per experiment, per treatment condition, per cultivar, per organism or per experimental unit. Analysing data this way enhances the interpretation of experimental data and the subsequent application of the model to different situations.

-
-
-####Applicable for:####
-
+####Applicable to:####
+OptiPa was developed for research purposes in general but with the food-related disciplines as the main focus. The model is to be defined by the user and therefore OptiPa is not limited to any particular application. One of the demo models contains an implementation of the Baranyi-model for bacterial growth combined with the Ratkowsky square root model to describe temperature dependence. Some experimental data is included on growth under dynamic temperature conditions. However, the user has the freedom to implement any possible (microbial  growth) model using a combination of ODEs and analytical equations.
+OptiPa was developed under Matlab (The MathWorks, Inc., Natick, MA, USA) and allows the user to to make use of the basic programming functionality from Matlab.

 ####Media covered:####
-
-
+It is up to the user to provide experimental data and model equations on any media specific aspects. From a modelling point of view there are no limitations. 

 ####Microorganisms covered:####
-   
-
+It is up to the user to provide experimental data and model equations on any organism specific aspects. From a modelling point of view there are no limitations.   

 ####Growth Factors covered:####

Recent changes to optipa

optipa modified by MaartenH

optipa modified by MaartenH

optipa modified by MaartenH

optipa modified by MaartenH

optipa modified by MaartenH

optipa modified by MaartenH

optipa modified by MaartenH

optipa modified by Matthias Filter

Name:

Company / Institution:

Development Partners:

Contact person:

Link:

Designed for:

Applicable for:

Media covered:

Microorganisms covered:

Growth Factors covered:

Modeling approach:

Description: