This introduction help to use the GlycoSim.
GlycoSim homepage has mainly 6 tabs
- Glycan Pathway Prediction
- Parameter Setting
- SBML Model Information
- Parameter Estimation
- Sensitivity Analysis
Glycan Pathway Prediction(GPP)
GPP method can predict a glycosylation pathway by using four input information.
Enzyme-substrate specificity defines the reaction relationships between substrates and products. The summary of the information is below:
|Property Name||Introduction||Input rules|
|Enzyme||Enzyme name that activates reactions.||Each 'Enzyme' names have to be unique.|
|Substrate||Reactants for reactions.||Its value has to be written in LiCoRR format for describe
Multiple substrates cannot be registered separately with half-spaces. However, They are alternatively registered as one substrates with an under bar.
|Product||Products for reactions||Same as the 'Substrate' section.|
|Constraint||Products for reactions||Same as the 'Substrate' section.|
|Cosubstrate||Products for reactions||Same as the 'Substrate' section.|
|Class||Enzyme function class that describes whether the enzymes works as a glycosidase or a transferase.||The letters 'GH' or 'GT' has to be specified. 'GH' means a glycosidase, and 'GT' is a transferase.|
|Type||Glycan type of the reactions that describes whether the glycan related to the reaction is a N-glycan, an O-glycan,a L-glycan(Lipid) or an other type of glycan.||
|Reaction type||Enzyatic reaction rates||
Substrate : (GN$b
Product : (Ab4GN$b
Constraint : none
Cosubstrate : UDP_Gal
Class : GT
Type : O
ReactionType: Sequential-order Bi-Bi kinetics
For example, one of the format '$' is used for indicating the modification uncertainty of monosaccharide such as sulfate modification like GN[6S]b3Ab4 wriiten in the LinearCode format. The left parency express the terminal glycan, therefore, the result comes different when the 'Substrate' value is attached with or without a left parency.
The 'Product' property is related to reaction products e.g.) (Ab4GN$b, (...(GNb6)ANa. the product value expression is the LiCoRR format that is same as 'Substrate' value.
The 'Constraint' property attaches substrate conditions to the 'Substrate' value. When these conditions are accomplished, the reaction relationship is attached to the prediction pathway.
The 'Cosubstrate' property defines a co-substrate such as UDP-Gal and UDP-Man. These value have to include hyphen or underbar between words e.g.) UDP-Gal or UDP_Gal for detecting coproduct information.
The 'Class' property can take two enzyme class. The "GH" means a glycosylation hydrase, the "GT" means a glycosylation transferase.
The 'Type' property defines a glycan type. There can be 4 types, N, O, L, and Other. 'N' is stand for N-glycan, 'O' is stand for O-glycan, 'L' is stand for L-glycan.
The initial glycan is the first substrate for glycan pathway. The input should be 4 comma separated data e.g.) 1,Ma2Ma2Ma3(Ma3(Ma2Ma6)Ma6)Mb4GNb4GNb;N,10,N. 1st tab is the species number, 2nd tab is LinearCode glycan representation, 3rd tab is the number of monosaccharides, 4th is glycan type.
This information is used for decrease the size of the prediction network.
These optional value is required for 3 prediction method ('forward prediction with glycoprofile', 'backward prediction with initial glycan', 'backward prediction with initial glycan')(
There are 4 types of prediction methods, and the defalt method is forward prediction. This method predicts relationships from intial glycans.
|Forward Prediction||Predict the pathway from the initial glycans.|
|Forward Prediction with glycoprofile||Predict the pathway from the initial glycans, then the size of predicted network is reduced by pruning the unnecessary reactions.|
|Backward Prediction||Predict the pathway retrogradely from the glycoprofile.|
|Backward Prediction with glycoprofile||Predict the pathway retrogradely from the glycoprofile, then the size of predicted network is reduced by pruning the unnecessary reactions.|
Another method, 'forward prediction with glycoprofile' need a glycoprofile information to use network pruning. Backward prediction makes pathway from product glycans. Another method 'Backward prediction with initial glycans' is for network pruning.
The result for the GPP method gives 3 types of information and prepare data for the next parameter setting section.
- Species information
- Reaction information
- Pathway information
Species information contains 4 columns.
Name Summary Index the glycan id for the glycan species. Structure the LinearCode glycan representation Size the length of glycan. Type the glycan type.
- Index - the glycan id for the glycan species.
- Structure - the LinearCode glycan representation
- Size - the length of glycan.
- Type - the glycan type.
Reactions information contains 4 columns.
- Index - the reaction id.
- Child - the product species id
- Parent - the reactant species id.
- Enzyme - the enzyme name.
Each node represents the species, and each edges represent the reactions. When the cursor pointer is on the node, the glycan images are pop up if existed.
After the pathway prediction, we have to set the parameter sets to build the mathematical model.
Set localization concentration
Set kinetic parameters
After that, initial glycans concentration are required. These values can set the value constant or not. Then, transport rates are set, which the defalt value is 0.18 [1/min], which is based on the article.
Set kinetic parameters in each reactions
Mathematical modeling of golgi apparatus
In the GlycoSim, the concept of the glycosylation model is shown below:
Each reactions in the compartments compose the Michaelis-Menten equation. The example formula of the transferase reactions is below:
Based on the model, The ordinary differential equations (ODE) are built to simulate the concentration of each glycan species at arbitary timepoints. The example of mathematical formula of the glycosyl transferase is below:
SBML Model Information
'SBML Model Information' page describes the information of the built model. The main component of the SBML file is
Editing SBML information by double click
'Simulation' page describes the information of the built model. The main component of the SBML file is
- SBML information
- Setting duration and time steps
- Solver configuration
- Individual Prot
- Group Plot
- Filtering Plot
'Parameter Estimation' page describes the information of estimation setting, and have 3 pages for adjusting parameter setting, experimental data preparation and result page.
Adjusting parameter page require the estimation parameters by checking the checkbox in the left first column. The number of these parameters should be more than 1 to execute the task. Upper bounds and lower bounds are required to clip the boundary of these values.
After setting the values, click the next 'Experimental Data' tab to translocate the page. In the experimental data table, the left column shows the species names. To set the experimental values, we have to set the experimental time to the first row. Then, we set each species concentration values to each rows. Note that these values should be the same column with 'time' values.
'Sensitivity Analysis' page describes the information of Sensitivity setting. In this page, the heatmap of how much the target variables are changed when the effect variables are changed at 1.01% are shown.
Users can select the types of effect variables:
- Initial Condition