You didn't still login or signup? Saving the edited data (session data) in the GlycoSim will be permitted after your login. So, we recommend you to login or signup before you go.
4. Check prediction method:
  •  Forward Prediction
  •  Forward Prediction with glycoprofile
  •  Backward Prediction
  •  Backward Prediction with initial glycans

          *This page can be available after GPP program was excuted.

          Description

          You can compare predicted glycans with ones from your csv file. The glycans represented by Linear Code supports one monosaccharide uncertainty '*' and linkage or anomer uncertainty '?'. For instance, "*Ab?GNb?ANa;S" has one unknown monosaccharide and uncertain linkage between GlcNAc(GN) and GalNAc(AN). Furthermore, you should set 'Glycan Type' to assign where the glycan is attributed to (e.g. N, O, L).

          CSV file with Linear Code and Glycan Type is required to compare.
          Sample Template for N-type glycans and O-type glycans of Mouse ES cells from Nairn et al.
          5. Check prediction method:
          •  Forward Prediction
          •  Forward Prediction with glycoprofile
          •  Backward Prediction
          •  Backward Prediction with initial glycans
          This page shows an example of mucin O-type glycan pathway.
          1. You can edit these parameters (i.e. Enzyme name, Class, Type, etc.) by changing these variables, and make sure all variables are correctly validated by cheking the background color is green.
          2. Enzyme specificities data can be displayed by either a table or a list. If all data are corrected, then we need to specifiy initial glycans.
          3. To setting initial glycans, we need to attribute index, LinearCode Representaion, number of monosaccharides, Glycan type into the textarea shown below (e.g. 1,;S/T,0,O)
          4. Set max monosaccharide size not to predict too much reactions.
          5. Push button to predict.
          4. Check prediction method:
          •  Forward Prediction
          •  Forward Prediction with glycoprofile
          •  Backward Prediction
          •  Backward Prediction with initial glycans
          • title
          Download Template
          Compartments:
          Species:
          Parameters:
          Reactions:
          Assignment Rule:
          1. Check your SBML content
          2. Setting Time-Course simulation duration time and step number.

          Simulation Engine:
          absolute tolerance:
          relative tolerance:
          3. Click to run simulation.
           Runge  kutta method(RK45)in 'scipy' python module is used for the simulation.
          If you want to execute the parameter estimation program, you have to login or signup to the GlycoSim in advance.
          Individual plot Download Result
          Group plot Download Result

          Use checkbox to filter variables by categories shown below.

            Compartments

            Glycan Type

          Input plot
          If you want to execute the parameter estimation program, you have to login or signup to the GlycoSim in advance.
          Description: We can estimate parameters such as kf, Km, Kmd, Et of each reactions. To do that, we need to select which parameter should be estimated, and experimental data of glycan species.
          1. Use checkbox to decide estimation parameters.
          Filter by:
          2. Set experimental data
          3. setting estimation algorithm configuration.
          Method:
          Number of generation:
          Number of generation
          Swarm size
          If you want to execute the parameter estimation program, you have to login or signup to the GlycoSim and save the session. Then, the user data pool page shows your latest session data in the user page. You click the session name to translocate the GlycoSim page with your session data.
          * When you click the "Run" or "Run with GPU" button, you will be translocated to your model data pool page.
          * In this page, you can see your session data information table.
          * It may take too much time because the execution time is depend on the paramter setting.
          * In the GPU estimation, only the LSODA solver is implemented,
          * Simulation setting for the parameter estimation task depends on the simulation task page settings.
          Property Name
          Kinetics
          Substrate
          Product
          Modifier
          Objective function used in this application is chi squared function between simulation points and observation points.
          The number of generations is used for the genetic algorithm. The objective values are displayed at each generations.
          Download result
          Download result
          The sum residue is:
          The sum of square residue is:
          Sensitivity Analysis calculates each species sensitivities of each target items. 'Target' means which kinds of objects should change. For example, the target 'parameter' changes the value itself at the rate of 1.01. 'Metric' means
          download result