N-Glycan biosynthesis - Revision history

Eseniya@developmentontheedge.com: /* Results */

2014-06-06T08:21:49Z

‎Results

Eseniya@developmentontheedge.com: /* Model optimization */

2014-06-06T08:20:47Z

‎Model optimization

Eseniya@developmentontheedge.com: /* Model optimization */

2014-06-06T08:19:40Z

‎Model optimization

Eseniya@developmentontheedge.com: /* Model optimization */

2014-06-06T08:17:59Z

‎Model optimization

Eseniya@developmentontheedge.com: /* Model optimization */

2014-06-06T08:07:24Z

‎Model optimization

Eseniya@developmentontheedge.com: /* The first stage of the modeling */

2014-06-06T08:01:09Z

‎The first stage of the modeling

Eseniya@developmentontheedge.com: /* Model realization */

2014-06-06T07:59:37Z

‎Model realization

Eseniya@developmentontheedge.com: /* The second stage of the modeling */

2014-06-06T07:59:21Z

‎The second stage of the modeling

Eseniya@developmentontheedge.com: /* Model realization */

2014-06-06T07:56:20Z

‎Model realization

Eseniya@developmentontheedge.com: /* The first stage of the modeling */

2014-06-06T07:54:23Z

‎The first stage of the modeling

@@ Line 47: / Line 47: @@
 === Results ===
-We carry out parameters optimization for patients from Korcula population. The objective functions, which is calculated as the sum of squared differences between model and experimental concenration, have the order of 0,0000001-0,00000001.
+The research with use methods of mathematical statistics and  R language features was carried out for the getting after individual optimization parameters. It was found that on the whole parameters correlate with each other weakly, two parameters (the turnover number for beta-1,4-mannosyl-glycoprotein (GnTIII, EC 2.4.1.144) and the Michaelis constant of glycan structure A2[3]BG2S1) correlate with patients’ age (with coefficients 0,1095195 and 0,131857 respectively).
+The Random Forest Algorithm applied to the results of the optimization can predict about 15% occurring processes (against 60% in the original parameter space).
-For every patient his/her individual parameters correlate with each other and with age poorly. Random forest analysis can rpedict only about 15% of results.
 ==References==
 <References/>

← Older revision		Revision as of 08:20, 6 June 2014
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	In the optimization of the second model the individual optimization time reduced to 4-5 minutes per patient, the level of accuracy of the parameters fitting about 10<sup>-3</sup>-10<sup>-4</sup> was achived.		In the optimization of the second model the individual optimization time reduced to 4-5 minutes per patient, the level of accuracy of the parameters fitting about 10<sup>-3</sup>-10<sup>-4</sup> was achived.
		+
		+	The third model has the minimum optimization time for each person (~1 min) and the highest accuracy of matching between the experimental and simulation data (~10<sup>-7</sup>-10<sup>-8</sup>) are achieved for this model (in comparison with the models obtained on the previous two stages).

	=== Results ===		=== Results ===

← Older revision		Revision as of 08:19, 6 June 2014
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	The optimization of first model was held relatively long time (~15 min for each patient), and the resulting accuracy of the parameters fitting was very low (~10<sup>-1</sup>)		The optimization of first model was held relatively long time (~15 min for each patient), and the resulting accuracy of the parameters fitting was very low (~10<sup>-1</sup>)
		+
		+	In the optimization of the second model the individual optimization time reduced to 4-5 minutes per patient, the level of accuracy of the parameters fitting about 10<sup>-3</sup>-10<sup>-4</sup> was achived.

	=== Results ===		=== Results ===

← Older revision		Revision as of 08:17, 6 June 2014
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	We use the expiremental data for Korcula population <ref>[http://www.ncbi.nlm.nih.gov/pubmed/21653738‎ High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations.]</ref> and made the [[Optimization]] of the individual parameters for all patients after every stage of the modeling.		We use the expiremental data for Korcula population <ref>[http://www.ncbi.nlm.nih.gov/pubmed/21653738‎ High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations.]</ref> and made the [[Optimization]] of the individual parameters for all patients after every stage of the modeling.
		+
		+	The optimization of first model was held relatively long time (~15 min for each patient), and the resulting accuracy of the parameters fitting was very low (~10<sup>-1</sup>)

	=== Results ===		=== Results ===

@@ Line 37: / Line 37: @@
 === Model optimization ===
-The next step was the model reduction: the decrease in the number of model parameters and materials, which allowed to significantly simplify the model, speed up the [[Simulation]] and compare the model results with the experiment.
+We use the expiremental data for Korcula population <ref>[http://www.ncbi.nlm.nih.gov/pubmed/21653738‎ High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations.]</ref> and made the [[Optimization]] of the individual parameters for all patients after every stage of the modeling.
-−
-We use the expiremental data for Korcula population <ref>[http://www.ncbi.nlm.nih.gov/pubmed/21653738‎ High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations.]</ref> and made the [[Optimization]] of model parameters was performed for every individual patient.
 === Results ===

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 There were the decrease in the number of diagram elements (8 enzymes and 41 glycans, 40 reactions with 39 parameters in the part of the model with the experimental confirmation). Based on experimentally validated information the initial glycan concentrations, except 9-mannose G00011, were taken as 0 mol. The initial concentration of 9-mannose G00011, which was the reactant in the first reaction occurring in the first compartment of the Golgi apparatus, was taken as 300 mol.
-==== The first stage of the modeling ====
+==== The third stage of the modeling ====
 === Model optimization ===

← Older revision		Revision as of 07:59, 6 June 2014
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	==== The second stage of the modeling ====		==== The second stage of the modeling ====
		+
		+	In the second stage the initial model was split into two parts. For glycans included in the first part the mass spectrometry data was available. So it was possible to check the correctness of the getting results. The second part of the model does not have experimental verification yet.
		+	Also an attempt to take into consideration the localization of enzymes in different compartments of the Golgi apparatus according to the order of their entry into the reactions was made.
		+	There were the decrease in the number of diagram elements (8 enzymes and 41 glycans, 40 reactions with 39 parameters in the part of the model with the experimental confirmation). Based on experimentally validated information the initial glycan concentrations, except 9-mannose G00011, were taken as 0 mol. The initial concentration of 9-mannose G00011, which was the reactant in the first reaction occurring in the first compartment of the Golgi apparatus, was taken as 300 mol.

	=== Model optimization ===		=== Model optimization ===

← Older revision		Revision as of 07:56, 6 June 2014
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	On the first stage of the modeling an attempt to implement the N-glycan biosynthesis model from the KEGG database was made. Reaction equations were generated using KEGGtranslator, reaction parameters were taken from the database BRENDA Enzyme Database, and the initial concentration of the substances were taken as 1 mol. The model included 25 enzymes, 62 glycan, a number of substances – participants of intermediate reactions (formation and dissociation of the enzyme-substrate complex).The model consisted of 68 reactions with 126 parameters in their equations. The localization of enzymes in different parts of Golgi apparatus according to the order of their entry into the reactions was not taken into consideration.		On the first stage of the modeling an attempt to implement the N-glycan biosynthesis model from the KEGG database was made. Reaction equations were generated using KEGGtranslator, reaction parameters were taken from the database BRENDA Enzyme Database, and the initial concentration of the substances were taken as 1 mol. The model included 25 enzymes, 62 glycan, a number of substances – participants of intermediate reactions (formation and dissociation of the enzyme-substrate complex).The model consisted of 68 reactions with 126 parameters in their equations. The localization of enzymes in different parts of Golgi apparatus according to the order of their entry into the reactions was not taken into consideration.
		+
		+	==== The second stage of the modeling ====

	=== Model optimization ===		=== Model optimization ===

← Older revision		Revision as of 07:54, 6 June 2014
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	==== The first stage of the modeling ====		==== The first stage of the modeling ====
		+
		+	On the first stage of the modeling an attempt to implement the N-glycan biosynthesis model from the KEGG database was made. Reaction equations were generated using KEGGtranslator, reaction parameters were taken from the database BRENDA Enzyme Database, and the initial concentration of the substances were taken as 1 mol. The model included 25 enzymes, 62 glycan, a number of substances – participants of intermediate reactions (formation and dissociation of the enzyme-substrate complex).The model consisted of 68 reactions with 126 parameters in their equations. The localization of enzymes in different parts of Golgi apparatus according to the order of their entry into the reactions was not taken into consideration.

	=== Model optimization ===		=== Model optimization ===