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Table 5 Model selection results of UDP-GlcNAc isotopologue data

From: Moiety modeling framework for deriving moiety abundances from mass spectrometry measured isotopologues

Modela

Estimator (AICc)

6_G1R1A1U3 (expert-derived model)

− 229.2918

6_G1R1A1U3_r4

− 227.5208

6_G1R1A1U3_u4

− 225.0006

6_G0R2A1U3_g3r2r3_g6r5

− 223.1633

6_G1R1A1U3_g5

− 215.9565

7_G1R2A1U3_r1

− 212.4727

7_G2R1A1U3_g1

−212.1217

7_G1R2A1U3_r3

−210.9640

7_G1R1A2U3

−210.0952

7_G2R1A1U3_g5

−208.1346

7_G1R2A1U3_g3r2r3

− 207.6523

7_G1R2A1U3_r2

−207.4187

7_G2R1A1U3_g4

− 206.6430

7_G2R1A1U3_g2

−206.5609

7_G0R2A2U3_g3r2r3_g6r5

− 205.0569

7_G2R1A1U3_g3

− 204.8797

7_G0R3A1U3_g3r2r3_g6r5_g5r4

−204.2729

7_G1R1A1U4

− 203.3710

7_G1R2A1U3_r4

− 202.6782

6_G1R1A1U3_a1

−199.5560

8_G2R1A2U3_g1

− 195.9713

7_G1R1A1U3C1

− 195.5788

8_G1R2A2U3_r1

−195.4893

7_G0R3A1U3_g3r2r3_g6r5_r4

−192.4980

8_G1R2A2U3_r2r3

−187.3342

8_G1R2A2U3_r3

−186.8810

8_G2R1A2U3_g5

−186.2693

8_G1R2A2U3_r2

−186.2562

8_G2R1A2U3_g2

− 185.6112

8_G2R1A2U3_g4

− 184.9444

8_G1R2A2U3_g3r2r3

−184.2929

8_G1R2A2U3_g3r2r3_g6r5_g5

− 183.2154

8_G2R1A2U3_g3

−183.1467

8_G1R2A2U3_r4

− 182.1334

8_G1R1A2U3C1

− 177.5013

9_G2R2A2U3_r2r3_g1

− 170.3323

9_G2R2A2U3_r2r3_g2

− 161.5770

9_G2R2A2U3_r2r3_g3

− 160.7823

9_G2R2A2U3_r2r3_g6r5_g3_g5

−160.6917

9_G2R2A2U3_r2r3_g4

−160.4500

9_G2R2A2U3_r2r3_g5

− 158.8733

  1. Optimization settings: method = ‘SAGA’, SAGA_parameters = {‘stepNumber’: 100000, ‘temperatureStepSize’: 100, ‘alpha’: 1, ‘crossoverRate’: 0.05, ‘mutationRate’: 3, ‘populationSize’: 20, ‘startTemperature’: 0.5}, repetition = 100, split, objective function = log difference
  2. aThe first number in the model name is the total number of free model parameters followed by the number of free parameters for each moiety and perturbations from the expert-derived model
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BMC Bioinformatics

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