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Table 3 Comparison of the different linearization options (I, II and IV)

From: Priming nonlinear searches for pathway identification

 

I. Absolutedeviation

II. Relative deviation

IV. Lotka-Volterra

a10

0.0000

0.0000

14.4748

a11

-14.3647

-14.3647

-18.9581

a12

-0.1466

-0.1466

-0.6836

a13

5.3878

7.3414

7.3367

a14

-0.1712

-0.2165

-0.4694

a15

-5.6702

-7.1723

-7.4981

a20

0.0000

0.0000

0.0144

a21

14.6119

14.6119

19.8910

a22

-14.6540

-14.6540

-19.9277

a23

-0.0006

-0.0009

-0.0001

a24

0.0390

0.0494

0.0472

a25

-0.0245

-0.0309

-0.0335

a30

0.0000

0.0000

26.4020

a31

-3.2058

-2.3527

2.8725

a32

1.9062

1.3989

-1.7989

a33

-27.9204

-27.9204

-26.6164

a34

1.8842

1.7491

-1.5871

a35

-1.0724

-0.9955

0.9692

a40

0.0000

0.0000

8.0270

a41

2.6365

2.0843

6.3364

a42

-1.3820

-1.0925

-4.1579

a43

17.6654

19.0295

19.0005

a44

-20.2112

-20.2112

-23.1319

a45

-8.3594

-8.3594

-7.7047

a50

0.0000

0.0000

0.0869

a51

-0.5092

-0.4026

-0.6617

a52

0.1751

0.1384

0.4441

a53

-0.0055

-0.0059

-0.0003

a54

18.8987

18.8987

20.2939

a55

-18.7852

-18.7852

-20.2152

  1. Estimated coefficients for three of the linearization approaches: absolute deviation from steady state (left column), relative deviation from steady state (center column) and Lotka-Volterra linearization (right column). The dataset consisted of 401 data points in the interval [0,4] and resulted from a simulation in which X3 was perturbed at t = 0 to a value 5% above its steady-state value.