| Computed coefficients | Estimated coefficients |
---|
a10
| 0 | 0.0000 |
a11
| -14.6780 | -14.3647 |
a12
| 0 | -0.1466 |
a13
| 7.3390 | 7.3414 |
a14
| 0 | -0.2165 |
a15
| -7.3390 | -7.1723 |
a20
| 0 | 0.0000 |
a21
| 14.6780 | 14.6119 |
a22
| -14.6780 | -14.6540 |
a23
| 0 | -0.0009 |
a24
| 0 | 0.0494 |
a25
| 0 | -0.0309 |
a30
| 0 | 0.0000 |
a31
| 0 | -2.3527 |
a32
| 0 | 1.3989 |
a33
| -27.2517 | -27.9204 |
a34
| 0 | 1.7491 |
a35
| 0 | -0.9955 |
a40
| 0 | 0.0000 |
a41
| 0 | 2.0843 |
a42
| 0 | -1.0925 |
a43
| 18.5664 | 19.0295 |
a44
| -18.5664 | -20.2112 |
a45
| -9.2832 | -8.3594 |
a50
| 0 | 0.0000 |
a51
| 0 | -0.4026 |
a52
| 0 | 0.1384 |
a53
| 0 | -0.0059 |
a54
| 18.5664 | 18.8987 |
a55
| -18.5664 | -18.7852 |
- Regression coefficients for the small gene network (Figure 1), linearized about the steady state and based on relative deviations (option II). The first and second columns contain the computed and estimated regression coefficients, respectively. The regression coefficients a
ij
refer to the influence of variable j on variable i, while ai 0is the constant term in each regression model. As the table indicates, the correspondence is good, except for the coefficients relating to X3 and X4 (see Text for explanation). 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.