Appendix A. Proof of Proposition 1
Let us denote the Lyapunov (energy) function of perturbative gene regulatory network in equation (4) as
T
[ ] X [ ] [ ] , for some symmetric positive definite matrix P
P T
0 .
[
1]
V X t
X
T
[ t
1] [
T
X [ ] [ ]
X
T t A (1
)
T
X
T t
A (1
)
T
T
(1 ) [ ] X [ ] [ ]
(1
)
P
[ ]
0
(1
)
T
(1
) P 0
Based on quadratic stability [29], if the LMI in equation (5) holds, then the perturbative network is robustly stable.
Appendix B. Proof of Proposition 2
Let us denote the Lyapunov function of gene regulatory network under external stimulation signals in equation (7) as
T
[ ] X [ ] [ ] , for some symmetric positive definite matrix P
P T
0 . We have
[
1]
X
T
[ t
1] [
1]
ˆ
[ ]
[ ]
ˆ
[ ]
[ ]
X
T
[ ]
ˆ
T
ˆ
[ ]
X
T
[ ]
ˆ
T
[ ]
T
[ ]
ˆ
[ ]
T
[ ] [ ]
T
T
[ ]
ˆ
T
ˆ
[ ]
T [ ]
ˆ
T [ ]
[ ]
ˆ
[ ]
T [ ] [ ]
2 T
U t U t
T
Y t Y t
2 T
[ ] [ ]
T
[ ]
ˆ
T
ˆ
[ ]
T
[ ]
ˆ
T
[ ]
[ ]
ˆ
[ ]
T
[ ] [ ]
T T
[ ]
2 T
[ ] [ ]
T
[ ] [ ]
T
[ ] [ ]
2 T
[ ] [ ]
1
If the following inequality holds
X
T
[ ]
ˆ
T
ˆ
[ ]
X
T
[ ]
ˆ
T
[ ]
U
T
[ ]
ˆ
[ ]
U
T
[ ] [ ]
T
X [ ]
T
[ ]
2
U
T
[ ] [ ]
X
T
[ ] [ ]
0 then, we get
[
1]
T
[ ] Y [ ] [ ]
2
U
T
[ ] [ ] inequality holds:
X
T
[ ]
ˆ
T
ˆ
[ ]
X
T
[ ]
ˆ
T
[ ]
U
T
[ ]
ˆ
[ ]
U
T
[ ] [ ]
T
X [ ]
T
[ ]
2
U
T
[ ] [ ]
X
T
[ ] [ ]
0
The above inequality can be equivalent to the following inequality
X
T
ˆ
T
ˆ T
P
ˆ
T
P
2
I
0
(A1)
So we sum the above inequality from 0 to ∞ to get
[0]
t
0
T
Y [ ] [ ]
2 t
0
T
U [ ] [ ]
t
0
Y
T
[ ] [ ] t
0
Y
T
[ ] [ ]
[0]
2 t
0
T
U [ ] [ ]
t
0
T T
Y [ ] [ ] X [0] PX [0]
2 t
0
T
U [ ] [ ]
(A2) which is the inequality of response ability in equation (9) in the case X [0]
0
t
0
T
2 t
0
T
t
0 t
0
T
T
2
The above results hold only when the inequality (A1) holds, i.e., the following
(A3)
The inequality (A3) holds for all [ ] and [ ] if and only if the following LMI holds.
ˆ
T
ˆ T
P
ˆ
T
2
P I
0
2