Several growth factors, cytokines, hormones activate PI3K/Akt pathway. Akt is a key node in this pathway and activates different downstream paths. One such path is Akt/mTORC1/S6K1 that controls protein synthesis, cell survival, and proliferation. Here we show that a negative feedback controls activation of S6K1 through this pathway. Due to this negative feedback, a sustained phospho-Akt signal generates a transient pulse of phospho-S6K1. We have created a mathematical model for this circuit. Analysis of this model shows that the negative feedback acts as a filter and preferentially allows a signal, with sharp and faster rise of phospho-Akt, to induce pronounced activation of S6K1.

1. Signal Discrimination
Through A Negative Feedback
Vimalathithan Devaraj
PhD student
Under the guidance of Dr. Biplab Bose
IIT Guwahati

2. Information Transfer Through Molecular Pathway
Fig. ref: PMID 18846202

3. Hourglass Architecture of Cell Signaling

4. The Key Question:
How does a network motif helps to
discriminate different signals ?

5. Same Path, Different Signals, Different Outputs
QuantitativeWestern Blot : MCF-7 cells

6. How does S6K1 Deciphers The Information From AKT ?

7. Signaling Path from Akt to S6K1 Has Adaptive Behavior
Quantitative western blot : MCF-7 cells

8. Finding The Network Motif
Possible adaptive network motifsAdaptive Signaling
Through Experiments we have confirmed the presence of Motif II in this system

9. Variants of Motif II
Buffer molecules X, Y and Z were used to introduce Delay in the Feedback

10. Equations used to Model the Negative Feedback
. .(( 6 ) ( 6 )) .( 6 )( 6 3
3
) 1 4.
(( 6 ) ( 6
(
( ))) ( 6 )2
)
5
k S K pS K k pS Kd pS K T
dt k S K pS K k pS KT
p t
k C
Ak k n
n n
−
= −
+ − + +
.( 6 ).(( ) ( )) .( )( ) 6 8
(( ) ( )) ( )7 9
k pS K X pX k pXd pX T
dt k X pX k pXT
−
= −
+ − +
10 12
11 13
1514
16 17
.( ).(( ) ( )) .( )( )
(( ) ( )) ( )
.( ).( ).((Z) ( ))( )
((Z) ( )) ( )
T
T
T
T
k pX Y pY k pYd pY
dt k Y pY k pY
k pZk pY pZd pZ
dt k pZ k pZ
Where pX or pY or pZ depending onthe model varC iant
−
= −
+ − +
−
= −
+ − +
=

11. Finding The Right Model
Model -2Log(L) AIC
Akt-S6K1 10.83 61.27
Akt-S6K1-X -9.27 54.33
Akt-S6K1-X-Y -11.19 67.05
Akt-S6K1-X-Y-Z -18.04 71.05
Model Statistics of data used in
parameter estimation :
Modeling tool: Data2Dynamics (MATLAB package)

12. Finding The Most Predictive Model
Input Data
Sum of Square Error (SSE)
Akt-S6K1-X Akt-S6K1-X-Y
IGF1–2.5 nM 0.020 0.011
Insulin 2.5 nM 0.025 0.016
IGF1-2.5 nM +
Insulin 2.5 nM
0.024 0.009
Model Statistics of validation data
(NOT USED in parameter estimation):
Model variant Akt-S6K1-X-Y is selected
for further analysis

13. Fitting of The Selected Model - Parameter Estimation
Input-pAktOutput-pS6K1
Filled circles: Experimental data; Black line: Model Predicted data

14. Validation of The Selected Model
Input-pAktOutput-pS6K1
Filled circles: Experimental data; Black line: Model Predicted data

15. Simulations to Understand The Negative Feedback

16. The NF Filters Delayed, Slow Input
.
n
n n
t
pAkt(t) a
K t
=
+

17. The NF Filters Delayed, Slow Input

18. The NF Filters, Fast Oscillatory Signal

19. Cell Proliferation Correlates with Signal Dynamics
r = 0.89

20. In Brief
▪ Dynamics of S6K1 is controlled by
Negative Feedback
▪ Dynamics of S6K1 is controlled by
dynamics of Akt irrespective of
signaling molecule
▪ The NF involving S6K1 acts like a
filter in PI3K/Akt/mTOR pathway
▪ NF explains the differential
mitogenic effect of IGF-1 and Insulin

21. biplabbose@iitg.ac.in
@bose_biplab