Signal Discrimination in Cells Through A Negative Feedback
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The document explores signal discrimination in cellular signaling networks, particularly through the negative feedback mechanisms involving the S6K1 and AKT pathways. Experiments confirm the presence of adaptive network motifs, demonstrating how different signals can yield varying outputs despite sharing the same pathways. The findings indicate that negative feedback filters slow inputs and differentially affects mitogenic responses to IGF-1 and insulin.
Signal Discrimination in Cells Through A Negative Feedback
- 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