Reconstruction & Modeling of MAPKinase Signaling Pathway

1. Reconstruction & Modeling of MAPKinase Signaling Pathway Sonia Chothani (IAS-INSA-NASI Summer research fellow) Department of Biotechnology IIT Madras

2. Biochemical Pathways • Molecular interaction network of biological processes in a cell. • The major types of pathways we are looking into: • Metabolic • Signaling

3. Metabolic Pathways • In simple words it is a step by step modification of the initial molecule to shape it into another product Substrate 1 Enzyme 1 Product 1 Substrate 2 Enzyme 2 Product 2 Substrate 3 Enzyme 3 Product3 Substrate 4 Enzyme 4 Product

4. Signaling pathways • A mechanism that converts an extracellular signal to a cell into a specific cellular response Stimulus Receptor 2nd messengers Cellular Response

5. Why do we study them? • To understand the biochemical processes involved in the cell • Identify difference of mechanism between species • Molecular pathological studies, as in most of the diseases there is some change in these normal pathways • Our study is concentrated on the MAPK pathway

6. Mitogen Activated Protein Kinase pathway (MAPK) • Mitogens - Chemical substances that triggers mitosis • Ser/Thr specific protein kinases • Occurs in almost all kinds of cells • Responses like Proliferation, differentiation • Specific Mutations cause uncontrolled proliferation  Cancer • Hence studying this pathway can help understanding the progression of the disease Stimulus MAPKKK MAPKK MAPK Cellular response

7. Objective Our Work To study MAPK pathway and hence identify important molecules that are involved in Cancer • Reconstruction of MAPK Pathway • Mathematical Modeling of the reconstructed pathway • Identification of optimal intervention points (targets) and alternative paths • Connecting to other pathways related to cancer

8. Why do we need reconstruction? • Numerous signaling databases present online • Inconsistent & Incomplete data available on different databases. • We studied 13 databases for MAPK signaling pathway and cross-checked with >70 published papers

9. Protein Lounge vs KEGG

10. ERK-MAPK Pathway JNK Pathway P38 Pathway MAPKK MAPK MAPKKK ERK5 Pathway

11. Logical Steady State Analysis (LSSA) • Analogous to Flux Balance Analysis • Differential Equations Logical Equation (AND, OR, NOT operators) • Stoichiometry Matrix Interaction matrix • We used CellNetAnalyzer, a MATLAB supported software for the Logical Steady State Analysis of MAPK pathway

12. CellNetAnalyzer • Designed for structural & functional analysis of biochemical networks • Facilitates Logical Steady State Analysis

13. Standardization of software • Signaling Toy Network was simulated in CNA • Results were verified with published literature Steffen Klamt, Julio Saez-Rodriguez, Jonathan A Lindquist, Luca Simeoniand Ernst D Gilles, “A methodology for the structural and functional analysis of signaling and regulatory networks” BMC Bioinformatics 2006, 7:56 • Then we proceed to model MAPK pathway in CNA

14. Procedure we followed

15. Logical Equations & Interaction Graphs 2 1 3 PI3K Grb2-SOS: Growth factor receptor-bound protein – Son of Sevenless (GEF) complex PKC: Protein Kinase C Gap1m: RAS GTP-ase activating protein (GTP hydrolysis) Ras: GTP-ases 4 Grb2-SOS PKC Activators = Grb2-SOS + PKC RAS PI3K: Phosphoinositide 3 kinase Gap1m Interaction Graph Interaction Hyper-graph RAS = Activators.!Gap1m

16. Validation of model • Published Literature for verification • Example: PTEN is a tumor suppressor Akira Suzuki, José Luis de la Pompa, Vuk Stambolic, Andrew J. Elia “High cancer susceptibility and embryonic lethality associated with mutation of the PTEN tumor suppressor gene in mice” Current Biology 1998, 8:1169–1178 • EGFR an oncogene is kept on • 1) PTEN (tumor suppressor) is kept off => Uncontrolled Proliferation • 2) PTEN (tumor suppressor) is kept on => Controlled proliferation & Apoptosis

17. Effect on Transcription factors varying PTEN X axis: Pathway/Response Y Axis: No. of Transcription factors EGFR on PTEN off Uncontrolled proliferation EGFR on PTEN on Normal cellular processes

19. Interaction Matrix YL axis: Species X Axis: Reactions YR axis: (g/r/b) i has no effect on j i has an activating influence on j i has an inhibiting influence on j i is activated by j

20. No influence of i on j i has activating and inhibiting effect on j i is a pure inhibitor of j i is a pure activator of j i is an independent inhibitor of j i is an independent activator of j Dependency Matrix & Shortest Path Analysis X Axis: Species Y Axis: Species

21. Identification of Key Species • Interactions with more number of molecules • Influencing low number but crucial molecules • Transcription factors leading to important pathways/cellular responses • Published literature

22. Ambivalent Effect Inhibiting Effect Activating Effect Totally Inhibiting Effect Totally Activating Effect Surface Molecules, Growth Factors, Ion channels ‘y’ no. of molecules are influenced by ‘x’ X Axis: Molecule Y Axis: No. of molecules ‘y’ no. of molecules influence ‘x’

23. Ambivalent Effect Inhibiting Effect Activating Effect Totally Inhibiting Effect Totally Activating Effect Transcription factors, Output Molecules ‘y’ no. of molecules are influenced by ‘x’ X Axis: Molecule Y Axis: No. of molecules ‘y’ no. of molecules influence ‘x’

24. Ambivalent Effect Inhibiting Effect Activating Effect Totally Inhibiting Effect Totally Activating Effect Intermediate Molecules ‘y’ no. of molecules are influenced by ‘x’ X Axis: Molecule Y Axis: No. of molecules ‘y’ no. of molecules influence ‘x’

25. Transcription factors leading to significant effects on other pathways X axis: Pathway/Response Y Axis: No. of Transcription factors

26. No Influence Totally Activating Effect Ambivalent Effect Totally Inhibiting Effect Activating Effect Inhibiting Effect X Axis: Molecule Y Axis: No. of molecules Influence on other species i.e.; ‘y’ no. of molecules get Influenced by ‘x’ Transcription Factors, Outputs Growth Factors, Surface Proteins, Inputs Intermediate Molecules Influenced by other species i.e.; ‘y’ no. of molecules Influence ‘x’

27. Perturbation Study IL-1/TNF-alpha Caspase-3 Apoptosis Independent pathway to apoptosis Need to prevent this inhibition JNK pathway Proliferation and Apoptosis PKB/AKT P38 pathway B-raf Uncontrolled Proliferation NKX-3 Better targets because stops uncontrolled proliferation Just negatively regulates so not a beneficial reaction to target B-Raf MEK1 ERK Proliferation

28. Linking with metabolic pathways • Transcription factors lead to cellular responses but undergo other processes which might regulate the response • TNFR, MEKK1, TPI2, TAK1 have an activating influence and PKB/AKT has an inhibiting influence on NF-KB • NF-KB (TF) is one of the regulators for IDH1(Isocitrate dehydrogenase-1) • IDH1 decarboxylates isocitrate to 2-oxoglutarate (TCA cycle) • Hence we can further see effects on this metabolic pathway

29. Conclusion • This kind of a study is important to identify important molecules and related sub-pathways for further experimental study • Identifies possible alternative pathways hence identifies optimal intervention points (targets) Further Work • Transcription factors need not be the output molecules, we need to consider detailed downstream paths. • We would like to further combine this pathway with other cancer related pathways (even some metabolic) to be able to confirm our conclusions and similarly identify more targets.

30. Dr. Ram Rup Sarkar and Dr. Somdatta Sinha for the continuous guidance and all the patience. I would also like to thank Dr. C Suguna and all other lab members for all the support and discussions. Last but not the least I would like to thank CCMB and IAS-NASI-INSA for giving me this great opportunity.