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 Product 1  Substrate 2 Substrate 1 Product 2  Substrate 3 Product 3  Substrate 4 Product Enzyme 1 Enzyme 2 Enzyme 3 Enzyme 4

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 MAPKKK Stimulus MAPKK MAPK Cellular response

7. Our Work 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 Objective To study MAPK pathway and hence identify important molecules that are involved in 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 DatabaseMolecules in MAPK signaling pathway Interactions in MAPK signaling pathway KEGG179110 Protein Lounge12075 Cell Signaling155(90+35+30)120(60+25+35) Panther3644 BioModels34(26+8)30(20+10) NPID80(45+35)95(55+40)

9. Protein Lounge vs KEGG

10. ERK- MAPK Pathway JNK Pathway P38 Pathway ERK5 Pathway No. of molecules 297 No. of Interactions 160 MAPKMAPKK MAPKKK

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 Simeoni and 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 Identification of targets Perturbation study Identification of Key species Extraction of data Dependency & Interaction analysis Validation of model Network Composition in CNA

15. Logical Equations & Interaction Graphs Grb2-SOS RAS PKC Gap1m Grb2 -SOS PKCORAND 0000 0110 1010 1111 Activ ators !Gap 1m ORAND 00  110 01  000 10  111 11  010 Activators = Grb2-SOS + PKC RAS = Activators.!Gap1m PI3K 3 1 2 4 000 0 00 0010 000 11 1 1(+)2(+)3(-)4(+) Grb2-SOS PKC PI3K Gap1m RAS 0 0 01 0 1 (1+2)&!43 Grb2-SOS PKC PI3K Gap1m RAS Interaction Graph Interaction Hyper-graph 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 PI3K: Phosphoinositide 3 kinase

16. 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 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 Validation of model

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

18. Basic Topological Properties Species without any predecessors (Sources)37 Species without any successors (Sinks)18 Species connected to no reactions0 No. of +ve Feedback loops15 No. of –ve Feedback loops4 No. of strongly connected components22 Mutually Excluding pairs119 Enzyme Subsets4

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

20. Dependency Matrix & Shortest Path Analysis 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 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. Surface Molecules, Growth Factors, Ion channels Ambivalent Effect Inhibiting Effect Activating Effect Totally Inhibiting Effect Totally Activating Effect ‘y’ no. of molecules are influenced by ‘x’ ‘y’ no. of molecules influence ‘x’ X Axis: Molecule Y Axis: No. of molecules

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

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

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

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

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

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.