reconstruction & modeling of mapkinase signaling pathway
DESCRIPTION
Reconstruction & Modeling of MAPKinase Signaling Pathway. Sonia Chothani (IAS-INSA-NASI Summer research fellow) Department of Biotechnology IIT Madras. Biochemical Pathways. Molecular interaction network of biological processes in a cell. The major types of pathways we are looking into: - PowerPoint PPT PresentationTRANSCRIPT
Reconstruction & Modeling of MAPKinase
Signaling Pathway
Sonia Chothani(IAS-INSA-NASI Summer research fellow)
Department of BiotechnologyIIT Madras
Biochemical Pathways Molecular interaction network of biological
processes in a cell.
The major types of pathways we are looking into: Metabolic Signaling
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
Signaling pathways A mechanism that converts an extracellular
signal to a cell into a specific cellular response
Stimulus
Receptor
2nd messengers
Cellular Response
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
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
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
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
Database Molecules in MAPK signaling pathway
Interactions in MAPK signaling pathway
KEGG 179 110Protein Lounge 120 75Cell Signaling 155(90+35+30) 120(60+25+35)
Panther 36 44BioModels 34(26+8) 30(20+10)
NPID 80(45+35) 95(55+40)
Protein Lounge vs KEGG
ERK-MAPK Pathway
JNK Pathway
P38 Pathway
ERK5 Pathway
No. of molecules
297
No. of Interactions
160
MAPKMAPKKMAPKKK
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
CellNetAnalyzer Designed for structural & functional analysis
of biochemical networks
Facilitates Logical Steady State Analysis
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
Procedure we followed
Logical Equations & Interaction GraphsGrb2-SOS
RAS
PKC
Gap1m
Grb2-SOS
PKC OR AND
0 0 0 0
0 1 1 0
1 0 1 0
1 1 1 1
Activators
!Gap1m
OR AND
0 0 1 1 0
0 1 0 0 0
1 0 1 1 1
1 1 0 1 0
Activators = Grb2-SOS + PKC
RAS = Activators.!Gap1m
PI3K3
1
2
4
-1 0 0 0
0 -1 0 0
0 0 1 0
0 0 0 -1
1 1 -1 1
1(+) 2(+) 3(-) 4(+)
Grb2-SOS
PKC
PI3K
Gap1m
RAS
-1 0
-1 0
0 1
-1 0
1 -1
(1+2)&!4 3
Grb2-SOS
PKC
PI3K
Gap1m
RAS
Interaction Graph Interaction Hyper-graph
Grb2-SOS: Growth factor receptor-bound protein – Son of Sevenless (GEF) complexPKC: Protein Kinase CGap1m: RAS GTP-ase activating protein (GTP hydrolysis)Ras: GTP-ases
PI3K: Phosphoinositide 3 kinase
EGFR an oncogene is kept on1) PTEN (tumor suppressor) is kept off
=> Uncontrolled Proliferation2) 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
Effect on Transcription factors varying PTEN
Uncontrolled proliferation
Normal cellular processes
X axis: Pathway/ResponseY Axis: No. of Transcription factors
EGFR on PTEN off
EGFR on PTEN on
Basic Topological Properties
Species without any predecessors (Sources) 37
Species without any successors (Sinks) 18
Species connected to no reactions 0
No. of +ve Feedback loops 15
No. of –ve Feedback loops 4
No. of strongly connected components 22
Mutually Excluding pairs 119
Enzyme Subsets 4
Interaction Matrix
i has no effect on j
i is activated by j
i has an activating influence on ji has an inhibiting influence on j
YL axis: SpeciesX Axis: ReactionsYR axis: (g/r/b)
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: SpeciesY Axis: Species
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
Surface Molecules, Growth Factors, Ion channelsAmbivalent 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
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
Intermediate MoleculesAmbivalent 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
Transcription factors leading to significant effects on other pathways
X axis: Pathway/ResponseY Axis: No. of Transcription factors
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, OutputsIntermediate Molecules
X Axis: Molecule Y Axis: No. of molecules
IL-1/TNF-alpha Caspase-3 Apoptosis
Independent pathway to apoptosis
PKB/AKT
JNK pathway
B-rafNKX-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
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
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.
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.