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.