1

Kami (Knowledge Aggregator and Model Instantiator) A graph rewriting pipeline from the biological

literature to rule-based models.

Adrien Basso-BlandinRussell Harmer

June 24th,ENS, Lyon

2

Biological knowledges

Problematic :• Large and complex systems• Multiple interaction levels• Literature and data fragmented• Studied piecewise in specific contexts• Inconsistent data in some cases

Objectives :• Understand biological processes globally• Aggregate knowledges into big mechanistic models• Infer new knowledges from models, graph analysis and

simulations

3

Big Mechanism

BDD

Litterature

Data fragments

Knowledge sources Biological data

(Nuggets)

Knowledge representation

SimulationLitteratureLiterature

BDDBDD

Extraction CompilationAggregation

4

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

5

Knowledge representation pipeline

Nuggets Action Graph

Simulation

Mechanistic rules

Model constraints

Logical contact graph

Ontology

instantiation rules

Meta-model : Layer-graph

Graph rewriting rules

• Graph patterns ?• DL logic

• Meta-model : Layer-graph

• Nuggets projection

Projection

compilation

Projection

• Meta-model : Site graph

• Nuggets projection

traduction

6

Nuggets

7

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

8

Biological nuggets

1. “EGFR binds the SH2 domain of Grb2 provided that EGFR is phosphorylated and residue 90 of Grb2 is a serine”

2. “Shc binds Grb2 on the Sh2 domain provided that Shc has a phosphorylated tyrosine”

3. “Grb2 can dimerize”4. “Shc tyrosine phosphorylation has a positive influence on Grb2

dimerization” 5. “Shc concentration has a negative influence on EGFR – Grb2 binding”

#### Signatures

%agent: EGFR(s2~u~p,s3)

%agent: Shc(y~u~p,s1)

%agent: Grb2(sh2,s4,s5)

#### Rules

EGFR(s2~p,s3),Grb2(sh2) -> EGFR(s2~p,s3!1),Grb2(sh2!1)@

Shc(y~p,s3),Grb2(sh2) -> Shc(y~p,s3!1),Grb2(sh2!1)@

Shc(y~u) -> Shc(y~p)@

Grb2(s4),Grb2(s5) -> Grb2(s4!1),Grb2(s5!1)@

#### Variables

#### Initial conditions

Kappa

• Site graphs• Graph rewriting rules• Modeling protein-protein interaction• Complete signature of each agents (protein)

Rules based modeling

Grb2EGFR Bnd sh2

Shc

P:ph

yP:ph Bnd

Mod

Bnd

Grb2EGFR Bnd

+

sh2

aa:S

S90Loc:90

Shc

P:ph

yaa:y

P:ph Bnd

Mod

Bnd

-

9

Biological nuggets

1. “EGFR binds the SH2 domain of Grb2 provided that EGFR is phosphorylated and residue 90 of Grb2 is a serine”

2. “Shc binds Grb2 on the Sh2 domain provided that Shc has a phosphorylated tyrosine”

3. “Grb2 can dimerize”4. “Shc tyrosine phosphorylation has a positive influence on

Grb2 dimerization” 5. “Shc concentration has a negative influence on EGFR – Grb2

binding”

Grb2EGFR Bnd sh2

aa:S

S90Loc:90

P:ph

1

Grb2sh2Shc

yaa:y

P:ph

Bnd2

Grb2

Bnd3

Grb2

+

Shc

yP:ph

BndMod

4

Shc

-

EGFR

Grb2Bnd

5

Partial mechanisticinformations

Influence(correlation)

10

Meta-model

loc aa

Is_bnd

bnd_rc

brk_rc

mod_rc

Agent MODBND

reg

res

flag

st

s

int

Flags -> impact the system during the simulation

Attributes -> impact the system during instantiation≠

∈ [𝑎 − 𝑧]

∈ {𝑢, 𝑝}∈ 𝐵𝑜𝑜𝑙

∈ ℝ

11

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

12

Graph rewriting rules

Example :• Rule for complexes

• Rule for families

Ac_complex

A CA C

A C

AAc_complex

A1G

A2 C A3T

F:A

A1G

A2 C A3T

F:A

G

C

T

F:A

G

C

T

13

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

14

Constraints on the model

Grb2EGFR Bnd

+

sh2

aa:S

S90Loc:90

Shc

P:ph

yaa:y

P:ph Bnd

Mod

Bnd

-

15

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

16

Biological meta-data

• “EGFR binds the SH2 domain of Grb2 provided that EGFR is phosphorylated and residue 90 of Grb2 is a serine”

• “Shc binds Grb2 on the Sh2 domain provided that Shc has a phosphorylated tyrosine”

• “Grb2 can dimerize”• “Shc tyrosine phosphorylation has a positive influence on

Grb2 dimerization” • “Shc concentration has a negative influence on EGFR – Grb2

binding”

Grb2EGFR Bnd

+

sh2

aa:S

S90Loc:90

Shc

P:ph

yaa:y

P:ph Bnd

Mod

Bnd

-

t:sh2Bnd

Rate:2,1

T:phos

t:sh2Bnd

17

Nuggets aggregation

18

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

19

“hand made” knowledge Aggregation

• “EGFR binds the SH2 domain of Grb2 provided that EGFR is phosphorylated and residue 90 of Grb2 is a serine”

• “Shc binds Grb2 on the Sh2 domain provided that Shc has a phosphorylated tyrosine”

• “Grb2 can dimerize”• “Shc tyrosine phosphorylation has a positive influence on

Grb2 dimerization” • “Shc concentration has a negative influence on EGFR – Grb2

binding”

Grb2EGFR Bnd

+

sh2

aa:S

S90Loc:90

Shc

P:ph

yaa:y

P:ph Bnd

Mod

Bnd

-

1. Nuggets aggregation and correctness2. Diversity of knowledges (non-homogeneous)3. Sparse models

Action graph

20

Semantic check

Mods t Bb1

type:phosPhos:1

type:kinase

??

C Binds t Bb1

[50-52]

?

?

DBinds t

• Verification over amino acid• Verification of sequence inclusion• Verification of action type / coherence

• Over binds• Over modification actions

• Verification of sequence overlapping / edge conflict• Domain specific action aggregation

K[1-122]

[50-52]

51

Aa:y

D

BBinds t

[40-52]

[30-47]Binds t

#

! work in progress !

• Ontology representing :• the formal model• Biological constraints

• DL constraints for structural integrity and automated inferences

21

Action aggregation

aa:yEGFR

Y

Loc:1092Phos:1 BNDs s

Grb2

• EGFR binds Grb2 on the SH2 domain provided EGFR is phosphorylated on Y1092• tyrosine-phosphorylated Shc binds the SH2 domain of Grb2

aa:S

pYLoc:90

Phos:1

SH2

aa:ySHC

YPhos:1

BNDs s

22

Compilation

23

Knowledge representation pipeline

NuggetsAction Graph

Simulation

Mechanistic rules

Model constraints

B

A

y445

M

B

B

Logical contact graph

+ Meta-Data

K

pinstantiation rules

?

...

Multiple instances of model

(site graph)

• Semantic Check (C.Froidevaux )

• Inference (F.Fages)

24

Logical contact graph

A logical graph :• Actions• Agents• Mechanistic edges• key residues/region instantiation• Virtual sites added

Grb2EGFR sh2

aa:S

S90Loc:90

Shc

yaa:y

P:ph Bnd

Mod

Bnd

User choice

User choice?

? ?

?

?P:ph

Nuggets modelLogical contact graph

25

Attributes instantiation

Grb2EGFR sh2

aa:S

S90Loc:90

Shc

yaa:y

P:ph Bnd

Mod

Bnd

User choice

User choice?

? ?

?

?P:ph

Aa:{y,t,u}

Loc:[75,95]

26

From Lcg to Simulation

Grb2EGFR

Bnd

sh2

Shc

yP:p Bnd

Mod

s3

s4 s5

s1

s2P:p

#### Signatures

%agent: EGFR(s2~u~p,s3)

%agent: Shc(y~u~p,s1)

%agent: Grb2(sh2,s4,s5)

#### Rules

EGFR(s2~p,s3),Grb2(sh2) -> EGFR(s2~p,s3!1),Grb2(sh2!1)@

Shc(y~p,s3),Grb2(sh2) -> Shc(y~p,s3!1),Grb2(sh2!1)@

Shc(y~u) -> Shc(y~p)@

Grb2(s4),Grb2(s5) -> Grb2(s4!1),Grb2(s5!1)@

#### Variables

#### Initial conditions

Egfr,Egfr.s2,Egfr.s2.p=p,Egfr.s3,Grb2,Grb2.sh2

Shc,Shc.y,Shc.y.p=p,Shc.s1,Grb2,Grb2.sh2

Grb2,Grb2.s4,Grb2.s5

Shc,Sch.y,Shc.y.p

F(P) = {u->p}

Rc:0.3

Rc:0.20.3

0.3

0.3

27

Knowledge inference

! work in progress !

28

Inferences

BDD

Litterature

Data fragments

Knowledge sources Biological

data

Knowledge representation

SimulationLitteratureLiterature

BDDBDD

inferenceRefinement

Semantic check

Extraction CompilationAggregation

inference

29

Knowledge inference

BA

y445

??

+

C-

Ac_complex

?

2 kind of knowledge

mechanistic rules Behaviours

A bind B C has a negative influence on B

Ph : Bind(A,B)

Problematic : • How to automatically infer formal rules from assertions ?

Approach : • Represent the model as a formula.• Derivating Graph from formula• Using sat Solver over formula.

• Use a proof assistant approach !

30

Knowledge inference example

BA

y445

Ph

??

+

C-

Ac_complex

?

1. Phosphorylation of B has a positive influence on its homodimerization

2. C is a known inhibitor of B homodimerization3. C is often found in complex with A

?

mechanistic rules assertions

∃ 𝐴, 𝐶, 𝑥, 𝑦, 𝑎𝑔𝑒𝑛𝑡 𝐴 ,a𝑔𝑒𝑛𝑡 𝐶 , 𝑠𝑖𝑡𝑒 𝐴, 𝑥 ,𝑠𝑖𝑡𝑒 𝐶, 𝑦 , 𝑏𝑖𝑛𝑑(𝑥, 𝑦)

𝐶𝑜𝑚𝑝𝑙𝑒𝑥(𝐴, 𝐶)

∃𝑏1, 𝑏2, 𝑥, 𝑦, 𝑏1: 𝐵, 𝑏2: 𝐵,𝑎𝑔𝑒𝑛𝑡 𝑏1 , 𝑠𝑖𝑡𝑒 𝑏1, 𝑥 ,𝑠𝑖𝑡𝑒 𝑏2, 𝑦 , 𝑎𝑔𝑒𝑛𝑡 𝑏2 ,𝑝ℎ𝑜𝑠 𝑥 → 𝑏𝑖𝑛𝑑 𝑥, 𝑦

𝑃ℎ𝑜𝑠 𝐵++> [𝑏𝑖𝑛𝑑 𝐵, 𝐵 ]

∃𝑏1, 𝑏2, 𝑥, 𝑦, 𝐶, 𝑎𝑔𝑒𝑛𝑡 𝑏1 ,𝑎𝑔𝑒𝑛𝑡 𝑏2 , 𝑏1: 𝐵, 𝑏2: 𝐵, … ?

𝐶 −→ [𝑏𝑖𝑛𝑑 𝐵, 𝐵 ]?

Ph : Bind(A,B)

PhBind : Phos(y445)

31

Collaborations

BDD

Litterature

Data fragments

Knowledge sources Biological

data

Knowledgerepresentation

SimulationLitteratureLiterature

BDDBDD

inferenceRefinement

Semantic check

Extraction CompilationAggregation

inference

?(James Allen

Team)

Human reading

Jérôme Feret(3)

François Fages(4)

Walter Fontana(1)

Hector Medina(1)

Pierre boutilier(1),Jean Krivine(2),Jérôme Feret(3)

Christine Froidevaux(5)

Conclusion

John Bachman(1)

1. Harvard Medical School2. IRIF (PPS)3. ENS Paris4. Inria Saclay5. LRI Orsay

32

Long term objectives ?

The

sisPostDoc

PostDoc

33

Thank you