Biocuration and rule-based modelling of protein interaction networks - - PowerPoint PPT Presentation

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Biocuration and rule-based modelling of protein interaction networks - - PowerPoint PPT Presentation

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Biocuration and rule-based modelling of protein interaction networks in KAMI Sbastien Lgar, Eugenia Oshurko and Russ Harmer LSB 2018 - Oxford July 13, 2018 Introduction What does K A M I stand for? K nowledge A ggregator and M odel I

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Biocuration and rule-based modelling

  • f protein interaction networks in KAMI

Sébastien Légaré, Eugenia Oshurko and Russ Harmer LSB 2018 - Oxford July 13, 2018

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What does K A M I stand for?

Knowledge Aggregator and Model Instantiator

What is special about KAMI ?

  • Rule-based strategy
  • Incremental aggregation of large models
  • Allows a posteriori understanding of models

Introduction

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Outline

2/21 KAMI

1) KAMIStudio (KAMI GUI) 2) Knowledge representation ( Nugget / ActionGraph ) 3) Building a model

Kappa

4) Rule-based executable model 5) Pathway discovery ( Causality analysis ) 6) The pYnet model

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Knowledge representation

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Split in two layers, the nuggets and action graph

  • Unambiguously specify interactions
  • Limited set of symbols

Can read a model using KAMIStudio Every nugget is independent

  • Facilitates incremental aggregation
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Building a KAMI model

Binding( RegionActor( gene=Gene(uniprot_ac="P00519", hgnc_symbol="ABL1"), region=Region(name="SH2", interproid="IPR000980") ), SiteActor( gene=Gene(uniprot_ac="P11274", hgnc_symbol="BCR"), site=Site(name="pY246", residues=[Residue(aa="Y", loc=246, state=State("phosphorylation", True))]) ), rate=0.001, desc="ABL1 binds BCR-Y246" )

KAMI interaction (programmatic) KAMI nugget (graph representation)

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How does it work?

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N : Nugget A : Action Graph

→

Arrow : Typing

KAMI graph hierarchy

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Typing

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Typing

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Typing

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Typing

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Semantic nuggets

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Knowledge aggregation

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Knowledge aggregation

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Knowledge aggregation (continued)

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Knowledge aggregation (continued)

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Biocuration with KAMI

Allows users to easily add new data

  • Detects elements already present in action graph
  • Semantic checks
  • Completes interaction if more detailed
  • Ignores new data if it already exists
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Kappa rule-based model

KAMI nugget Kappa rule

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ABL1(SH2[./1]), BCR(pY246[./1] Y246_phos{True})

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1 nugget ≈ 1 rule (info from AG)

IFGR1(pY1281[./1] Y1281_phos{True}), ABL1(SH2[./1]) IFGR1(pY1280[./1] Y1280_phos{True}), ITK(SH2[./1])

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1 nugget ≈ 1 rule (info from AG)

Nuggets Action Graph

IFGR1(pY1281[./1] Y1281_phos{True}), ABL1(SH2[./1]) IFGR1(pY1280[./1] Y1280_phos{True}), ITK(SH2[./1])

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1 nugget ≈ 1 rule (info from AG)

Nuggets Action Graph

IFGR1(pY1280-1[./1] Y1281_phos{True}), ABL1(SH2[./1]) IFGR1(pY1280-1[./1] Y1280_phos{True}), ITK(SH2[./1])

=

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Simulations with KaSim

  • Rule-based
  • Deals with combinatorial

complexity

  • Quantitative
  • Stochastic
  • No spatial dimension
  • Analysis system

dynamics

ABL1 BCR FES SHC1 SYK

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Causality Analysis (KaStor)

Expected pathway EGFR dimerizes and recruits GRB2

Recruited GRB2

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Causality Analysis (KaStor)

Discovered pathway ZAP70 involved in GRB2 recruitment

Recruited GRB2

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The pYnet model

Cell signaling

  • Tyrosine phosphorylation
  • SH2 domain bindings

900 interactions extracted from

  • PhosphoSite
  • Phospho.ELM
  • NCI Pathway Interaction Database

Well suited to showcase rule-based modelling

  • Combinatorial complexity
  • Large
  • Scaffolding

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Important combinatorial compl.

Processive phosphorylation

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Summary

KAMI allows

  • Representation of individual interactions
  • Aggregation into an interaction network

KAMI works with Kappa to

  • Produce dynamic simulations
  • Discover pathway using causality analysis

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Summary

Fundamentally different approach to modelling

  • No need to know exactly where new data fits
  • Can just “smash” interactions together
  • No need to explicitly build the pathways (bias)
  • Can discover the pathways through analysis

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Using KAMI and Kappa

In development KAMI: github.com/Kappa-Dev/KAMI KAMIStudio: github.com/Kappa-Dev/KAMIStudio Graph rewriting ReGraph: github.com/Kappa-Dev/ReGraph Kappa KaSim: github.com/Kappa-Dev/KaSim.git

Web Site: kappalanguage.org 20/21

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Thanks

Walter Fontana Pierre Boutillier Hector Medina Ioana Cristescu Jérôme Feret Vincent Danos Yves-Stan Le Cornec Jean Krivine Russ Harmer Eugenia Oshurko

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