Executable Knowledge for rule-based modelling of cellular signalling networks
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1 Executable Knowledge for rule-based modelling of cellular signalling networks Russ Harmer (CNRS & ENS Lyon) + Adrien Basso-Blandin (ENSL) & Walter Fontana (HMS) + John Bachman (HMS) & Pierre Boutillier (HMS) & Lucian Galescu (IHMC) & Ben Gyori (HMS)
2 Context
3 Cellular signalling Decentralized coordination of tissue formation and maintenance extra-cellular ligands trigger intra-cellular signalling athways to control cell growth, death, division, Perturbed in disease states, e.g. cancer, diabetes, kinetic erturbations: over-exression, knock-outs, causal erturbations: mutations, truncations,
4 Why model signalling? Signalling networks, and their literature, are mind-bogglingly comlicated 1000s of roteins and 10s (or even 100s) of thousands of PPIs [rotein-rotein interactions] emirical knowledge is fragmented and scattered across a vast literature imossible to work out in your head
5 Traditional modelling Modelling as a rimarily mental activity identify the key variables and their inter-deendencies under standard erturbations amounts to making model-level assumtions during model construction and debugging model as synthesis of understanding
6 Traditional modelling To ut it differently: the modelling is done in your head the model is an artifact that emerges fully-formed and is just written down the model is then debugged if it fails to meet your mental secification
7 Modelling signalling Signalling needs models as tools for discovery a formal reification of the modelling rocess [rather than just the resulting artefact] as an audit trail recisely in order to achieve an understanding reverse-engineer a secification by combining emirical knowledge with (biological) inference
8 Modelling signalling Models must therefore be easily extensible and modifiable [since emirical knowledge is always changing] be arbitrarily erturbable [since we wish to discover, not hard-wire, their effects] incororate emirical and inferred knowledge [at various levels of detail]
9 Rule-based modelling Formal reresentation for the (10s? of 1000s of) rotein-rotein interactions (PPIs) in signalling grah rewriting formalism scalable stochastic simulation athways as causal traces Handles kinetic, but not causal, erturbations
10 Serendiity
11 The cognitive barrier Have to read many aers to find various fragments of knowledge about a single PPI many different uzzle ieces, at varying levels of detail, that must [somehow] be assembled into rules the effects of causal erturbations must be hard-wired by enumerating all cases [rather than emerging] not scalable for a human curator [believe me, I ve done it]
12 Big Mechanism Seeks causal exlanations of comlex system behaviour [not just correlations] Machine reading of aers, automatic assembly into models that yield causal exlanations The chosen use case: signalling athways in cancer!
13 Breaching the cognitive barrier
14 Assembly Big Mechanism aims to make reading scalable and RBM rovides causal exlanations once your PPIs have been formalized as rules The hard roblem is assembly combining fragments of knowledge into rules in such a way that (aarently) conflicting information can be accommodated and the effects of causal erturbations emerge
15 KAMI knowledge aggregator & model instantiator Uses a grah-based reresentation of PPIs a grah with two directed edge structures, resecting a meta-model: bnd? res I O BND agent flag MOD I O BRK reg uses grah rewriting to udate and aggregate PPIs
16 KAMI PubMed BioPAX Reading Dee imort exort KAMI Instantiation Annotation RBM &c. &c.
17 KAMI in BigM PubMed Reading Dee KAMI Instantiation Annotation RBM
18 (KAMI) read Grb2 s SH2-domain binds hoshorylated EGFR PubMed Reading Dee KAMI Instantiation Annotation RBM thanks to Lucian Galescu et alia!
19 KAMI inut Grb2 SH2 BND EGFR PubMed Reading Dee KAMI Instantiation Annotation RBM thanks to Ben Gyori & John Bachman!
20 KAMI read/inut aa:y loc:1092 Grb2 BND EGFR Grb2 binds EGFR hoshorylated on Y1092 PubMed Reading Dee KAMI Instantiation Annotation RBM
21 KAMI dee reading I already know something about this interaction PubMed Reading Dee KAMI Instantiation Annotation RBM Grb2 s SH2-domain binds [hoshorylated] EGFR hoshorylated on Y1092 this is not yet fully automated: requires a semantic layer
22 KAMI udate aa:y loc:1092 Grb2 SH2 BND EGFR merge PubMed Reading Dee KAMI Instantiation Annotation RBM Grb2 s SH2-domain binds [hoshorylated] EGFR hoshorylated on Y1092 this is a ste of grah rewriting
23 KAMI udate aa:y loc:1092 Grb2 SH2 BND EGFR merged PubMed Reading Dee KAMI Instantiation Annotation RBM Grb2 s SH2-domain binds [hoshorylated] EGFR hoshorylated on Y1092 this is another ste of grah rewriting
24 KAMI read/inut Grb2 SH2 BND Shc Grb2 s SH2-domain binds hoshorylated Shc PubMed Reading Dee KAMI Instantiation Annotation RBM Shc BND Grb2 SH2 BND aa:y loc:1092 EGFR
25 KAMI instantiate indeendent! Grb2(SH2e), EGFR(g,Y1092~) -> Grb2(SH2e!1), EGFR(g!1,Y1092~) Grb2(SH2s), Shc(g,shc~) -> Grb2(SH2s!1), Shc(g!1,shc~) PubMed Reading Dee KAMI Instantiation Annotation RBM Shc BND Grb2 SH2 BND aa:y loc:1092 EGFR
26 KAMI aggregate These interactions use the same mechanism! PubMed Reading Dee KAMI Instantiation Annotation RBM this is not yet fully automated: requires a semantic layer BND merge Shc Grb2 SH2 BND aa:y loc:1092 EGFR
27 KAMI aggregate PubMed Reading Dee KAMI Instantiation Annotation RBM merged (reversibly) this is a ste of grah rewriting Shc Grb2 SH2 BND aa:y loc:1092 EGFR
28 KAMI instantiate conflict! Grb2(SH2), EGFR(g,Y1092~) -> Grb2(SH2!1), EGFR(g!1,Y1092~) Grb2(SH2), Shc(g,shc~) -> Grb2(SH2!1), Shc(g!1,shc~) PubMed Reading Dee KAMI Instantiation Annotation RBM Shc Grb2 SH2 BND aa:y loc:1092 EGFR
29 KAMI negation only aa:s can bind aa:s loc:90 Grb2 BND EGFR Grb2-S90D does not bind EGFR PubMed Reading Dee KAMI Instantiation Annotation RBM aa can be S or D aa:s,d loc:90 Shc BND Grb2 SH2 BND aa:y loc:1092 EGFR
30 KAMI enumeration only one rule for Grb2_D90 Grb2_S90(SH2e), EGFR(g,Y1092~) -> Grb2_S90(SH2!1), EGFR(g!1,Y1092~) Grb2_S90(SH2s), Shc(g,shc~) -> Grb2_S90(SH2!1), Shc(g!1,shc~) Grb2_D90(SH2s), Shc(g,shc~) -> Grb2_D90(SH2!1), Shc(g!1,shc~) PubMed Reading Dee KAMI Instantiation Annotation RBM automatic enumeration of rules aa:s,d loc:90 Shc BND Grb2 SH2 BND aa:y loc:1092 EGFR
31 KAMI negation only aa:s can bind aa:s loc:90 Grb2 BND EGFR Grb2-S90D does not bind EGFR PubMed Reading Dee KAMI Instantiation Annotation RBM this automatically roagates to the interaction with Shc too aa:s,d loc:90 Shc aa can be S or D Grb2 SH2 BND aa:y loc:1092 EGFR
32 KAMI enumeration no rules for Grb2_D90 Grb2_S90(SH2), EGFR(g,Y1092~) -> Grb2_S90(SH2!1), EGFR(g!1,Y1092~) Grb2_S90(SH2), Shc(g,shc~) -> Grb2_S90(SH2!1), Shc(g!1,shc~) PubMed Reading Dee KAMI Instantiation Annotation RBM automatic enumeration of rules aa:s,d loc:90 Shc Grb2 SH2 BND aa:y loc:1092 EGFR
33 Wraing u
34 Summary A urely formal grah rewriting foundation reresents knowledge and [revokable] hyotheses using formal oerations of (udate and) aggregation Model instantiation into RBM automatically maintains desired [conflict] invariants and handles the effects of mutations because all enumeration is done by the machine
35 Automation? Other than an exert user, where could the stes of rewriting come from? semantics: tyically steric or functional roerties of certain regions, e.g. SH2 or kinase domains also allows for semantic checking and inference more general inference
36 Work in rogress Oen re-imlementation as a Python library standard meta-models and meta-model transformations can also be user-defined Based on a grah rewriting Python library itself built on to of NetworkX multi-level rewriting with uward roagation
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