challenges in formal reasoning about signaling networks · • formal models of biomolecular...
TRANSCRIPT
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Challenges in formal reasoning about signaling networks
Carolyn Talcott Simmons
August 2015
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Plan
• What models are we talking about?
• Pathway Logic in a nutshell
• Challenges
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Modeling 101
• What questions do you want the model answer?
• What can you observe/measure?
• What does that mean?
• Explain it to a computer!
• Need a formal representation system
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Formal Modeling Methodology
Impact
rapid prototyping
state space search
S |=Φ model
checking
Curator/model builder
asking questionsmodel
!
data
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Symbolic analysis -- answering questions
• Forward collection -- upper bound on possible states
• Backward collection -- initial states leading to states of interest
• Search -- for (symbolic) state of interest
• Model checking -- do all executions satisfy ϕ, find counter example
• Constraint solving -- steady state analysis
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Pathway Logic
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Executable models of cellular processes
http://pl.csl.sri.com
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Pathway Logic (PL) Goals
• Understanding how cells work
• Formal models of biomolecular processes that
• capture biologist intuitions
• can be executed
• Tools to
• organize and analyze experimental findings
• carry out gedanken experiments
• discover/assemble execution pathways
• New insights into the inner workings of a cell.
• A new kind of review
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ErbB network cartoon—biologists review model
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Yarden and Sliwkowski, Nat. Rev. Mol. Cell Biol. 2: 127-137, 2001
Yarden and Sliwkowski, Nat. Rev. Mol. Cell Biol. 2: 127-137, 2001
Yarden and Sliwkowski, Nat. Rev. Mol. Cell Biol. 2: 127-137, 2001
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PL Egf dish - an executable review model
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Yarden and Sliwkowski, Nat. Rev. Mol. Cell Biol. 2: 127-137, 2001
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PL from 1k feet
Key components
• Representation system
• controlled vocabulary
• datums (formalized experimental results)
• rules
• Curated datum knowledge base (KB) and search tool
• Evidence based rule networks
• STM, Protease, Mycolate, GlycoSTM
• Executable models
• generated by specifying initial conditions and constraints
• query using formal reasoning techniques
• Visualize and browse subnets
Curation Inference ReasoningLittle Mechanismto
Big Mechanism
RKB
Paper Datums Rules ExecutableRuleKB Explanation
Sanity Check
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Example signal propagation (using Petri Nets)
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Sos1-reloc-CLi
Sos1-CLc
EgfR-CLm
1
5
Grb2-reloc-CLi
Egf:EgfR-act-CLm Grb2-CLc
Egf-Out
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Sos1-reloc-CLi
Sos1-CLc
EgfR-CLm
1
5
Grb2-reloc-CLi
Egf:EgfR-act-CLm Grb2-CLc
Egf-Out
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Sos1-reloc-CLi
Sos1-CLc
EgfR-CLm
1
5
Grb2-reloc-CLi
Egf:EgfR-act-CLm Grb2-CLc
Egf-Out
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Sos1-reloc-CLi
Sos1-CLc
EgfR-CLm
1
5
Grb2-reloc-CLi
Egf:EgfR-act-CLm Grb2-CLc
Egf-Out
Sos1Dish3Sos1Dish2Sos1Dish1Sos1Dish =rule1=> =rule5=> =rule13=>
Ovals are occurrences -- biomolecules in locations (aka places). Dark ovals are present in the current state (marked). Squares are rules (aka transitions). Dashed edges connect components that are not changed.
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Rule Knowledge Base (RKB) — A Rewrite Theory
• Rewrite rules describe local change and specify required contextrl[529.Hras.irt.Egf]:< Egf : [EgfR - Yphos], EgfRC > < [gab:GabS - Yphos], EgfRC >< [hrasgef:HrasGEF - Yphos], EgfRC > < Pi3k, EgfRC > < [Shp2 - Yphos], EgfRC >< [Hras - GDP], CLi > =>< Egf : [EgfR - Yphos], EgfRC > < [gab:GabS - Yphos], EgfRC >< [hrasgef:HrasGEF - Yphos], EgfRC > < Pi3k, EgfRC > < [Shp2 - Yphos], EgfRC >< [Hras - GTP], CLi >
*** ~/evidence/Egf-Evidence/Hras.irt.Egf.529.txt • Symbolic rules represent a family of rules using sorted variables
• EgfRC is the location of the Egf Receptor complex, it is populated in response to the Egf signal. CLi is the membrane interior
• gab:GabS is a variable standing for Gab1 or Gab2, hrasgef:HrasGEF is a variable for any of several HrasGEFs (enzymes to exchange GDP for GTP)
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Where do rules come from?
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xHras[tAb] GTP-association[BDPD] is increased irt Egf (5 min)
Subject Assay Change Treatment
SProtein
Handle
Nam
e
Detection
Method
Treatment
Times
The Elements of a Datum
source: 15574420-Fig-5a
Source
PMID
Figure
inhibited by: xGab1(Y627F) [substitution]
ExtraEntity
Mutation
Type
Mode
cells: VERO<xHras><xGab1> in BMLS
Environment
Cells
Medium
Cell
Mutation
Cell
Mutation
They are inferred from experimental findings.These are collected using a formal data structure call datums
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What can be done with an executable RKB?
• Generate a model, for example, of response to some stimulus
• Define initial state -- cell components and additions -- experimental setup
• Forward collection gives a network of all the possibly reachable rules
• The Signal Transduction Model (STM) RKB comes with > 30 models of response (of a resting cell) to different treatments (Egf,Insulin,Tnfa,Tgfb, Lps (bug bit), Serum, ....)
• From a model you can
• Backwards collection generates a subnet relevant to a specific outcome (Erk activated in the nucleus)
• find an execution path—model-checking the assertion that no path exists
• carryout in silico knockouts
• compare nets
• explore connections up/down stream
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The subnet of the Egf model for activating (GTPing) Hras. (Represented as a Petri net.)
Msk2
Creb1Arf1
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Comparing two pathways
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Symbolic analysis -- answering global questions• RMP -- the set of all reaction minimal paths from an initial state to a
given set of goals
• From this set we can compute
• Essential transitions – reactions that are in all pathways to an output.
• Used places – biochemical species that are in at least one pathway.
• Knockouts – biochemical species that are in all pathways to an output.
• Multisignal cellular responses – at least one pathway to an output has more than one stimulus.
• In the Hras subnet
• 6 execution pathways (3 using Sos1, 3 using RasGrp3 — GEFs)
• 20 double knockouts (from 4 protein pairs)
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Challenges
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Collecting data Data to Knowledge Knowledge to Model Dynamics
Automating all of this!
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Collecting Data
• Finding sources
• abstracts are NOT sufficient sources for indexing / search
• inspiring Biologists to do the missing experiment
• Extracting datums (semi) automatically
• Naming things
• What was measured, under what conditions
• Resolving ambiguities
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Data to Kapta: Assembling diverse formal factletts into signaling rules
• Each datum is a partial view — constraint solving can integrate views
• Resolving apparent inconsistencies
• Resolving different experimental conditions
• Interpreting variants
• mutations, deletions, fragments
• Why was the perturbation chosen? How does that change the meaning?
• Combining experiments
• activity in a test-tube + activity in a cell 20
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KB to Model
• Rules may not connect
• Different levels of detail
• Location location — experiments rarely give location information :-(
• Activity vs modification state
• What does a give cell type/state express — the initial state
• It is rare that cells are well characterized
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Background knowledge!!!
• Notions such as kinase, Yphos more specific than phos ….
• Needed in all steps
• The is a lot of it
• some in databases, some in books
• can it be relied upon — computational vs experimental
• Needs to be curated into computable form.
• Need a well-defined formal representation that can be used by
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Dynamics
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• PL models capture before/after but not how much or how fast. !
• Data for real kinetics is hard to find !
• Cells aren’t well stirred solutions !
• What really matters depends on the question being asked !
• What about symbolic quantitative reasoning? • using variables and constraints among them • what are the right abstractions • what are relative rates of processes • characterizing effects of competition between processes !
• We need biological reasoning principles (which can then be formalized and helpful tools built)
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Questions ???
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Controlled vocabulary
sort HrasSort .subsort HrasSort < RasS < BProtein .!op Hras : -> HrasSort [ctor metadata ( (spnumber P01112) (hugosym HRAS) (synonyms "GTPase HRas" "Transforming protein p21" "v-Ha-ras Harvey rat sarcoma viral oncogene homolog" "Harvey murine sarcoma virus oncogene" "H-Ras-1" "c-H-ras" "HRAS1" "RASH1" "RASH_HUMAN"))] .!op Rass : -> RasS [ctor metadata ( (category Family) (members Hras Kras Nras))] . !op Pi3k : -> Composite [ctor metadata "( (subunits Pik3cs Pik3rs) (comment "PI3 Kinase is a heterodimer of:" "a p110 catalytic subunit: Pik3ca, Pik3cb, Pik3cd or Pik3cg" "a p85 regulatory subunit: Pik3r1, Pik3r2, or Pik3r3"))] .!