BioSigNet: Reasoning and Hypothesizing about Signaling Networks

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BioSigNet Reasoning and Hypothesizing about
Signaling Networks
Nam Tran
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Main points

• Biomedical databases structured data and
  queries.
• http//cbio.mskcc.org/prl/
• Next step knowledge bases and reasoning.
• Kinds of reasoning, incomplete knowledge
• How can existing knowledge be revised, expanded?
• Hypothesis formation
• Experimental verifications

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Knowledge based reasoning

• Various kinds of reasoning
• Prediction side effects
• Planning designing therapies
• Explanation reasoning about unobserved aspects
• Consistency checking correctness of ontologies
• Additional facets/nuances
• Reasoning with incomplete knowledge.
• Reasoning with defaults.
• Ease of updating knowledge (elaboration
  tolerance)

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Hypothesis formation

• If
• our observations can not be explained by our
  existing knowledge?
• or the explanations given by our existing
  knowledge are invalidated by experiments?
• Then Our knowledge needs to be augmented or
  revised?
• How?
• Can we use a reasoning system to predict some
  hypothesis that one can verify through
  experimentation?

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Knowledge base
Hypothesis space
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Motivation -- summary

• Goal To emulate the abstract reasoning done by
  biologists, medical researchers, and pharmacology
  researchers.
• Types of reasoning prediction, explanation and
  planning.
• Current system biology approaches mostly
  prediction.
• Incomplete knowledge constantly needs to be
  updated -gt Hypothesis formation

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Overview of our approach

• Represent signal network as a knowledge base that
  describes
• actions/events (biological interactions,
  processes).
• effect of these actions/events.
• triggering conditions of the actions/events.
• To query using the knowledge base
• Prediction explanation planning.
• Hypothesizing to discover new knowledge
• BioSigNet-RRH Biological Signal Network
  Representation, Reasoning and Hypothesizing

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Foundation behind our approach

• Research on representing and reasoning about
  dynamic systems (space shuttles, mobile robots,
  software agents)
• causal relations between properties of the world
• effects of actions (when can they be executed)
• goal specification
• action-plans
• Research on knowledge representation, reasoning
  and declarative problem solving the AnsProlog
  language.

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Representing signal networks as a Knowledge Base

• Alphabet
• Actions/Events bind(ligand,receptor)
• Fluents high(ligand), high(receptor)
• Statements
• Effect axioms
• bind(ligand,receptor) causes bound(ligand,recept
  or) if con.
• high(other_ligand) inhibits bind(lig,receptor)
  if cond.
• Trigger conditions
• high(ligand), high(receptor) triggers
  bind(ligand,receptor)

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Initial observations, Queries, Entailment

• Entailment (K,I) Q
• Given
• K the knowledge base of binding
• I initially high(ligand), high(receptor)
• Conclude
• Q eventually bound(ligand,receptor)
• Given
• K the knowledge base of binding
• I initially high(ligand), high(receptor),
  high(other_ligand)
• Conclude ? Q

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Importance of a formal semantics

• Besides defining prediction, explanation and
  planning, it is also useful in identifying
• Under what restrictions the answer given by a
  given algorithm will be correct. (soundness!)
• Under what restrictions a given algorithm will
  find a correct answer if one exists.
  (completeness!)

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• bind(TNF-?,TNFR1) causes trimerized(TNFR1)
• trimerized(TNFR1) triggers bind(TNFR1,TRADD)

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Prediction

• Given some initial conditions and observations,
  to predict how the world would evolve or predict
  the outcome of (hypothetical) interventions.

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• Initial Condition
• bind(TNF-a,TNF-R1) occurs at 0
• Query
• predict eventually apoptosis
• Answer Unknown!
• Incomplete knowledge about the TRADDs bindings.
• Depends on if bind(TRADD, RIP) happened or not!

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• Initial Condition
• bind(TNF-a,TNF-R1) occurs at t0
• Observation
• TRADDs binding with TRAF2, FADD, RIP
• Query
• predict eventually apoptosis
• Answer Yes!

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Explanation

• Given initial condition and observations, to
  explain why final outcome does not match
  expectation.
• Relation to diagnosis.

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• Initial condition
• bound(TNF-?,TNFR1) at t0
• Observation
• bound(TRADD, TRAF2) at t1
• Query Explain apoptosis
• One explanation
• Binding of TRADD with RIP
• Binding of TRADD with FADD

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Planning

• Given initial conditions, to plan interventions
  to achieve a goal.
• Application in drug and therapy design.

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Planning requirements

• In addition to the knowledge about the pathway we
  need additional information about possible
  interventions such as
• What proteins can be introduced
• What mutations can be forced.

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Planning example

• Defining possible interventions
• intervention intro(DN-TRAF2)
• intro(DN-TRAF2) causes present(DN-TRAF2)
• present(DN-TRAF2) inhibits bind(TRAF2,TRADD)
• present(DN-TRAF2) inhibits interact(TRAF2,NIK)
• Initial condition
• bound(NF?B,I?B) at 0
• bind(TNF-a,TNF-R1) at 0
• Goal to keep NF?B remain inactive.
• Query
• plan always bound(NF?B,I?B) from 0

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Future Works!

• Further development of the language
• To better approximate cellular systems
• Delay triggers
• Granularities of representation
• Continuous processes, hybrid systems
• Concurrency, durative actions
• Scaled-up implementation
• Kohns map
• Networks in Reactome and other repositories
• Ontologies
• Integration with BioPax

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Knowledge base
Hypothesis space
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Issues in this tiny example

• Hypothesis formation
• Theory UV leads to cancer.
• Observation wild-type p53 resists the UV
  effect.
• Hypothesis p53 is a tumor-suppressor.
• Elaboration tolerance
• How do we update/revise UV leads to cancer?
• Defaults and non-monotonic reasoning
• Normally UV leads to cancer.
• UV does not lead to cancer if p53 is present.

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Construction of hypothesis space

• Present manual construction, using research
  literature
• Future integration of multiple data sources
• Protein interactions
• Pathway databases
• Biological ontologies
• ..
• Provide cues, hunches such as
• A may interact with B action interact(A,B)
• A-B interaction may have effect C
• interact(A,B) causes C

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Generation of hypotheses

• Enumeration of hypotheses
• Search computing with Smodels (an implementation
  of AnsProlog)
• Heuristics
• A trigger statement is selected only if it is the
  only cause of some action occurrence that is
  needed to explain the novel observations.
• An inhibition statement is selected only if it is
  the only blocker of some triggered action at some
  time.
• Maximizing preferences of selected statements

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Generation (cont) heuristics

• Knowledge base K
• a causes g
• b causes g
• Initial condition I intially f
• Observation O eventually g
• (K,I) does not entail O
• Hypothesis space to expand K with rules among
• f triggers a
• f triggers b
• Hypotheses f triggers a , or f
  triggers b

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Case study p53 network
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Tumor suppression by p53

• p53 has 3 main functional domains
• N terminal transactivator domain
• Central DNA-binding domain
• C terminal domain that recognizes DNA damage
• Appropriate binding of N terminal activates
  pathways that lead to protection of cell from
  cancer.
• Inappropriate binding (say to Mdm2) inhibits p53
  induced tumor suppression.

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p53 knowledge base

• Stress
• high(UV ) triggers upregulate(mRNA(p53))
• Upregulation of p53
• upregulate(mRNA(p53)) causes high(mRNA(p53))
• high(mRNA(p53)) triggers translate(p53)
• translate(p53) causes high(p53)

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p53 knowledge base (cont.)

• Tumor suppression by p53
• high(p53) inhibits growth(tumor)

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p53 knowledge base (cont)

• Interaction between Mdm2 and p53
• high(p53), high(mdm2) triggers bind(p53,mdm2)
• bind(p53,mdm2) causes bound(dom(p53,N))
• bind(p53,mdm2) causes high(p53 mdm2),
• bind(p53,mdm2) causes high(p53),high(mdm2)

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Hypothesis formation

• Experimental observation
• I initially high(UV), high(mdm2), high(ARF)
  
• O eventually tumorous
• (K,I) does not entail O
• Need to hypothesize the role of ARF.

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Constructing hypothesis space

• Levels of ARF and p53 correlate
• high(ARF) triggers upregulate(mRNA(p53))
• high(p53) triggers upregulate(mRNA(ARF))

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Constructing (cont)

• Interactions of ARF with the known proteins
• bind(p53,ARF) causes bound(dom(p53,N))

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Constructing (cont)

• Influence of X (ARF) on other interactions
• high(ARF) triggers upreg(mRNA(p53))
• high(ARF) triggers translate(p53)
• high(ARF) triggers bind(p53,mdm2)

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Hypothesis

• high(UV) triggers upregulate(mRNA(ARF))
• high(ARF), high(mdm2) triggers bind(ARF,mdm2)

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Future Works

• Automatic construction of hypothesis space
• Extraction of facts like protein interactions
• Integration of knowledge from different sources
• Consistency-based integration (HyBrow)
• Ontologies
• Heuristics for hypothesis search
• Ranking of hypotheses
• Make use of number data like microarray?