Conceptual+modeling+in+Systems+ Biology:+before+math++ · Why+model+always++ mathemaNcal+ •...
Transcript of Conceptual+modeling+in+Systems+ Biology:+before+math++ · Why+model+always++ mathemaNcal+ •...
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Conceptual modeling in Systems Biology: before math
Anatoly Sorokin 06/11/2009
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Outline
• Why conceptual modeling? • SBGN – diagram as conceptual model • SBGN-‐PD • Exercise • SBGN-‐ER • Exercise • SBGN-‐AF • Exercise SoluNons and QuesNons
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ScienNfic modelling
• “A model in science is a physical, mathemaNcal, or logical representaNon of a system of enNNes, phenomena, or processes.” (Wikipedia)
• “Scien&fic modelling is the process of generaNng abstract, conceptual, graphical and or mathemaNcal models” (Wikipedia)
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Why model always mathemaNcal
• Formalised – Unambiguous – Precise – ConverNble to machine-‐readable format – Analysable • AnalyNcally • Numerically • AutomaNcally • Manually
– Simulatable
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Why development mathemaNcal of models are
difficult?
• Formalised – Precise – Skills are required – Concept mapping is required
• Dual experNse • AssumpNon validaNon
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New language?
• Allow the representaNon of diverse biological objects and interacNons
• Be semanNcally and visually unambiguous; • Allow implementaNon in so_ware that can aid the drawing and verificaNon of diagrams;
• Have semanNcs that are sufficiently well defined that so_ware tools can convert graphical models into mathemaNcal formulas for analysis and simulaNon; and
• Be unrestricted in use and distribuNon, so that the enNre community can freely use the notaNon without encumbrance or fear of intellectual property infracNons.
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SBGN
• Standard representaNon of essenNal biochemical and cellular processes – Set of symbols – SemanNcs – Syntax
• UlNmate goal: – Thousands way to draw – One way to read
• www.sbgn.org
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"The goal of SBML is to help people to disagree as precisely as possible". Ed Franck, Argonne NaNonal Laboratory
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SBGN
• Community effort (about 30 contributors) • Started at 2006 by Hiroaki Kitano • First language release ICSB 2008 (SBGN-‐PD) • Three languages (released independently)
– Process Diagram: the causal sequences of molecular processes and their results
– En?ty Rela?onship: the interacNons between enNNes irrespecNve of sequence
– AcNvity flow: the flux of informaNon going from one enNty to another
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SBGN
• Colour has no meaning • Size has no meaning
• Meaning should be conserved upon – scaling – resoluNon – relayout
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Process DescripNon language
• DescripNon of change • Sequence of events is depicted • Operates with pools rather than individuals • Mapping to ODE model (SBML)
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Pools of enNNes
• CollecNon of molecules indisNnguishable in some sense
• Non-‐overlapping • characterized by concentraNon
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EnNty types
LABEL LABEL LABEL LABEL
Unspecified EnNty
Simple chemical
Macromolecule Nucleic acid feature
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Material type of molecule
• Unit of informaNon • Controlled vocabulary
Name Label Non-macromolecular ion mt:ion Non-macromolecular radical mt:rad Ribonucleic acid mt:rna Deoxribonucleic acid mt:dna Protein mt:prot Polysaccharide mt:psac
pre:label
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Conceptual type of nucleic acid feature
Name Label Gene ct:gene Transcription start site ct:tss Gene coding region ct:coding Gene regulatory region ct:grr Messenger RNA ct:mRNA
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PhyA mt:prot
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Macromolecular pools: state variables
• Pools is set of molecules somehow undisNnguishable
• Molecules can be in different state – (Non)phosphorylated – Open/close channel – Modified at some state
R R P
R 2P
Ch
Close
Ch
Open
Kinase
P@237
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Stateless and state-‐full enNty types
• Not all enNNes can have states: Stateless – Simple chemicals – Unspecified enNty
• State-‐full enNNes – Macromolecule – Nucleic acid feature – Complex
• State is defined as combinaNon of state values • Once defined state variable should be always visible
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PhyA mt:prot
Pr/Prf
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Complex and mulNmer • Represents complexes of molecules held together by non-‐covalent bonds
• MulNmer require cardinality
• Can have State variables – In mulNmer it means that all monomers – Use complex instead
N:2
LABEL N:2
LABEL LABEL N:2 LABEL
LABEL
MulNmers Complex
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N:2
PhyA Pr
PhyA Pr
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Key concept: Process
• Process: conversion of element of one pool to another
• Special cases: – Non-‐covalent binding
• AssociaNon • DissociaNon
– Incompleteness • Uncertain process • Omiled process
?
//
AssociaNon
DissociaNon
Process
Uncertain process
Omiled process
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N:2
PhyA Pr
PhyA mt:prot
Pr
2
1
N:2
PhyA Pfr
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Arcs
• Using pools by process – ConsumpNon/producNon – Stoichiometry (opNonal)
• RegulaNng process rate – SNmulaNon
– InhibiNon – Catalysis
• Requirement for process – Necessary sNmulaNon
producNon
catalysis
sNmulaNon
inhibiNon
necessary sNmulaNon
modulaNon
consumpNon 2
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Environmental influence
• External influences: Perturbing agent – Light – Temperature change – MutaNon/disease
• System manifestaNon: Phenotype – Apoptosis – Phenotype
LABEL LABEL
Perturbing agent
Phenotype
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N:2
PhyA Pr
N:2
PhyA Pfr
RL FRL
PhyA mt:prot
Pr
2
1
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Laying out process arcs
• ProducNon can represents consumpNon: – Reversible process – Substrates and products should come to opposite sides of process shape (two connectors)
• Regulatory arcs should come to other two sides of the process
• If you have separate regulaNon of forward and backward process, you have to split
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Sink/source: creaNon and destrucNon
• We need represent creaNon and destrucNon of enNNes
• We cannot omit consumpNon and producNon arcs
• We need shape to represent source of materials and sink of degraded enNNes
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2
N:2
PhyA Pr
N:2
PhyA Pfr
RL FRL
N:2
PhyA Pfr
N:2
FHY1
PhyA mt:prot
Pr
2
1
1
1 FHY1 mt:prot
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Compartments • Container to represent physical or logical structure – Free form – Visually thicker line
• The same enNty pools in different compartments are different
• Compartments are independent • Overlapping do not mean containment
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Gene acNvaNon
2
N:2
PhyA Pr
N:2
PhyA Pfr
RL FRL
N:2
PhyA Pfr
N:2
FHY1
PhyA mt:prot
Pr
2
1
1
1
N:2
PhyA Pfr
N:2
FHY1 2
N:2
PhyA Pfr
1
1
FHY1 mt:prot
oy
FHY1 mt:prot
oy
FHY1 mt:prot
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Clone marker
• Each enNty pool represents only once on the map
• Layout problems
• Clone marker as visual indicator of duplicaNon – Stateless nodes carry unnamed marker – State-‐full nodes carry named marker to simplify recogniNon
marker
LABEL
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Gene acNvaNon
2
N:2
PhyA Pr
N:2
PhyA Pfr
RL FRL
N:2
PhyA Pfr
N:2
FHY1
PhyA mt:prot
Pr
2
1
1
1
N:2
PhyA Pfr
N:2
FHY1 2
N:2
PhyA Pfr
1
1
FHY1 mt:prot
oy
FHY1 mt:prot
oy
FHY1 mt:prot
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Logical gates
• Encode of network logic – to simplify layout
• 20 acNvators for the process – to include uncertain informaNon
• CombinaNon of TF with unknown or combinatorial binding kineNcs
• Three main logic operaNons – AND: all are required – OR: any combinaNon is required – NOT: prevent influence
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Strength and weakness of SBGN-‐PD Strength
• Easy convert to math – Natural mapping to SBML
• A lot of informaNon in DB – KEGG – Panther
• Timeline is easily extractable
Weakness
• Full explicit definiNon of state – Combinatorial complexity – AddiNonal assumpNon to
include uncertain informaNon
• Laborious creaNon
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FIRST EXERCISE
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EnNty-‐RelaNonship language
• Draw influences of enNNes • States are independent • There is no Nme sequence
• Logical or probabilisNc descripNon of system
• Naturally map narraNve descripNon of the system
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Model to draw
• Process of Polymerase Chain ReacNon (PCR): – Sense and anNsense DNA stains bind to each other – Polymerase enzyme recreate missing parts of dsDNA based upon ssDNA as template
– There are two short primers to iniNate synthesis of new DNA
– Once heated DNA melts and primers become able to bind ssDNA and prevent sense and anNsense DNA stains bind to each other back
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EnNty
• There is only one shape for enNty – There is no difference between Macromolecule and Gene
– Material and/or conceptual type could be represented as Unit-‐of-‐InformaNon
• EnNty is something that can exists – Molecule – Gene – Allele LABEL
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InteracNon
• EnNNes can interact if they exists • InteracNon is statement – If (when) two enNNes interacts, then..
• We have new enNty, which is actualizaNon of THEN: – Outcome
Sense
mt:dna
AnNsense
mt:dna
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State variables
• State of EnNty could be described with state variables
• Unlike PD state variables – do not require to have value – Are independent – Can be assigned
• Two special variable types – Existence – LocaNon
LocaNon
Existence
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Sense
mt:dna
AnNsense
mt:dna
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State value assignment
• Another type of statement – If (when) state variable acquire a value
• Site is phosphorylated • EnNty deleted
– existence assigned FALSE • EnNty moved to nucleus
– locaNon assigned value ‘nucleus’ – then …
• Another outcome
• Selector: – More than one value of variable
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Sense
mt:dna
AnNsense
mt:dna
T T
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Influences
• Arc to represent influence of enNty (outcome) to a relaNonship (interacNon or assignment)
• Logic rules to connect statements
sNmulaNon
inhibiNon
necessary sNmulaNon
modulaNon
absolute inhibiNon
absolute sNmulaNon
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Sense mt:dna
AnNsense mt:dna
3’ primer mt:dna
5’ primer mt:dna
T T
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Environmental influence
• External influences: Perturbing agent – Light – Temperature change – MutaNon/disease
• System manifestaNon: Phenotype – Apoptosis – Phenotype
LABEL LABEL
Perturbing agent
Phenotype
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Sense mt:dna
AnNsense mt:dna
3’ primer mt:dna
5’ primer mt:dna
T T
Heat
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Cis and Trans interacNon
• Working with individuals we need to disNnguish between two cases – Changing itself (Cis) • Internal phosphorylaNon of RTK a_er dimerisaNon
– Changing neighbours of the same type (Trans) • AcNvaNon kinase to phosphorylate another proteins
• Shown in the same way as stoichiometry in PD cis trans
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Sense mt:dna
AnNsense mt:dna
T T
3’ primer mt:dna
5’ primer mt:dna
cis cis
Heat
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Strength and weakness of SBGN-‐ER Strength
• Handle combinatorial complexity naturally
• Close mapping to rule-‐based modelling – BNGL, kappa
• Statement based nature of ER helps in text anotaNon
Weakness
• Difficult to read
• No Nmeline • Difficult for validaNon and
reasoning
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SECOND EXERCISE
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You cannot have too much
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AbstracNon and decomposiNon Decomposi&on
• IdenNfy boundaries in the model – Scales
• Time
• ConcentraNon – FuncNon
• Topology
• Split model into set of simple modules
• PD submap
Abstrac&on
• New concepts • Less details • Higher levels • AF
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1
2
Back to PD: submaps
• Encapsulate modules • Provide connecNon between main map and module
• Share the namespace • There is no way to define role of elements within submap
1
3
2 H2O
O2
H2
H2O
O2
H2
3
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AcNvity Flow: abstracNon
• Main concept is Biological AcNvity – Each node represents an acNvity, but not the enNty.
– MulNple nodes can be used to represent acNviNes from one enNty, e.g., receptor protein kinase.
– One node can be used to represent acNviNes from a group of enNNes (e.g., a complex).
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Material and conceptual types in AF • AcNvity node is rectangular to emphasize similarity to reacNon
• Unit of informaNon has shape according to node type
• Unit of informaNon can carry name of enNty, which has the acNvity
AcNvity
mt:ion
LABEL LABEL
PerturbaNon Phenotype
AcNvity
mt:prot
AcNvity of ion AcNvity of protein
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Regulatory arcs
• Operates on acNviNes • Shows influences – PosiNve • Catalysis • SNmulaNon
– NegaNve • InhibiNon
– Required • Necessary sNmulaNon
PosiNve influence
NegaNve influence
Necessary sNmulaNon
Unknown influence
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Logical gates
• Three main logic operaNons – AND: all are required – OR: any combinaNon is required – NOT: prevent influence
• Crucial for AF – No complex
– No outcome – No modificaNons
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HRG HER3
mt:prot
HER2
mt:prot
Shc
mt:prot PI3K
mt:prot
GS
Ras
Raf
MEK
ERK
PP2A
PTEN PIP3
Akt
PDK
Apoptosis
2C4
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Strength and weakness of SBGN-‐AF Strength
• Similar to biological sketch drawings
• Compact
Weakness
• Ambiguous
• Requires text or other diagram
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THIRD EXERCISE
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SpecificaNons
• Process DescripNon (doi:10.1038/npre.2009.3721.1) – Molecular pools and reacNons
• AcNvity Flow (doi:10.1038/npre.2009.3724.1) – FuncNons and their cross-‐coupling
• EnNty RelaNonship (doi:10.1038/npre.2009.3719.1) – Molecules and their interacNons
• www.sbgn.org • SBGN-‐discuss mailing list
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So_ware support of SBGN
• Edinburgh Pathway Editor – www.pathwayeditor.org
• Vanted – vanted.ipk-‐gatersleben.de
• CellDesigner – www.celldesigner.org
• Arcadia – www.arcadiapathways.sf.net
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SOLUTIONS AND QUESTIONS
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SBGN PD
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B
F
C
B
C
A mt:prot
mt:prot
mt:prot
• Protein C acNvity in triggering phenotype F is promoted by Protein A through marking its inhibitor B for degradaNon
• Protein A inhibits phenotype F by triggering gene B, which product degrade protein C that required for F to take place.
• Protein A protects protein B from degradaNon and that makes protein C being sequestered by B and prevents sNmulaNon of phenotype F
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F
B mt:prot
C mt:prot
A mt:prot
• Protein C acNvity in triggering phenotype F is promoted by Protein A through marking its inhibitor B for degradaNon
• Protein A inhibits phenotype F by triggering gene B, which product degrade protein C that required for F to take place.
• Protein A protects protein B from degradaNon and that makes protein C sequestered by B and prevents sNmulaNon of phenotype F
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B
F
C
B
C
A mt:prot
mt:prot
mt:prot
• Protein C acNvity in triggering phenotype F is promoted by Protein A through marking its inhibitor B for degradaNon
• Protein A inhibits phenotype F by triggering gene B, which product degrade protein C that required for F to take place.
• Protein A protects protein B from degradaNon and that makes protein C sequestered by B and prevents sNmulaNon of phenotype F
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SBGN ER
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L
R
K
P
cis
• Binding of ligand L to receptor R sequesters kinase K to the complex and causes phosphorylaNon of receptor. Kinase can not bind to phosphorylated receptor and leaves the complex
• Kinase K and ligand L can bind receptor independently but only in the presence of ligand kinase is able to phoshporylate receptor
• Ligand L binding to receptor-‐kinase complex (RK) makes possible phosphorylaNon of another molecule of receptor R by kinase K.
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L
R
K
P
cis
• Binding of ligand L to receptor R sequesters kinase K to the complex and causes phosphorylaNon of receptor. Kinase can not bind to phosphorylated receptor and leaves the complex
• Kinase K and ligand L can bind receptor independently but only in the presence of ligand kinase is able to phoshporylate receptor
• Ligand L binding to receptor-‐kinase complex (RK) makes possible phosphorylaNon of another molecule of receptor R by kinase K.
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L
R
K
P
cis
trans
• Binding of ligand L to receptor R sequesters kinase K to the complex and causes phosphorylaNon of receptor. Kinase can not bind to phosphorylated receptor and leaves the complex
• Kinase K and ligand L can bind receptor independently but only in the presence of ligand kinase is able to phoshporylate receptor
• Ligand L binding to receptor-‐kinase complex (RK) makes possible phosphorylaNon of another molecule of receptor R by kinase K.
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SBGN AF
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I mt:ion
B mt:prot
F
S
C mt:prot
A drug
• Drug A inhibits phenotype F associated with stress S by modulaNng intracellular level of ion I and inhibiNng protein B and C acNvaNon
• Drug A was shown to reduce response of the system to stress S by inducing protein B and C associaNon, which reduces protein C acNvity. Simultaneously drug A block protein C trans-‐acNvaNon domain that also inhibits phenotype F
• Drug A was shown to reduce transcripNonal acNvaNon and expression of gene B in protein C dependant manner which might contribute to inhibiNon of phenotype F.
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Binding acNvity B
F
S
A drug
TF acNvity C
TF acNvity
• Drug A inhibits phenotype F associated with stress S by modulaNng intracellular level of ion I and inhibiNng protein B and C acNvaNon
• Drug A was shown to reduce response of the system to stress S by inducing protein B and C associaNon, which reduces protein C acNvity. Simultaneously drug A block protein C trans-‐acNvaNon domain that also inhibits phenotype F
• Drug A was shown to reduce transcripNonal acNvaNon and expression of gene B in protein C dependant manner which might contribute to inhibiNon of phenotype F.
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B
ct:gene
F
A drug TF
acNvity C
TF acNvity
• Drug A inhibits phenotype F associated with stress S by modulaNng intracellular level of ion I and inhibiNng protein B and C acNvaNon
• Drug A was shown to reduce response of the system to stress S by inducing protein B and C associaNon, which reduces protein C acNvity. Simultaneously drug A block protein C trans-‐acNvaNon domain that also inhibits phenotype F
• Drug A was shown to reduce transcripNonal acNvaNon and expression of gene B in protein C dependant manner which might contribute to inhibiNon of phenotype F.