Pathway based Models to

understand The Role of biological Timing in health

and disease

Carolyn Talcott and Mary Ann GrecoSRI International

May, 2008

1

PLan

Big Picture

Preliminary data and model

SSB and PL [backup]

2

Vision

System level model of biological timing using simple behaviors

Circadian rhythms

Sleep / wake behavior

Connected to activity, stress, disease

Hypothesis: Biological timing is key to maintain health and for successful treatment of disease

3

Symbolic systems Biology

Symbolic -- logical concepts and relations

Systems -- mulitple aspects, views

Requires integration of experiment and modeling

translate data into knowledge

4

Inter-ORGAN relationships

The human body is compartmentalized

Brain: regulation / corrordination of functions

Liver: acquisition / adaptation of ingested raw material for maintainence and growth

Heart: circulation of blood, delivery of oxygen

Simple behavior -- baseline of how organs interact

Time of day (circadian) - effectiveness of chemotherapies

Sleep-wakefulness - affected by many factors

5

Biological timing and Sleep

Why sleep? An essential behavior Regulated by circadian and homeostatic influences Accounts for about 1/3 of lifetime

the function is unknown

Questions What are your organs doing when you sleep? When you are awake? What is common across organs? What is unique to an organ?

6

Biological timing: approach

Rat model Identify proteins in various organs differentially expressed across sleep-wake states Cellular functions extrapolated from protein IDs Pathway based modeling

Use known signal transduction pathways to create model for each organ Identify common/unique pathways for each organ

Verify results using other proteomics approaches (Western analysis, enzyme activity assays) in different behavioral paradigms (SD/RS)

7

Natural Sleep Paradigm

8

Inter-Organ Modeling of Simple Behavior

9

Hypothesis- Protein levels and activity in each of these organs are affected by sleep. Preliminary results indicate unique profiles underlie sleep-wake in each organ…

10

…and a subset of these spots are present in all 3 organs

11

The time of day (circadian) influence on protein expression is well established.

12

Per2 expression variesacross SD/RS in theseorgans

13

Architecture of a Systems Model

liver heart

brain

behavior

system

organ

statesignaleffect

statechange

14

Preliminary Results For the Brain

15

2D Master GEL -- Frontal Cortex

Proteins unique to different states were identifiedThose modeled in PL included Actin and RhobUse the PLA explorer to find signaling connections

16

Spots Identified by MS

17

Picking out the interesting bits(with Some additional Curation)

from 2D-Gels:-------------

State Protein Data Hypothetical Modification

Wake RhoGdi unique spot Yphos RhoB unique spot GTP Grb3{1} unique spot upreg (splice variant) Cofillin common phos Actin phosphorylated polymerized

SWS RhoGdi unique spot gone unphosed Rhob unique spot gone GDP Grb3 unique spot gone downreg(splice variant) Cofillin common unphos Actin-phos less phosphorylated depolymerized{2}

18

Exploring PL KB from Actin

Tns1

E41

Rac1-GTP

Actinin

Tns1

601

Pxn

434

Integrins-clustered

764165 1075 1076713 813

Ilk:Lims1:Parva

Vasp

374-8

Cofilin Ptk2-act

Pi3k-act

364-2

Crk-Yphos

53

Pfn1

73211886

Abi1:Eps8:Sos1

Src-act

Cofilin-phos

ActininActin-poly

Ilk:Lims1:Parva

555

Tmsb4x:Actin-mono

Pxn-Yphos

Pfn1:Actin-mono

Vasp Vcl

Zyx

Tmsb4x

Vcl

Zyx

Egf:EgfR-act

Ssh

Ptk6-act

Limk1-act

Tln-act

Diaph1-act

672

Pxn

Arp23-act

Rac1-GDP

Actin-mono

19

Exploring PL KB from RhoB

Prkca

802-2

331

Gsk3-Sphos

84-6

DAGIP3

1004

Rap1a-GTP

906

PA

Prkca-act

101 83934-18 229 83815 251-2074-18 37

RalGds-relocBtk-deactTiam1-phos

591-2

Cit-act

Prkcl1

581-2

Srf-act

Rap1Gap-act

90

Rhob-GDP

221-2798-2 807-2

Rhob-GTP

699-2 680-2 679-4611-2 700-2 698-2 593-2

Dgk-actSmad3

374-8

Cofilin

PP1-inhib

Cit

Erk2-act

Prkcl1-act Limk1

23827

CholineSmad3-deact

Ktn1-act

Diaph1

PP1

697

Smad2-deact

75-3

cAMP

Adcy4-act

Dgk

Rhpn1-actPld1-act

RtknKtn1

Btk-act Pebp1:Raf1 Diaph1-actPCCamk2-act

Gq-GDP

Actin-poly

Limk1-actAdcy2-act

Erk2

2003

Rhpn1

58

Myl9-phosRaf1Pebp1-phos

Plce1-act

ATP

Gsk3 RalGds

Ngef-reloc

Rock1-act

81

Braf-act Rap1a-GDP

Mcf2-act

PIP2

Crkl-reloc Rtkn-actPlcb-act

Trio-act

364-2

671298

Pld1

PKA-act

1054

Cofilin-phos

Rock1

Gs-bg RapGef1-actRap1Gap2-actAdcy2 Smad2

Ssh

Adcy4 Gq-GTP BrafTiam1 Myl9Sipa1-act

Srf

Gs-GTP

20

Comparing the Explore Nets

Prkca

802-2

Tns1

331

Gsk3-Sphos

84-6

DAGIP3

E41

Rac1-GTP

1004

Rap1a-GTP

37

RalGds-reloc

Prkca-act

101 83934-18 229 83815 251-2074-18 906

PABtk-deact

Tns1

601

Tiam1-phos

591-2

Cit-act

Prkcl1

581-2

Pxn

434

Srf-act

Rap1Gap-act

90

Rhob-GDP

221-2 798-2 807-2

Rhob-GTP

699-2 680-2 679-4611-2 700-2 698-2 593-2

Ilk:Lims1:Parva

1075

Dgk-actSmad3

764

Vasp

374-8

Cofilin

PP1-inhib

Cit

165

Erk2-act

Prkcl1-act Limk1

23827

CholineSmad3-deact

Crk-Yphos

53

Ktn1-act

Pfn1

11886 732

Abi1:Eps8:Sos1

Src-act

Diaph1

75-3

cAMP

Smad2-deact

PP1

697

Adcy4-act

Actinin

713

Rhpn1-act

Dgk

Pfn1:Actin-mono

Pld1-act

Vasp

Tmsb4x:Actin-mono

Ktn1 Rtkn

Btk-act Pebp1:Raf1 PC

Vcl

813

Diaph1-act

Gq-GDP

Camk2-act

1076

Zyx

Tmsb4x Actin-poly

Limk1-act

Zyx

Pxn-Yphos

Adcy2-act

Ptk6-act

Diaph1-act

672

Tln-act

555

Pxn

Erk2

2003

Arp23-act

Rac1-GDP

Vcl

Rhpn1

Actin-mono

58

Raf1 Pebp1-phos Myl9-phos

Plce1-act

ATP

Gsk3

Actinin

RalGds

Ngef-reloc

Rock1-act

Integrins-clustered

81

Braf-actRap1a-GDP

Mcf2-act

PIP2

Crkl-reloc Rtkn-actPlcb-act

Ptk2-act

Trio-act

Pi3k-act

364-2

671298

Pld1

PKA-act

1054

Cofilin-phos

Rock1

Ilk:Lims1:Parva

Gs-bg RapGef1-actRap1Gap2-actAdcy2 Smad2

Ssh

Egf:EgfR-act

Adcy4 Gq-GTP BrafTiam1 Myl9Sipa1-act

Srf

Gs-GTP

21

A HypotheticaL Model PathwayRelating State and Synaptic Plasticity

Wake state: unknown signal(s) => phosphorylation of Rock1 => activation of Limk1 => phosphorylation of cofilin => increase in polymerized actin (Phosphorylated cofilin is unable to depolymerize actin)

SWS:RhoDG11 binds Rhob-GDP (is not phosphorylated) => Rock1, Limk1, and cofillin would not be phosphorylated and => actin depolymerization => decrease in synaptic weight

22

Next Steps

Test pathway modeldetermine ratio of phosphorylated / non-phosphorylated proteins for spontaneous waking, sws, and recovery sleep following deprivation (Western analysis)expect LimkP/Limk higher in wake than sws, and lower in sleep following deprivationCofilin should have opposite ratios

Study effects of ageModel behaviors / timing in liver and heart

23

Symbolic Systems Biology and

PATHWAY LOGIC

24

Symbolic Systems BIOLOGY

Symbolic systems biology is the qualitative and quantitative study of biological processes as integrated systems rather than as isolated parts.

Our initial goal for symbolic systems biology include: modeling causal networks of biomolecular interactions in a logical framework at multiple scalesdeveloping executable formal models that are as close as possible to domain expert’s (biologists) mental models being able to compute with and analyze these complex networks

abstracting and refining the logical modelsusing simulation/deduction to compute/check postulated propertiesmaking testable predictions about possible outcomesusing experimental results to update the models

25

Sampling of Symbolic/Executable Modeling Approaches

Rewriting Logic + Temporal Logic (Pathway Logic!)Chemical Abstract Machine + Computation Tree Logic (BIOCHAM)Membrane calculi -- spatial process calculi / logics

BioAmbients / Brane calculus -- mobility of membranesP Systems -- mobility of processes

Statecharts + Live Sequence ChartsProcess Algebras + Stochastic Simulation & Probabilistic Model Checking Hybrid SAL -- hybrid (discrete + continuous) systems

26

About Pathway Logic

Pathway Logic (PL) uses rewriting logic to model biological processes as executable formal specifications with pathways as computations. Models can be queried

using formal methods tools: execute --- find some pathway search --- find all reachable states satisfying a given property model-check --- find a pathway satisfying a temporal formula

using reflection find all rules that use / produce X (for example, activated Rac) find rules updown stream of a given rule or component find the subnet relevant to a goal (desired state)

27

Essence of Pathway Logic

Pathway Logic (PL) is an approach to modeling biological systems using facts and rules in a logical theory (symbolic systems).

One aim is to directly represent the informal (mental) models that biologists use to understand experimental results.

PL provides a way to organize information in a notation that has a well defined meaning and can be processed by a computer using powerful tools developed by the formal methods community.

System components can be modeled at different levels of detail and at different scales.

28

The Pathway Logic Assistant (PLA)

Provides a means to interact with a PL model

Manages multiple representations

Maude module (logical representation)PetriNet (process representation for efficient query)Graph (for interactive visualization)

Exports Representations to other tools

Lola (and SAL model checkers) Dot -- graph layout JLambda (interactive visualization, Java side) SBML (xml based standard for model exchange)

29

30

About Petri Nets

1

AB

D

2

A

C

B

1

AB

D

2

A

C

B

1

AB

D

2

A

C

B

A Petri net is represented as a graph with two kinds of nodes: * transitions/rules (reactions--squares) * places/occurrences (reactants, products, modifiers--ovals)

A Petri net process has tokens on some of its places. A rule can fire if all of its inputs have tokens. Firing a rule moves tokens from input to output.

An execution is a sequence of rule firings.A pathway is represented as an execution subgraph.

31

Inter-Organ Modeling of Simple Behavior

Proteins unique to different states were identifiedin all three organs. Phosphoprotein data suggestsactivities differ across states in these organs.

32